CCP in Synergistic Reconstruction for Biomedical Imaging
Lead Research Organisation:
UNIVERSITY COLLEGE LONDON
Department Name: Medicine
Abstract
Biomedical imaging has a crucial role in (pre)clinical research, drug development, medical diagnosis and assessment of therapy response. Often, the images are tomographic: from the measured data, (stacks of) slices or volumes representing anatomical and functional properties of the patient can be reconstructed using sophisticated algorithms. Increasingly, images from multiple types of systems such as Magnetic Resonance (MR), radionuclide imaging using Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) and X-ray Computed Tomography (CT) are analysed together.
Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data require considerable expertise and effort in software implementation. In our previous CCP on synergistic reconstruction for PET-MR, we created a network of UK and international researchers working towards integrating image reconstruction of data from integrated, simultaneous, PET-MR scanners. New multi-modality systems are now available or under development, for instance SPECT-MR or even tri-modality PET-SPECT-CT systems. At the same time, top-of-the-range multi-modality systems are expensive and instead combining single-modality scans from different time-points and systems can provide more cost-effective solutions in some cases.
Synergistic image reconstruction aims to exploit the commonalities between the data from the different modalities and time points. However, cross-modality methods are particularly challenging. We will therefore extend the network to exploit synergy in multi-modal, multi-contrast, multi-time point information for biomedical applications, concentrating on the logistical and computational aspects of synergistic image reconstruction.
The Open Source Software platform to be provided by this CCP will be an enabling technology which removes the frequent obstacles encountered when working with the raw medical imaging datasets, accelerating innovative developments in image reconstruction, and ultimately enabling the possibility of synergistic image reconstruction by establishing validated pipelines for processing raw data of multiple data-sets.
Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data require considerable expertise and effort in software implementation. In our previous CCP on synergistic reconstruction for PET-MR, we created a network of UK and international researchers working towards integrating image reconstruction of data from integrated, simultaneous, PET-MR scanners. New multi-modality systems are now available or under development, for instance SPECT-MR or even tri-modality PET-SPECT-CT systems. At the same time, top-of-the-range multi-modality systems are expensive and instead combining single-modality scans from different time-points and systems can provide more cost-effective solutions in some cases.
Synergistic image reconstruction aims to exploit the commonalities between the data from the different modalities and time points. However, cross-modality methods are particularly challenging. We will therefore extend the network to exploit synergy in multi-modal, multi-contrast, multi-time point information for biomedical applications, concentrating on the logistical and computational aspects of synergistic image reconstruction.
The Open Source Software platform to be provided by this CCP will be an enabling technology which removes the frequent obstacles encountered when working with the raw medical imaging datasets, accelerating innovative developments in image reconstruction, and ultimately enabling the possibility of synergistic image reconstruction by establishing validated pipelines for processing raw data of multiple data-sets.
Planned Impact
Medical imaging has had significant impact upon healthcare over the last 40 years. Multi-modality imaging has been of particular benefit, for example as witnessed by the impact of PET-CT on the treatment of cancer due to its combination of functional and anatomical information. Now, the forefront of imaging research focuses on the opportunities that combined PET-MR systems can provide. In the UK, eight scanners have been installed with primary focus to study dementias, both as a tool to understand disease processes and to can provide early diagnosis.
Imaging also transformed preclinical research by providing biology and pharmacology researchers the building blocks to fundamental knowledge. Successful imaging techniques in this arena beyond PET-CT, include also SPECT-CT and multi-sequence MRI which allow to study a wide range of diseases at multiple time points during their evolution or therapeutic processes.
Our previous CCP network developed open source software for PET-MR data and in this renewal, we plan to integrate this more closely with the now-installed systems and expand to other joint imaging modalities such as SPECT-CT and multi-sequence MRI. The proposed network will strongly benefit its academic partners and is open to all. The following are expected beneficiaries beyond the academic community:
- Patients: The CCP will accelerate research into novel algorithms for improved reconstructed image quality, faster scanning and dose reduction. Furthermore, researchers will be able to use our software platform to evaluate new algorithms on a much larger number of patient data sets than currently possible. In collaboration with clinical researchers, we will establish proof-of-concept processing pipelines for specific studies. This will enable us to bridge the gap from theory to translation of successful algorithms into clinical research and practice. This delivers benefit to patients, such as those suffering from cancer, heart conditions, or brain disorders.
- Pharmaceutical industry & advanced imaging centres: PET and MR are increasingly used in trials for new therapeutic agents and in understanding of disease. Synergistic reconstruction promises enhanced image quality and more accurate quantification as well as the synthesis of information from multiple imaging time points and modalities. In the long term, these improvements will help increase statistical power at early-stage drug trials, and hence reduce the huge costs associated with testing new therapies.
- Imaging industry: The synergistic developments of this CCP could showcase capabilities of multimodality imaging in a diagnostic context, which could be highly profitable to the manufacturers of a new generation of scanners. The publicly accessible knowledge and Open Source software will reduce the cost of creating new products, for instance for preclinical imaging. Finally, the training provided to young UK researchers will enlarge the skill base for future recruitment into the imaging industry.
- Developing researchers: The training of use in standardised UK-wide software and its exploitation for modelling and algorithms to respond to the needs of clinical researchers (academia and industry) will help the development of interdisciplinary researchers. The seminars and training from leading experts will enhance the research skills of participants in the network, providing ample opportunity to pursue careers in, for example, advanced imaging sciences, including fields beyond the medical arena.
Imaging also transformed preclinical research by providing biology and pharmacology researchers the building blocks to fundamental knowledge. Successful imaging techniques in this arena beyond PET-CT, include also SPECT-CT and multi-sequence MRI which allow to study a wide range of diseases at multiple time points during their evolution or therapeutic processes.
Our previous CCP network developed open source software for PET-MR data and in this renewal, we plan to integrate this more closely with the now-installed systems and expand to other joint imaging modalities such as SPECT-CT and multi-sequence MRI. The proposed network will strongly benefit its academic partners and is open to all. The following are expected beneficiaries beyond the academic community:
- Patients: The CCP will accelerate research into novel algorithms for improved reconstructed image quality, faster scanning and dose reduction. Furthermore, researchers will be able to use our software platform to evaluate new algorithms on a much larger number of patient data sets than currently possible. In collaboration with clinical researchers, we will establish proof-of-concept processing pipelines for specific studies. This will enable us to bridge the gap from theory to translation of successful algorithms into clinical research and practice. This delivers benefit to patients, such as those suffering from cancer, heart conditions, or brain disorders.
- Pharmaceutical industry & advanced imaging centres: PET and MR are increasingly used in trials for new therapeutic agents and in understanding of disease. Synergistic reconstruction promises enhanced image quality and more accurate quantification as well as the synthesis of information from multiple imaging time points and modalities. In the long term, these improvements will help increase statistical power at early-stage drug trials, and hence reduce the huge costs associated with testing new therapies.
- Imaging industry: The synergistic developments of this CCP could showcase capabilities of multimodality imaging in a diagnostic context, which could be highly profitable to the manufacturers of a new generation of scanners. The publicly accessible knowledge and Open Source software will reduce the cost of creating new products, for instance for preclinical imaging. Finally, the training provided to young UK researchers will enlarge the skill base for future recruitment into the imaging industry.
- Developing researchers: The training of use in standardised UK-wide software and its exploitation for modelling and algorithms to respond to the needs of clinical researchers (academia and industry) will help the development of interdisciplinary researchers. The seminars and training from leading experts will enhance the research skills of participants in the network, providing ample opportunity to pursue careers in, for example, advanced imaging sciences, including fields beyond the medical arena.
Organisations
- UNIVERSITY COLLEGE LONDON (Lead Research Organisation)
- Dalhousie University (Collaboration)
- Philips Healthcare (Collaboration)
- Johns Hopkins University (Collaboration)
- Microsoft Research (Collaboration)
- Mediso Medical Imaging Systems (Collaboration)
- University of Ottawa (Collaboration)
- Siemens Healthcare (Collaboration)
- Physikalisch-Technische Bundesanstalt (Collaboration)
- Positrigo (Collaboration)
- United Imaging Healthcare (Collaboration)
- Memorial Sloan Kettering Cancer Center (Collaboration)
- Polytechnic University of Milan (Collaboration)
- Cornell University (Collaboration)
- National Institutes of Health (NIH) (Collaboration)
- GE Healthcare (Collaboration)
- National Physical Laboratory (Collaboration)
- National Physical Laboratory (Project Partner)
- GE (General Electric Company) (Project Partner)
- Imanova Limited (Project Partner)
- Leeds Test Objects (Project Partner)
- Bruker BioSpin (Project Partner)
- Siemens plc (UK) (Project Partner)
- Mediso (Project Partner)
Publications
Akerele MI
(2020)
Comparison of Correction Techniques for the Spill in Effect in Emission Tomography.
in IEEE transactions on radiation and plasma medical sciences
Arridge SR
(2021)
(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Barbano R
(2024)
Score-Based Generative Models for PET Image Reconstruction
in Machine Learning for Biomedical Imaging
Biguri A
(2021)
Recent Progress in STIR 5.0
Brown R
(2021)
Motion estimation and correction for simultaneous PET/MR using SIRF and CIL.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Bungert L
(2020)
Robust Image Reconstruction With Misaligned Structural Information
in IEEE Access
Celledoni E
(2020)
Structure preserving deep learning
Celledoni E
(2021)
Equivariant neural networks for inverse problems.
in Inverse problems
| Description | The CCP has created (and is creating) a large set of open-source software for image reconstruction of PET, SPECT and MR data, all crucial imaging modalities affecting patient health. This software and our associated training program allows researchers to improve current processing methods, ultimately resulting in improved image quality and therefore patient diagnosis and treatment. We have organised an open Challenge to allow the community to test new algorithms and to learn from the work of other researchers. A major addition compared to previous years is the capability to process SPECT data fully quantitatively, with new capabilities for advanced collimators. This has importance for the rapidly growing treatment of cancer using Molecular Radiotherapy. Another important outcome of this award will be the establishment of a standard for PET raw data for the very first time. This has the potential to accelerate research by increasing "statistical power" of multi-centre trials as well as the availability of data for training AI methods. We have now achieved a major milestone by having our software integrated into a commercially available low-cost brain PET scanner, designed for dementia diagnosis. This could enable future access for a larger number of patients. |
| Exploitation Route | As the software is open source, this can be used for UK and international researchers as well as companies. To facilitate this, we regularly publish papers to help with dissemination. |
| Sectors | Education Healthcare Pharmaceuticals and Medical Biotechnology |
| URL | https://etsinitiative.org/ |
| Description | The software has been used by at least one startup company for development of a PET head scanner. Its first device has now been released and is being installed in a first sites, enabling cheaper diagnosis for dementia. The image reconstruction software is based on our open source software. The software is also being used by the UK National Physical Laboratory to investigate and characterise uncertainty in images obtained from nuclear medicine scanners (SPECT). The aim is to establish a secondary standard for quantification that can be used by all hospitals for quantitative imaging in many applications. This is a long term project. |
| First Year Of Impact | 2024 |
| Sector | Healthcare |
| Impact Types | Societal Economic |
| Description | Open source platform for image reconstruction |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Impact | This style of training is becoming more popular and arguably initiated in our field by us. We have been invited by several organisations to lead training sessions or contribute to dedicated schools. |
| Description | Attenuation Estimation of MRI hardware in high resolution PET-MRI |
| Amount | £110,000 (GBP) |
| Funding ID | 2532272 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2021 |
| End | 05/2025 |
| Description | Deep Learning for Joint Reconstruction for PET-MR |
| Amount | £105,000 (GBP) |
| Funding ID | 2407114 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2020 |
| End | 09/2024 |
| Description | Evaluation of Machine Learning methods for image denoising for dosimetry in Molecular Radiotherapy |
| Amount | £120,000 (GBP) |
| Funding ID | 2874500 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2023 |
| End | 09/2027 |
| Description | Health And Bioscience IDEAS - Imaging, Data Structures, GEnetics And Analytical Strategies |
| Amount | £799,401 (GBP) |
| Funding ID | MR/V03863X/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2021 |
| End | 04/2023 |
| Description | Improving absorbed dose estimation for treatment planning in Molecular Radiotherapy |
| Amount | £120,000 (GBP) |
| Funding ID | 2734835 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2022 |
| End | 09/2026 |
| Description | Synergistic Reconstruction for Biomedical Imaging (SyneRBI) bridging |
| Amount | £282,370 (GBP) |
| Organisation | Science and Technologies Facilities Council (STFC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 11/2024 |
| End | 10/2026 |
| Description | Synergistic Triple Modality PET/SPECT/CT Reconstruction for Early Detection and Treatment of Cancer |
| Amount | £110,000 (GBP) |
| Funding ID | 2580067 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2021 |
| End | 10/2025 |
| Description | Whole Body dynamic PET |
| Amount | £252,000 (GBP) |
| Organisation | GE Healthcare Limited |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 02/2020 |
| End | 02/2022 |
| Title | 2D Cardiac black-blood TSE MR raw data |
| Description | Raw data in ismrmrd format obtained with a 2D black-blood TSE sequence on a 3T Siemens Verio scanner in three different orientations. This data is used as test data for the comparison of different open-source image reconstruction packages provided here: OpenSourceMrRecon |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Validation of various open source software packages for MR reconstruction |
| URL | https://zenodo.org/record/7468886 |
| Title | 2D Dynamic Golden radial MR raw data |
| Description | Raw MR data set in ISMRMRD format of a 2D Golden radial acquisition of a T1MES phantom. Data acquisition is carried out continuously and multiple inversion pulses are applied at regular intervals. The inversion pulses make the data acquisition sensitive to T1 and allow for T1 mapping. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | This dataset allows testing of software for image reconstruction of MR data acquired with advanced sequences. This provides QA for the software to work with patient data. |
| URL | https://zenodo.org/record/7903232 |
| Title | 3D Golden radial phase encoding MR raw data |
| Description | MR raw dataset in ISMRMRD format acquired with a 3D Golden radial phase encoding trajectory. One data set is of a static phantom, the other data set is of a moving phantom. For details about how to reconstruct this data sets please have a look at: SyneRBI/SIRF-Exercises |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | This dataset allows testing of software for image reconstruction of MR data acquired with advanced sequences. This provides QA for the software to work with patient data. |
| URL | https://zenodo.org/record/7903281 |
| Title | GE Discovery TOF MI PET NEMA IQ projector benchmark listmode data |
| Description | ## LIST0000.BLF listmode file from GE Discvoery MI PET/CT containing all acquired emission events (HDF5) of a single bed position NEMA IQ phantom acq. ## corrections.h5 file containing all quantitative corrections estimate using GE's duetto tool box (HDF5) - correction_lists/sens -> sensivity value for acquired events - correction_lists/atten -> attenuation value for acquired events - correction_lists/contam -> additive contaminations (randoms + scatter) for all acquired events - all_xtals/atten -> attenuation values for all possible crystal combinations - all_xtals/sens -> sensitivity values for all possible crystal combinations - all_xtals/xtal_ids -> all possible crystal combinations |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | This dataset allows testing of software for PET image reconstruction on data from a clinical scanner, therefore ensuring the software will work on patient data as well. |
| URL | https://zenodo.org/record/8404014 |
| Title | GE Discovery TOF MI PET NEMA IQ projector benchmark listmode data |
| Description | ## LIST0000.BLF listmode file from GE Discvoery MI PET/CT containing all acquired emission events (HDF5) of a single bed position NEMA IQ phantom acq. ## corrections.h5 file containing all quantitative corrections estimate using GE's duetto tool box (HDF5) - correction_lists/sens -> sensivity value for acquired events - correction_lists/atten -> attenuation value for acquired events - correction_lists/contam -> additive contaminations (randoms + scatter) for all acquired events - all_xtals/atten -> attenuation values for all possible crystal combinations - all_xtals/sens -> sensitivity values for all possible crystal combinations - all_xtals/xtal_ids -> all possible crystal combinations |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/8404015 |
| Title | NIfTI data files |
| Description | NIfTI files to support the SIRF Exercises regarding Geometry. Data from static phantoms in MRI and a PET/MR phantom - see readme file. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4940071 |
| Title | NIfTI data files |
| Description | NIfTI files to support the SIRF Exercises regarding Geometry. Data from static phantoms in MRI and a PET/MR phantom - see readme file. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4940072 |
| Title | PET phantom data from Siemens Biograph mMR with Carbon-11 scanned over 12 half-lives. |
| Description | Data summary ------------ Title: PET phantom data from Siemens Biograph mMR with Carbon-11 scanned over 12 half-lives. Summary: List mode & accompanying raw data for 12 PET acquistions with DIXON, UTE and CT based u-maps. PET Aquisitions: PET raw data from Siemens Biograph mMR PET-MR scanner with VE11P software. Consists of twelve acquisitions of a 5.5L bottle containing carbon-11, each acquisition approximately one 11C half-life apart (20 min). Bottle was not moved between acquisitions. Bed was not moved between acquisitions. Scans were acquired in a single bed position with upper and lower head-only coils in position. Activity concentration of 11C in the bottle was measured from 10 x 0.2 mL samples taken from the bottle prior to the first scan and measured on a cross-calibrated Perkin-Elmer Wizard 2470 10-detector gamma-counter. Gamma-counter data for this measurement is not included. Concentration at the start of the first PET scan was measured to be 52.804 kBq/mL. Attenuation Maps: MR-attentuation u-maps were acquired with each PET scan using the "HiRes" DIXON sequence recommended by Siemens for brain scans. A single UTE MR-attentuation u-map was acquired with the first PET scan. A compatible CT-attenuation u-map was created from a low dose CT scan from a GE710 Discovery PET-CT scanner which was coregistered with the single UTE. Siemens hardware u-maps for bed and coils are not included. PET Images: The static PET images automatically reconstructed after acquisition on the Biograph mMR are included for basic comparison purposes. An attenuation corrected and non-attenuation corrected PET scan from the first PET acqusition are included. Folders: pet-raw: Contains 12 subfolders, each containing 4 pairs of PET raw data in dcm/BF format. Subfolder names, with PET acqusition start time and duration in seconds is listed in the table below. 30001Head_1_PetAcquisition_Raw_Data 12:26:01 1200 30004Head_2_PetAcquisition_Raw_Data 12:47:11 600 30007Head_3_PetAcquisition_Raw_Data 13:08:10 600 30010Head_4_PetAcquisition_Raw_Data 13:28:48 600 30013Head_5_PetAcquisition_Raw_Data 13:49:13 600 30016Head_6_PetAcquisition_Raw_Data 14:09:32 600 30019Head_7_PetAcquisition_Raw_Data 14:29:48 600 30022Head_8_PetAcquisition_Raw_Data 14:50:03 600 30025Head_9_PetAcquisition_Raw_Data 15:10:19 600 30028Head_10_PetAcquisition_Raw_Data 15:30:33 600 30031Head_11_PetAcquisition_Raw_Data 15:50:46 600 30034Head_12_PetAcquisition_Raw_Data 16:10:59 600 umaps: Contains 3 subfolders with DIXON, UTE and CT u-umaps in DICOM (IMA) format. 1. CTAC: u-map based on CTAC coregistered to UTE. 2. MRAC-HiRes: twelve DIXON "HiRes" u-maps for each PET acquisition. 3. MRAC-UTE: single UTE u-map from first PET acquisition. pet-images-01: Contains 2 subfolders with attenuation corrected (AC) and non-attenuation corrected (NAC) static PET images automatically reconstructed from acqusition of first scan. Images are in DICOM (IMA) format. 1. PETAC 2. PETNAC |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Provides measured data for testing and comparing image reconstruction algorithms. This includes dynamic data, rarely made available, and allows testing of count-rate performance etc. |
| URL | https://zenodo.org/record/4751232 |
| Title | PTB GRPE Interleaved Resolution Phantom Acquisition |
| Description | Financing from the German Research Foundation (DFG) project number GRT 2260, BIOQIC is acknowledged Copyright 2021 Physikalisch-Technische Bundesanstalt (PTB) If used in accordance with the supplied licence please cite the Digital Object Identifier (DOI) provided by Zenodo. The dataset contains 3 files. They contain 3D MR golden-angle radial phase encoding [parallel cartesian readouts with phase encoding points assembled on a non-uniform grid] acquisition data of a standard ACR resolution phantom. The dataset was acquired on a Siemens scanner and converted into ISMRMRD format using a converter ( https://github.com/ismrmrd/siemens_to_ismrmrd ). Dataset name: PTB GRPE Resolution Phantom 3D File format: ISMRMRD (ISMRM Raw Data, http://ismrmrd.github.io/) File extension: .h5 Image/KSpace Data Dimension = 3D Imaging Modality: MRI Institution: Physikalisch-Technische Bundesanstalt Scanner: SIEMENS Verio 3T For details please refer to the file README.txt. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Provides measured data for testing and comparing image reconstruction algorithms. |
| URL | https://zenodo.org/record/4600937 |
| Title | Phantom data from the Siemens mMR scanner |
| Description | Raw data and reconstructed images of the NEMA Image Quality phantom and of a germanium-68 point from the Siemens mMR scanner. A data description document is also included for each dataset. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Provides measured data for testing and comparing image reconstruction algorithms. |
| URL | https://zenodo.org/record/4778982 |
| Title | Volumetric Quality Control Phantom Acquisition on the GE SIGNA PET/MR |
| Description | This is a Volumetric quality control (VQC) phantom dataset acquired on the GE SIGNA PET/MR Scanner at Invicro, London. This dataset comprises of : 1. LST : Uncompressed PET listmode file 2. MR: Reconstructed MRI images of the acquisition over 146 slices 3. MRRAW: Raw MR '.p' files 4. PT: Reconstructed PET images over 89 slices 5. PTRAW: Uncompressed raw emission and normalisation sinograms README.txt contains additional information of the dataset. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | This dataset allows researchers to test PET image reconstruction software, in particular performance with point sources and alignment between PET and MR images. t was acquired on GE Signa PET/MR scanner. In particular, it can be used with our own STIR and SIRF open source reconstruction software. |
| URL | https://zenodo.org/record/3887516 |
| Description | Develpment of a SPECT camera for monitoring Boron Neutron Capture Therapy |
| Organisation | Polytechnic University of Milan |
| Country | Italy |
| Sector | Academic/University |
| PI Contribution | We provided guidance on SPECT camera development, including collimator design, positioning, guidance on how to extend our open source software STIR for the specific collimator being used, as well as reconstruction algorithms. We hosted a PhD student to complete the software development and get proof-of-concept. |
| Collaborator Contribution | Boron Neutron Capture Therapy (BNCT) is a promising new way for cancer treatment, where a pharmaceutical is labelled with boron and binds to preferentially to the tumour. The patient is then irradiated with a neutron beam, which highest dose delivered by the neutrons captured by boron. However, it is necessary to monitor the delivered dose due to possible side effects, for instance due to non-specific binding of the pharmaceutical. There is growing interest in BNCT, including at UCL. The hardware and software for the camera as well as a preliminary design of the collimator were previously developed at Polimi. The software developed by the PhD student will be contributed to our open source software, enabling further applications. |
| Impact | The student gave several presentations on BNCT and his results, including at conferences, increasing our profile in a new community. Software is currently still only available in within the collaboration but will be made open source in the future. |
| Start Year | 2023 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | Cornell University |
| Department | Weill Cornell Medicine |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | GE Healthcare |
| Country | United States |
| Sector | Private |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | Johns Hopkins University |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | Memorial Sloan Kettering Cancer Center |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | Microsoft Research |
| Country | Global |
| Sector | Private |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | Philips Healthcare |
| Country | Netherlands |
| Sector | Private |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | Siemens Healthcare |
| Country | Germany |
| Sector | Private |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | United Imaging Healthcare |
| Country | China |
| Sector | Private |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Establishing a standard for PET Raw Data |
| Organisation | University of Ottawa |
| Department | University of Ottawa Heart Institute |
| Country | Canada |
| Sector | Hospitals |
| PI Contribution | Co-lead of the project overall, and lead of a subgroup on data elements |
| Collaborator Contribution | Partners contribute to discussions and agree on standards. They also co-develop software for enabling the standard as well as use-cases. |
| Impact | This is an international initiative to standardise raw data from Positron Emission Tomography (PET) scanners. Partners are from academia and all vendors of clinical systems. Impact is expected to be large, leading to opening the market, enabling big data applications, and ultimately increase diagnostic accuracy and monitoring of therapy. |
| Start Year | 2022 |
| Description | Interaction on standards for file formats for MR (ISMRMRD) |
| Organisation | Microsoft Research |
| Country | Global |
| Sector | Private |
| PI Contribution | The International Society for Magnetic Resonance in Medicine has developed a standard for Raw Data for MR (ISRMRMRD 1.0). We are now joining discussions on an update of this standard MRD 2.0. This includes investigating overlap with our own efforts on PET Raw Data standardisation. We regularly contribute minor fixes to the software used to read/write data in this standard. This software is used by our own reconstruction software (STIR) |
| Collaborator Contribution | David Atkinson (UCL) has contributed to version 1.0 of this standard. The standard is developed by many institutions. |
| Impact | 10.1002/mrm.26089 |
| Start Year | 2016 |
| Description | Interaction on standards for file formats for MR (ISMRMRD) |
| Organisation | National Institutes of Health (NIH) |
| Department | National Heart, Lung, and Blood Institute (NHLBI) |
| Country | United States |
| Sector | Public |
| PI Contribution | The International Society for Magnetic Resonance in Medicine has developed a standard for Raw Data for MR (ISRMRMRD 1.0). We are now joining discussions on an update of this standard MRD 2.0. This includes investigating overlap with our own efforts on PET Raw Data standardisation. We regularly contribute minor fixes to the software used to read/write data in this standard. This software is used by our own reconstruction software (STIR) |
| Collaborator Contribution | David Atkinson (UCL) has contributed to version 1.0 of this standard. The standard is developed by many institutions. |
| Impact | 10.1002/mrm.26089 |
| Start Year | 2016 |
| Description | Motion correction for cardiac PET/MR |
| Organisation | Physikalisch-Technische Bundesanstalt |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Our main contribution is via the open source software SIRF and associated software for PET reconstruction STIR, enabling reconstruction of data from the Siemens mMR PET/MR scanner in an independent and open framework, allowing offline processing, non-standard gating etc. We have provided financial support for visits by a PhD student and training in the software and development tools. In addition, we bring expertise on respiratory motion correction in PET/MR. |
| Collaborator Contribution | MR expertise and test-data. In addition, Dr Kolbitsch and his PhD student have made substantial contributions to the training material for the software, as well as actively participating in training events. They are in the process of contributing an addition to our SIRF software allowing simulation of dynamic PET/MR with motion, which will be a major step towards giving researchers easy-to-use tools for developing and validating advanced motion correction strategies. Recent contributions include software for non-Cartesian MR sequences what are crucial for good cardiac imaging. |
| Impact | jupyter notebooks for MR reconstruction via SIRF, see https://github.com/CCPPETMR/SIRF-Exercises. Paper published in Phys Med Biol on the simulation framework. Extra capabilities for SIRF are in development and will be contributed in the near future. |
| Start Year | 2018 |
| Description | Open source software for pinhole SPECT with dual-isotopes |
| Organisation | Dalhousie University |
| Country | Canada |
| Sector | Academic/University |
| PI Contribution | We provided the main modelling software for pinhole SPECT as well as skeleton implementations for dual-isotope image reconstruction. We have provided financial support for conference attendance and visit by a PhD student and training in the software and development tools. |
| Collaborator Contribution | The partner supplied test data (Monte Carlo simulations and measured data), tested and finalised the open source software and contributed it to STIR and SIRF. Further testing and publications are currently in progress. |
| Impact | A 3D Single Photon Emission Computed Tomography (SPECT) modelling library specific for pinhole collimators was add to our open source software STIR and SIRF to enable corrections for the spatially variant collimator-detector response and attenuation by incorporating their effects into the system matrix. The inclusion of the pinhole-SPECT library in SIRF could greatly benefit the research community given the recent advancements in imaging technology, namely in the preclinical setting. A growing topic of interest is the use of multi-radionuclides in SPECT, allowing characterisation of physiological properties in a single scan. However, data reconstructed with conventional energy windows can contain cross-talk which impedes quantification accuracy. We are therefore developing and testing a multi-radionuclide reconstruction method and will test it with test objects as well as preclinical data. This work has also led to a collaboration with Polimi, Milano, Italy on developing a SPECT camera for monitoring Boron Neutron Capture Therapy, enabled by the pinhole collimator code contributed to STIR. This project is multi-disciplinary, involving physicists, engineers, radiochemists and biologists. cross-talk correction without compromising count statistics. |
| Start Year | 2022 |
| Description | Quantitative synergistic image reconstruction to enhance positron emission tomography for imaging patients with Alzheimer's disease |
| Organisation | Positrigo |
| Country | Switzerland |
| Sector | Private |
| PI Contribution | Software, know how |
| Collaborator Contribution | Hardware, data, and know how |
| Impact | No outputs yet but our software is expected to be used in the commercial clinical product |
| Start Year | 2020 |
| Description | Secondary Standard for Quantitative Imaging in Nuclear Medicine |
| Organisation | Mediso Medical Imaging Systems |
| Country | Hungary |
| Sector | Private |
| PI Contribution | Our open source software STIR forms the basis for this collaboration with the UK National Physical Laboratory to investigate and characterise uncertainty in images obtained from nuclear medicine scanners (SPECT). We provide advise on the software, help with further developments. In addition, we provide scientific and technical advice on SPECT and PET imaging and factors affecting image quality and quantification. |
| Collaborator Contribution | Mediso provides complete information on their trimodality PET/SPECT/CT scanner (the Mediso Anyscan), installed at NPL. This information is under NDA. NPL provides many measurements of phantom data, and has one staff member fully dedicated to this project, who has contributed many components to the open source software, as well as wrote the (closed) software specific for the Mediso AnyScan. In addition, NPL provides statistical analysis of the data, with a publication forthcoming. They also are in contact with other national standardisation institutions about this project. |
| Impact | The aim is to establish a secondary standard for quantification that can be used by all hospitals for quantitative imaging in many applications.This is a long term project, but with potentially large impact on hospital practices. |
| Start Year | 2019 |
| Description | Secondary Standard for Quantitative Imaging in Nuclear Medicine |
| Organisation | National Physical Laboratory |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Our open source software STIR forms the basis for this collaboration with the UK National Physical Laboratory to investigate and characterise uncertainty in images obtained from nuclear medicine scanners (SPECT). We provide advise on the software, help with further developments. In addition, we provide scientific and technical advice on SPECT and PET imaging and factors affecting image quality and quantification. |
| Collaborator Contribution | Mediso provides complete information on their trimodality PET/SPECT/CT scanner (the Mediso Anyscan), installed at NPL. This information is under NDA. NPL provides many measurements of phantom data, and has one staff member fully dedicated to this project, who has contributed many components to the open source software, as well as wrote the (closed) software specific for the Mediso AnyScan. In addition, NPL provides statistical analysis of the data, with a publication forthcoming. They also are in contact with other national standardisation institutions about this project. |
| Impact | The aim is to establish a secondary standard for quantification that can be used by all hospitals for quantitative imaging in many applications.This is a long term project, but with potentially large impact on hospital practices. |
| Start Year | 2019 |
| Description | quantitative reconstruction of PET data from GE PET/CT scanners |
| Organisation | GE Healthcare |
| Country | United States |
| Sector | Private |
| PI Contribution | This is an extension of the work done with GE Healthcare on supporting data from the GE Signa PET/MR scanner. It adds capabilities to our open source software STIR and SIRF for reconstructing data from GE PET/CT scanners. We have now expanded our support for one version of the GE file format from PET/MR to PET/CT. Others need to follow. |
| Collaborator Contribution | GE Healthcare waved confidentiality rights on data formats and some processing methods, and allowed us to convert our knowledge into open source software. In addition, they have provided technical assistance and support. The value of this support is hard to quantify, but provides tremendous opportunities for researchers. |
| Impact | Open source software for reading the RDF9 fileformat for GE PET/CT scanners. This is leading towards new collaborations and allows others to provide proof-of-concept on clinical data for their algorithms. |
| Start Year | 2020 |
| Title | Positrigo NeuroLF Brain PET scanner |
| Description | The NeuroLF is a low-cost Brain PET (Positron Emission Tomography) scanner developed by a Swiss company called Positrigo (spin-off of ETH Zurich). It is intended to make PET much more available, emphasising cost and easy installation and operation. It is a novel approach, targeted towards diagnosis and decisions on treatment pathway for dementia. The entire reconstruction chain is based on our open source software STIR. This is a major change compared to other vendors who have their in-house developments. It is also the first time that a dedicated version of STIR has been approved by FDA and received CE marking. STIR has been under development for 25 years with many sources of funding, including EU, commercial, and most recently UKRI EPSRC via CCP PET/MR EP/M022587/1, its software flagship project EP/P022200/1 and CCP SyneRBI EP/T026693/1. |
| Type | Diagnostic Tool - Imaging |
| Current Stage Of Development | Small-scale adoption |
| Year Development Stage Completed | 2024 |
| Development Status | Under active development/distribution |
| Clinical Trial? | Yes |
| Impact | The requirements for integrating open source software into a commercial product meant more rigirous testing and validation. This has included automatic testing, improved interface via Python for early development, developments for systems with non-standard geometry. These efforts were largely contributed by the company (or with funding to NPL, one of our partners). The company has licensed all their contributions under the same open source license (Apache 2.0), meaning that this benefits the whole research community, but also other start-ups. At least 2 other start-ups have now started to make steps towards using STIR for their products. The number of units sold is currently still very small, so impact on patients remain for the future. |
| URL | https://www.positrigo.com/product/ |
| Title | CCP SyneRBI SIRF |
| Description | Minor patch on 3.1.0 added external project astra-python-wrapper to allow updates of ASTRA without conflicts #605 fix docker/entrypoint for case where a user has a GID that already exists in the Docker image #606 |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | This is an update to our software for synergistic image reconstruction. SIRF 3.1.0 (and this patch release 3.1.1) main addition is the support of Golden-angle radial phase encoding (RPE) trajectories for MR. This opens up possibilities for advanced applications in MR and PET/MR, in particular for motion compensation. This was illustrated for cardiac PET/MR where the motion was obtained from an RPE sequence and used for motion compensation of both MR and PET data. |
| URL | https://zenodo.org/record/5075290 |
| Title | CCP SyneRBI SIRF |
| Description | New features PET Addition of
sirf.STIR.ScatterEstimation and
ScatterSimulation to allow (non-TOF) scatter estimation in PET GE Signa PET/MR reading of listmode data, sinograms, normalisation and randoms support added. If STIR is at least version 5 or built from the master branch, Georg Schramm's parallel (computing) projector is now made available from SIRF (use
AcquisitionModelUsingParallelproj). This uses Joseph interpolation, but importantly can use your GPU (if CUDA was found during building). Implemented extraction of the operator representing the linear part of PET acquisition model and computation of its norm. When adding a shape to a
sirf.STIR.ImageData, optionally give the number of times to sample a voxel. This is useful when the shape partially - but not completely - fills a voxel. If
storage_scheme is set to
memory,
PETAcquisitionData allows direct modification, whereas before a copy would need to be created first. (Internally, it uses STIR
ProjDataInMemory, instead of
ProjDataFromStream). Registration Registration of 2d images is now supported with aladin and f3d. examples data: Installs
examples,
data and
doc to the install directory, i.e.
${CMAKE_INSTALL_PREFIX}/share/SIRF-. directory. If the
SIRF_DATA_PATH environment variable is set,
examples_data_path will search for the examples data there, or in
SIRF_INSTALL_PATH/share/SIRF-./data directory. In MATLAB, the
example_data_path function has the version set by CMake at install time. Other Python features: Define
__version__ in
sirf python package. Added implementation of division and multiplication for
NiftiImageData. Data validity checks return
NotImplemented instead of throwing error, opening the door for future implementations of operations on data. Backwards incompatible changes STIR version 4.1.0 is now required. Python 2 is no longer supported. Most code might still work, but we do not check. A warning is written when the Python version found is 2. This will be changed to
FATAL_ERROR at a later stage. Handling of coil images and sensitivities in C++ code simplified by inheriting CoilImagesVector from GadgetronImagesVector and replacing CoilSensitivitiesAsImages with CoilSensitivitiesVector, also inheriting from GadgetronImagesVector. All methods of CoilImagesVector and CoilSensitivitiesVector other than those inherited from GadgetronImagesVector are no longer supported except methods named compute(), which are renamed to calculate(). Deprecations (will be errors in SIRF 4.0)
Registration: renamed
Resample to
Resampler and
NiftyResample to
NiftyResampler. Old names are now deprecated but should still work. STIR
AcquisitionModel
forward,
direct,
backward and
adjoint signatures have changed in Python. Subset information should now be set via
num_subsets and
subset_num members.
Theforward
andbackward
members can still be called with the previous syntax but this will be removed in a later version. Note that default values ofnum_subsets
andsubset_num` are 0 and 1 respectively, such that default behaviour is default behaviour (i.e. process all data) is unchanged. MR acquisition data storage scheme restricted to memory only (a message will be printed but no error thrown) Use CMake variable names from
find_package(Python) which are available with CMake 3.12+. SIRF CMake files will accept both
Python_EXECUTABLE or
PYTHON_EXECUTABLE, for the latter it will send a deprecation warning. Other changes When registering, internally the forward displacement is no longer stored, replaced by the forward deformation. The inverse is no longer stored, and is calculated as needed.
PETAcquisitionData.axpby now uses STIR's
axpby and is therefore faster. Speed-up in
stir::AcquisitionDataInMemory of
as_array,
fill,
dot,
norm, etc. (by using STIR iterators). Added common Python
DataContainer algebra unit tests for all
DataContainer inherited classes. Continuous Integration now uses Github Actions. Travis-CI has been dropped. New
CMake option
BUILD_DOCUMENTATION to use doxygen to build C++ documentation. It will be installed in the
share/SIRF-version/doc/doxygen. Bug fixes Python
fill method in MR
DataContainer accepts
numpy array, number or
DataContainer.
get_index_to_physical_point_matrix() returned a wrong matrix in MATLAB and Python. path manipulation of
examples_data_path now should work for any platform, not just linux. |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | This major upgrade to our Synergistic Image Reconstruction Framework (SIRF) added several new features. Most notable were the addition of scatter simulation and estimation, and an interface to an external project providing a GPU projector, both for Positron Emission Tomography (PET). The former enables quantitative reconstruction of PET data, independent of the vendor of the scanners. The latter provides considerable speed-up of the image reconstruction process if GPU hardware is available. |
| URL | https://zenodo.org/record/4776289 |
| Title | CCP SyneRBI SIRF |
| Description | v3.1.0 MR/Gadgetron Golden-angle radial phase encoding (RPE) trajectory is supported if
Gadgetron toolboxes were found during building
WARNING if Gadgetron was compiled with CUDA support, you need to build SIRF with the Gadgetron_USE_CUDA CMake variable set to
ON. Automatic calling of
sort_by_time() in most places. This ensures that only consistent images are reconstructed. Encoding classes perform the Fourier transformations instead of the
MRAcquisitionModel
CoilSensitivitiesVector class now has forward and backward method using the encoding classes getting rid of the duplicate FFT code used to compute
coil sensitivities from
MRAcquisitionData. Added constructor for
GadgetronImagesVector from
MRAcquisitionData. This allows setting up an MR acquisition model without having to perform a reconstruction first. PET/STIR iterative reconstructors
set_current_estimate and
get_current_estimate now create a clone to avoid surprising modifications of arguments. The old behaviour of
set_current_estimate can still be achieved by
set_estimate.
Warning This is backwards incompatible, but arguably a bug fix. SIRF Python interface
range_geometry and
domain_geometry methods of
AcquisitionModel classes, required by CIL algorithms, now obtain data via respective C++
AcquisitionModel classes accessors, in line with our strategy of keeping interface code minimal
sirf.Gadgetron.AcquisitionData.get_info was renamed to
get_ISMRMRD_info to avoid confusion with the other
get_info() methods that return a string. (
get_info still works but issues a deprecation warning). Build system fix bug with older CMake (pre-3.12?) that the Python interface was not built #939. |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | The addition of support for MR Golden-angle radial phase encoding (RPE) trajectories enables a new class of MR sequencies for advanced applications in thoracic, and in particular cardiac, MR, where organ motion prevents good image quality with normal sequences. |
| URL | https://zenodo.org/record/5028210 |
| Title | Code to reproduce results of "Core Imaging Library Part I: a versatile python framework for tomographic imaging" |
| Description | This code reproduces all the results presented in the article Core Imaging Library Part I: a versatile python framework for tomographic imaging by Jakob S. Jørgensen, Evelina Ametova, Genoveva Burca, Gemma Fardell, Evangelos Papoutsellis, Edoardo Pasca, Kris Thielemans, Martin Turner, Ryan Warr, William R. B. Lionheart, and Philip J. Withers which will be available from 5 July 2021 at https://doi.org/10.1098/rsta.2020.0192 A preprint is available from arXiv: https://arxiv.org/abs/2102.04560 Instructions are available in the file README.md as well as at the source GitHub repository https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-I |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Impact | This software increases the dissemination and exposure of our software. Reproducibility is crucial in science and this contributes to open data and software best practices. |
| URL | https://zenodo.org/record/4744394 |
| Title | Code to reproduce results of "Core Imaging Library Part I: a versatile python framework for tomographic imaging" |
| Description | This code reproduces all the results presented in the article Core Imaging Library Part I: a versatile python framework for tomographic imaging by Jakob S. Jørgensen, Evelina Ametova, Genoveva Burca, Gemma Fardell, Evangelos Papoutsellis, Edoardo Pasca, Kris Thielemans, Martin Turner, Ryan Warr, William R. B. Lionheart, and Philip J. Withers which will be available from 5 July 2021 at https://doi.org/10.1098/rsta.2020.0192 A preprint is available from arXiv: https://arxiv.org/abs/2102.04560 Instructions are available in the file README.md as well as at the source GitHub repository https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-I |
| Type Of Technology | Software |
| Year Produced | 2021 |
| URL | https://zenodo.org/record/4744395 |
| Title | PET Raw data tools v2.0 |
| Description | pet-rd-tools provides a set of tools for handling raw data from PET scanners. It enables researchers to use the data from their own scanners, unpack them etc, and then use as input for their own image reconstruction software, including our own STIR and SIRF packages. This second release adds support for data from GE PET/CT scanners. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | This software has enabled various researchers to handle data from their scanner with greater easy, leading to several publications on validation of the software and novel methods. It is now part of the suite of packages provided by our CCP PETMR/SyneRBI. |
| URL | https://github.com/UCL/pet-rd-tools/ |
| Title | SIRF Virtual Machine |
| Description | The Synergistic Image Reconstruction Framework (SIRF) is a software suite aiming at providing the scientific community open source software for reconstruction of data from real PET/MR scanners, whilst being simple enough to be used in a teaching context. SIRF provides user-friendly Python and MATLAB interfaces built on top of C++ libraries. SIRF uses advanced PET and MR reconstruction software packages (currently STIR, Software for Tomographic Image Reconstruction, for PET and Gadgetron for MR) and image registration tools (currently NiftyReg). The current download is a VirtualBox appliance with SIRF and prerequisites pre-installed in a Ubuntu 18.04 machine. |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Impact | This Virtual Machine (corresponding to v3.3.0) was used in training sessions and to give researchers an easy path to entry. |
| URL | https://zenodo.org/record/7050371 |
| Title | SIRF v2.2 |
| Description | This is an update of our open source software Synergistic Image Reconstruction Framework (SIRF). This framework amalgamates several other open source packages for medical imaging into one consistent package, providing C++, MATLAB and Python interfaces. This release provides several improvement including GPU compute capabilities for PET reconstruction, registration of MR images, access to the SPM registration toolkit, and the basic building blocks for motion corrected image reconstruction (MCIR). |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | This release enabled us to investigate novel ways for motion corrected image reconstruction, with a publication currently in revision and more to come. It also increates the capabilities of SIRF, attracting more interest therefore. |
| URL | https://www.ccpsynerbi.ac.uk/ |
| Title | SIRF-SuperBuild |
| Description | VM: "update_VM.sh -s" (i.e. "UPDATE.sh -s") no longer runs configure_gnome.sh. If you have a very old VM, run it manually instead. Updates to run using docker scripts installs custom pip and all python prerequisites with pip Bugfix in finding cython and python in UPDATE.sh general refresh of scripts etc move
zero_fill.sh from
first_run.sh and move it to a new
clean_before_VM_export.sh script, which also removes build files to make the exported VM smaller. docker and VM: install
uuid-dev such that we're prepared for installing ROOT no longer force numpy<=1.20 CMake: FindCython allows hints |
| Type Of Technology | Software |
| Year Produced | 2022 |
| Impact | The SIRF-SuperBuild software allows building and distributing a coherent set of open source packages related to medical data processing and image reconstruction. It forms the basis for docker images, a virtual machine, automated testing and self-build instructions, making sure that researchers have access to a working st of software to provide the tasks that they need for PET, SPECT, MR data. |
| URL | https://zenodo.org/record/7022530 |
| Title | STIR GATE Connection |
| Description | The first release of the STIR-GATE-Connection. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | This software provides a set of example files and scripts for bridging the gap between the PET Monte Carlo simulator GATE and our open source Software for Tomographic Image Reconstruction STIR. While this was possible before, many researchers struggled to get the two software programs to work together and give good results. This software was presented at the STIR User's Meeting 2020 with high acclaim. Several researchers have provided positive feedback after using it. |
| URL | https://zenodo.org/record/4271795 |
| Title | STIR Software for Tomographic Image Reconstruction |
| Description | Summary of changes in STIR release 6.0 This version is 99% backwards compatible with STIR 5.x for the user (see below). Developers might need to make code changes as detailed below. Note though that the locations of installed files have changed. Developers of other software that uses STIR via CMake will therefore need to adapt (see below). Overall summary This release is a major upgrade adding Time of Flight (TOF) capabilities to STIR. This version has a major code-cleanup related to removing old compiler work-arounds, consistent use of override and white-space enforcement. Overall code management and assistance was Kris Thielemans (UCL and ASC). Other main contributors include: Nikos Efthimiou (UCL, University of Hull, UPenn, MGH) for the TOF framework and list-mode reconstruction, Elise Emond (UCL) for adapting TOF framework for projection-data, Palak Wadhwa (University of Leeds) for adaptations and testing on GE Signa PET/MR data, Robert Twyman for extending projector symmetries to TOF and formalising ROOT-based testing, Nicole Jurjew (UCL) for adaptations and testing on Siemens Vision 600 data. Non-TOF contributors include Daniel Deidda (NPL) and Markus Jehl (Positrigo). Patch release info 6.0.0 released 07/02/2024 GitHub Milestone 6.0 Summary for end users (also to be read by developers) Changes breaking backwards compatibility from a user-perspective When parsing Interfile headers for projection data and the originating system is not recognised, the previous version of STIR tried to guess the scanner based on the number of views or rings. This was using very old scanners though, and could lead to confusion. These guesses have now been removed. (deprecated) support for the GE VOLPET format (an old format used by the GE Advance and Discover LS sinograms when using "break-pointing") has been removed. (deprecated) support for the AVW format via the (very old) AnalyzeAVW commercial library has been removed. Most installed files are now in versioned directories. The following shows the new and old locations relative to CMAKE_INSTALL_PREFIX, where V.v indicates the major.minor version number, e.g. 6.0: documentation (including examples as subfolder): share/doc/STIR-V.v (was share/doc/stir-V.v) JSON files with radionuclide database: share/STIR-V.v/config (was share/stir/config) Developers also need to check the new location to use for STIR_DIR documented below. Bug fixes Interfile parsing no longer gets confused by the use of : in a keyword (e.g., used by Siemens for dates). PR #1267 New functionality General Radionuclide database now has a datasource entry with the radionuclide decay table (lnHB ). This makes it traceable to standardised measures of branching ratios, half lives etc. The change is backward compatible and old format is still supported. However we encourage to use the new one, see src/config/radionuclide_info.json. TOF of course! This is mostly transparent, i.e. normally no changes are required to the reconstruction code etc. When using Interfile or ROOT files, certain new keywords are required, see examples/samples/PET_TOF_Interfile_header_Signa_PETMR.hs and examples/samples/root_header.hroot. See also the updated STIR_glossary. Please cite the following papers: Efthimiou, N., Emond, E., Wadhwa, P., Cawthorne, C., Tsoumpas, C., Thielemans, K., 2019. Implementation and validation of time-of-flight PET image reconstruction module for listmode and sinogram projection data in the STIR library. Phys Med Biol 64, 035004. DOI: 10.1088/1361-6560/aaf9b9. Wadhwa, P., Thielemans, K., Efthimiou, N., Wangerin, K., Keat, N., Emond, E., Deller, T., Bertolli, O., Deidda, D., Delso, G., Tohme, M., Jansen, F., Gunn, R.N., Hallett, W., Tsoumpas, C., 2021. PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library. Methods, Methods on simulation in biomedicine 185, 110-119. DOI: 10.1016/j.ymeth.2020.01.005 See also the (enormous) PR #304. Limitations Currently on the matrix based projectors support TOF. Note that the implementation is generic but slow: a non-TOF row is computed and then multiplied with the TOF kernel. This is somewhat alleviated by the use of caching. However, as not all symmetries are supported yet, caching of the projection matrix needs substantially more memory than in the non-TOF situation. We do not have TOF scatter simulation/estimation yet. Radionuclide information is read from Interfile and GE HDF5 headers. If the radionuclide name is recognised to the STIR database, its values for half-life etc are used, as opposed to what was recorded in the file (if anything). list_lm_events now has an additional option --event-bin which lists the bin assigned for the event (according to the "native" projection data, i.e. without any mashing). In addition, the --event-LOR option now also works for SPECT (it was disabled by accident). stir_list_registries is a new utility that list possible values of various registries, which can be useful to know what to use in a .par file. Python (and MATLAB) exposed ProjMatrixByBinPinholeSPECTUB PR #1366 PR #1288 exposed ListRecord etc, such that loops over list-mode data can now be performed in Python (although this will be somewhat slow). See examples/python/listmode_loop_demo.py. added LORAs2Points,LORInCylinderCoordinates, LORInAxialAndSinogramCoordinates and PointOnCylinder. Warning: renamed FloatLOR to LOR, and same for derived classes. add DetectionPositionPair.__repr__ for printing and change order of text in DetectionPosition.__repr__ to fit with constructor to avoid confusion. PR #1316 Changed functionality breaking backwards incompatibility General ProjDataInfo::ask_parameters() and therefore create_projdata_template has changed: If the scanner definition in STIR has TOF capabilities, it will ask for the TOF mashing factor. The default for arc-correction has changed to N, i.e. false. Default value for span is now 11 for Siemens and 2 for GE scanners. The span=0 case (i.e. span-3 for segment 0, span=1 for oblique ones, erroneously by STIR used for the GE Advance) is no deprecated. GE uses span=2. (Reading a "span=0" case is still supported) Projection-data related classes have accessors with an optional make_num_tangential_poss_odd argument (defaulting to false), which made the returned argument a different size. This has been deprecated since version 5.0. Setting this argument to true will now raise an error. Python (and MATLAB) renamed FloatLOR to LOR, and same for derived classes. Changed functionality We now always check (in ProjDataInfo*NoArcCorr) if number of tangential positions in the projection data exceeds the maximum number of non arc-corrected bins set for the scanner. If it is, an error is raised. You might therefore have to adapt your interfile header. Interfile header changes: Write STIR6.0 as Interfile key version to denote TOF changes. This is currently ignored for parsing though. (PET) The effective central bin size (cm) keyword for projection data is now only used for arc-corrected data. It is no longer written to the header for non-arccorrected data. Build system CMake version 3.14 is now required. C++-14 is now required. In fact, it is possible that C++-11 still works. If you really need it, you can try to modify the main CMakeLists.txt accordingly. STIR_CONFIG_DIR is no longer a CMake cached variable, such that it automatically moves along with CMAKE_INSTALL_PREFIX. However, if you are upgrading an existing STIR build, you might have to delete the cached variable, or it will point to the old location. Known problems See our issue tracker. Documentation changes Added (some) documentation on TOF features Added examples/C++/using_installed_STIR to illustrate how to use STIR as a "library". Renamed examples/C++/src to examples/C++/using_STIR_LOCAL. New deprecations for future versions CMake option STIR_USE_BOOST_SHARED_PTR will be removed. It probably no longer works anyway. Therefore stir::shared_ptr will always be std::shared_ptr. Direct X-windows display (corresponding to the CMake option `GRAPHICS=X`) will be removed. It is very outdated and sometimes doesn't work. remaining files for ECAT6 support will be removed. What's new for developers (aside from what should be obvious from the above): White-space and style enforcement We now use clang-format to enforce C++-style, including white-space settings, line-breaks etc. This uses the .clang-format file in the root directory of STIR. Developers should configure their editor encordingly, and ideally use pre-commit. It also has consequences for existing branches as you might experience more conflicts than usual during a merge. More detail is in documentation/devel/README.md. PR #1368. Backward incompatibities ListModeData now has a shared_ptr proj_data_info_sptr protected member, and the scanner_sptr member has been removed. Warning: If your derived class had its own proj_data_info_sptr, it should be removed. virtual ListModeData::get_scanner_ptr() is replaced by ListModeData::get_scanner(). ProjDataInfo*NoArcCorr::get_bin_for_det_pair is now private. Use get_bin_for_det_pos_pair instead. The GeneralisedObjectiveFunction hierarchy now has a already_set_up member variable that needs to be set to false by set_* functions and checked by callers. (deprecated) members/functions have been removed BinNormalisation::undo and apply members that take explicit time arguments extend_sinogram_in_views, extend_segment_in_views and interpolate_axial_position As mentioned above, installation locations are now versioned. New locations that could affect developers that use STIR as an external project: include files: include/STIR-V.v (was include). This should be transparant if you use find_package(STIR). CMake exported STIRConfig.cmake etc: lib/cmake/STIR-V.v (was share/lib). The CMake variable STIR_DIR should now be set to /lib/cmake/STIR-V.v. However, this new location increases chances that find_package finds STIR as it follows conventions better. For instance, STIR can now by found by find_package when setting CMAKE_PREFIX_PATH to what was used for CMAKE_INSTALL_PREFIX when installing STIR (indicated as STIR_CMAKE_INSTALL_PREFIX above). Moreover, if you use the same CMAKE_INSTALL_PREFIX for your project as for STIR, you shouldn't need to set STIR_DIR nor CMAKE_PREFIX_PATH. New functionality TOF related Scanner now allows storing TOF information. This is currently not yet done for all TOF-capable scanners though. Contributions welcome! All projection-data related classes and their members now have a TOF bin index and related information. At present, old-style accessors are in an awkward format such as auto sino = proj_data.get_sinogram(ax_pos_num, segment_num, false, timing_pos_num); These are deprecated since version 5.2 and should be replaced by const SinogramIndices sinogram_idxs{ax_pos_num, segment_num, timing_pos_num}; auto sino = proj_data.get_sinogram(sinogram_idxs); List-mode data for TOF-capable scanners need to pass the relevant information through appropriately of course. Non-TOF related Projectors now have a clone() member, currently returning a bare pointer (like other STIR classes). Bin can now be output to stream as text. Added RunTests::check_if_equal for Bin. KeyParser has a new facility to add an alias to a keyword. This can be used to rename a keyword for instance while remaining backwards compatible. By default, a warning will be written, but this can be disabled. Changed functionality TOF related ProjDataInfoCylindricalNoArcCorr::get_all_det_pos_pairs_for_bin is in most places intended to return the physical locations. However, a `DetectionPositionPair` also contains (unmashed) TOF bin information. This will be further complicated once energy windows are supported. The method therefore has an extra boolean argument ignore_non_spatial_dimensions, which defaults to true. multiply_crystal_factors is essentially a non-TOF calculation. When given TOF projection data, it will "spread" the non-TOF result equally over all TOF bins. This is also appropriate for randoms_from_singles. Code clean-up Clean-up of various work-arounds such as STIR_NO_NAMESPACES, STIR_NO_MUTABLE, BOOST_NO_TEMPLATE_SPECIALIZATION, BOOST_NO_STRINGSTREAM and various items specifically for VC 6.0. Consistently use override in derived classes, via clang-tidy --modernize-use-override. Test changes recon_test_pack changes additional tests for TOF, expansion of some existing tests for TOF updated version number and added some clarification to the README.txt C++ tests additional tests for TOF, expansion of some existing tests for TOF |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| Impact | This version of STIR makes a major contribution to our software to be able to handle Time of Flight, which is now standard on most PET scanners. |
| URL | https://zenodo.org/doi/10.5281/zenodo.10628651 |
| Title | STIR Software for Tomographic Image Reconstruction |
| Description | Summary of changes in STIR release 6.1 This version is 100% backwards compatible with STIR 6.0 for the user, except for the bug-fix in the RDP (see below). Overall summary This version adds capability of using Parallelproj (CPU and GPU versions) for TOF data. In addition, the list-mode objective function has several improvements, including speed-up by using multi-threading if caching was not enabled. Of course, there is also the usual code-cleanup and improvements to the documentation. This release contains mainly code written by Nicole Jurjew (UCL) and Kris Thielemans (UCL). Summary for end users (also to be read by developers) New functionality Add TOF capability of the parallelproj projector (see PR #1356) It is now possible to read TOF bin order from the interfile header (see PR #1389) PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin can now compute the value as well as accumulate_Hessian_times_input. PR #1418 GeneralisedObjectiveFunction has 2 new members to compute the full gradient (compute_gradient and compute_gradient_without_penalty). Previously, only subset gradients were available. PR #1418 Changed functionality PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin now computes the gradient multi-threaded (if OpenMP is enabled), even if caching to file of the list-mode file is not enabled. PR #1418 Accumulation in computation of priors now uses doubles, which could result in slightly better precision. Part of PR #1410. Bug fixes The Relative Difference Prior gave incorrect results, probably since switching to C++-14 in version 6.0, although we are not sure. See PR #1410 and associated issue #1409. Our checks for determining system byte-order were out-of-date and in particular did not work on MacOS 14 on ARM. We now use CMake's CMAKE_CXX_BYTE_ORDER (available since CMake 3.20). This could potentially affect reading of list-mode data (which would otherwise be garbled). Fixed in PR #1412. The listmode objective function did not loop over TOF bins when computing the Hessian. This would give different results in OSSPS for computing the "denominator". Fixed in issue #1427. Known problems See our issue tracker. What's new for developers (aside from what should be obvious from the above): Backward incompatibities Additional checks on GeometryBlocksOnCylindrical scanner configuration, which may lead to an error being raised, while previously the code silently proceeded. Bug fixes PoissonLogLikelihoodWithLinearModelForMeanAndProjData had a (minor?) problem with TOF data that when computing the gradient, the normalisation object was not set-up with the TOF data, but non-TOF instead. This did not happen in our normal reconstruction code, and would have thrown an error if it occured. Fixed in issue #1427. Other code changes Fixes an incompatibility with C++20. Build system Force C++ version according to CERN ROOT versions: ROOT 6.28.10 needs C++17 and 6.30.2 needs C++20. Also some fixes when relying on root-config. Test changes C++ tests Objective functions (both projection-data and list-mode) and priors now have a numerical test for accumulate_Hessian_times_input PR #1418 Full Changelog: https://github.com/UCL/STIR/compare/rel_6.0.0...rel_6.1.0 |
| Type Of Technology | Software |
| Year Produced | 2025 |
| Open Source License? | Yes |
| Impact | This is release 6.1 of our open source reconstruction software STIR. This version added capability of using Parallelproj (CPU and GPU versions) for TOF data. In addition, the list-mode objective function has several improvements, including speed-up by using multi-threading if caching was not enabled. |
| URL | https://zenodo.org/doi/10.5281/zenodo.14998581 |
| Title | STIR Software for Tomographic Image Reconstruction |
| Description | Summary of changes in STIR release 6.2 Overall summary This version is 100% backwards compatible with STIR 6.1, aside from a bug fix for the blocks-on-cylindrical set-up which caused the wrong geometry, and a changed default of the tail-fitting of the scatter estimator (see below). However, C++-17 is now required. Main new features are the capability to mash TOF bins (via SSRB utility/code) and a CUDA version of the Relative Difference Prior. There are also some import bug fixes, as well as some speed-up (hopefully) in the use of arrays. Of course, there is also the usual code-cleanup and improvements to the documentation. This release contains mainly code written by @NicoleJurjew (UCL) (SSRB for TOF), @Imraj-Singh (UCL) (CUDA version of the Relative Difference Prior), @markus-jehl (Positrigo) (fixes for blocks on cylindrical) and @KrisThielemans (UCL). Patch release info 6.2.0 released 23/07/2024 GitHub Milestone 6.2 Summary for end users (also to be read by developers) New functionality ProjData now has most of the methods for numerical operations as Array, i.e. +=,-=,*=,/=,find_max(),find_min(),sum(). ProjDataInMemory adds =,-,*,/ (as well as overloads that are faster than the implementations in ProjData). PR #1439 and PR #1448 New prior CudaRelativeDifferencePrior (use Cuda Relative Difference Prior in .par files), only available if the CUDA nvcc is found during building. Results are identical to RelativeDifferencePrior up to numerical rounding issues. However, the code is currently limited to 3x3x3 weights. Added timings for the RDP (both non-CUDA and CUDA) to the stir_timings utility. PR #1408 Changed functionality The default minimum scale factor for tail-fitting in the scatter estimation is now 0.05 (was 0.4). This (temporarily) resolves a problem that for the Siemens mMR, the default factor was too large (see issue #1280). **WARNING:***This potentially changes your scatter estimates*. (You can check log files of the scatter estimation to see what the scaling factors are.) However, the Siemens mMR example files already lowered the default scale factor to .1, so if you use(d) those, you will get identical results. Array::sum() (and hence images etc) now accumulates in a variable at higher precision to avoid loss of precision. PR #1439 PoissonLogLikelihoodWithLinearModelForMeanAndProjData now attempts to auto-detect if it should compute sensitivity using TOF back-projection or not. It does this by checking if its normalisation object can only handle TOF data or not. This should mean that the use time-of-flight sensitivities keyword should now rarely (if ever) be necessary. PR #1458 SSRB now allows to mash TOF bins. PR #1464 SPECT_dicom_to_interfile improvements: remove requirement for the is_planar parameters. As STIR can only read SPECT sinograms, we now read/set all fields from a planar scan as well. There is therefore no need anymore for the boolean, and it is just ignored. Output of a conversion of planar data is now directly readable into STIR. do checks if sequences are present to avoid seg-faults See PR #1473 Bug fixes There was a bug in the computation of the detector coordinates for BlocksOnCylindrical scanners that caused the buckets to not be symmetric. PR #1462 BlocksOnCylindrical scanners were not axially symmetric due to a bug in how gaps were handled. Also, downsampling of BlocksOnCylindrical scanners in scatter simulation was inaccurate. PR #1466 The "Hessian times input" calculations of the Poisson log-likelihood for projection data were incorrect when the forward projection of the "input" contains negatives. We now detect this and throw an error if it occurs. A proper fix will have to be for later. See Issue #1461 Build system C++-17 is now required. Force C++ version according to CERN ROOT versions: ROOT 6.28.10 needs C++17 and 6.30.2 needs C++20. Also some fixes when relying on root-config. Optionally enable CUDA as a CMake language (for the CUDA RDP). You should use CMake 3.23 or later if you use CUDA. If you have the CUDA Toolkit but an old version of CMake that you cannot update, you will have to set DISABLE_STIR_CUDA to ON. Known problems See our issue tracker. What is new for developers (aside from what should be obvious from the above): Changed functionality Array classes by default use contiguous memory allocation (as opposed to a sequence of 1D vectors). This could speed up memory allocation and destruction of arrays with a high number of underlying 1D vectors. It also allows reading/writing data in one call to the C++ library, as opposed to many small calls. Also added move constructors to the Array, VectorWithOffset classes. PR #1236 and PR #1438. Bug fixes PoissonLogLikelihoodWithLinearModelForMeanAndProjData had a (minor?) problem with TOF data that when computing the gradient, the normalisation object was not set-up with the TOF data, but non-TOF instead. This did not happen in our normal reconstruction code, and would have thrown an error if it occured. Fixed in PR #1427. Other code changes Fixed an incompatibility with C++20. Enabled OpenMP for Array members find_max(), find_min(), sum(), sum_positive(). PR #1449. Changes to allow reading Siemens Biograph Vision data: iSSRB and SSRB are now included in the SWIG interface; minor changes to a shell script altering e7tools headers. Test changes C++ tests Objective functions (both projection-data and list-mode) and priors now have a numerical test for accumulate_Hessian_times_input PR #1418 recon_test_pack The output of simulate_PET_data_for_tests.sh can now be varied by setting environment variables, e.g. max_rd. (Do not forget to unset those variables afterwards!) New test run_test_SSRB.sh New Contributors @Imraj-Singh made their first contribution in https://github.com/UCL/STIR/pull/1408 Full Changelog: https://github.com/UCL/STIR/compare/rel_6.1.0...rel_6.2.0 |
| Type Of Technology | Software |
| Year Produced | 2025 |
| Open Source License? | Yes |
| Impact | This is release 6.2 of our open source reconstruction software STIR. Main new features are the capability to mash TOF bins (via SSRB utility/code) and a CUDA version of the Relative Difference Prior. There are also some import bug fixes, as well as some speed-up (hopefully) in the use of arrays. |
| URL | https://zenodo.org/doi/10.5281/zenodo.15013643 |
| Title | STIR v4.0 |
| Description | This is a major update to our open source Software for Tomographic Image Reconstruction (STIR). STIR provides researchers with the capability to reconstruct PET and SPECT data, completely independently from the manufacturer software (for supported scanners). This release adds several major features, including scatter estimation for PET, the hybrid kernel image reconstruction method, which allows incorporating of anatomical side information, listmode reconstruction, resolution modelling of the PET reconstruction. In addition, STIR can now read data from many Siemens PET scanners, including the mMR PET/MR scanner, as well as output of one of the best known Monte Carlo simulators GATE. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | This release is the first to allow image reconstruction of the Siemens mMR PET/MR scanner. This has enabled several researchers to test new algorithms in real data, leading to Sseveral publications. some of these algorithms were then contributed to the open source project. STIR has attracted around 500 citations since its first release in 2000. |
| URL | http://stir.sourceforge.net/ |
| Title | STIR: Software for Tomographic Image Reconstruction |
| Description | These notes only describe a summary of the changes. Please check documentation/release_4.1.htm New functionality Almost all modifications and additions since version 0.92 in 2001 are now licensed under the Apache 2.0 license as opposed to the (L)GPL. Reading of GE HDF5 data (GE fileformat RDF 9) for the GE Signa PET/MR and recent GE PET/CT scanners (but depending on their software version). We currently read listmode, sinograms and calibration files (except for the WCC (well-counter calibration) files). This code is TOF-ready, but TOF is not enabled pending merge of the TOF Pull Request. Therefore, all reconstructions will be non-TOF. Addition of the Relative Difference Prior [J. Nuyts, et al., 2002]. Interfile and ITK image IO now understand some date/time fields. This required a change to our Interfile version specification to be able to handle time-zones. These can now be added via the same suffix as used by DICOM, e.g. 10:01:01.00+0130. NiftyPET's GPU projectors have been wrapped. stir_math now supports the --output-format option for dynamic and parametric images as well. (An example file using the Multi format is given in the examples/samples folder). Added a utility list_lm_info - A new script create_fdef_from_listmode.sh that can be used to create a single-time frame definition file for a given listmode file. Implementation of ax+by methods xapyb and sapyb where a and b can be scalar or vector types. The LmToProjData hierarchy has a new member function set_up(), and more set_*functions, such that it can be used without parsing. Changed functionality Modification of log-likelihood computation to use more doubles, improving numerical stability, see PR 528. Reconstruction algorithms perform now more checks during set_up() etc. However, this means that you have to call set_up() also for analytic reconstruction algorithms. (This was already documented, but not yet enforced). copy energy information from the template in the forward_project utility. PET scatter improvements: the scatter estimation parsing now prefers specifying normalisation and attenuation via two separate keywords (as opposed to via a chained bin normalisation), (keyword normalisation type) and the attenuation factors. Check the updated sample file. The old parsing keyword Bin normalisation type will still work but is deprecated and will be removed in a future version. scatter estimation uses now more defaults such that you need to have less files. ScatterEstimation has now many more set* functions such that most (all?) parameters can be set from C++/Python (i.e. without parsing). The SinglesRates hierarchy has been revised. The SinglesRates classes assumed that stored singles were rates but in practice were totals-per-time-interval (for both ECAT7 and RDF9). It is convenient to be able to access totals and rates, so SinglesRates has now get_singles (returning the singles in the time interval) and get_singles_rates (returning the rate per second). Due to the previous confusion, "client"-code will need to be adapted to use either get_singles or get_singles_rate, depending on what is needed |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | This is an update on the Software for Tomographic Image Reconstruction, covering both PET and SPECT reconstruction. The main addition in this release is support for PET data of PET/CT or PET/MR scanners from General Electric, one of the main scanner manufacturers. This is currently restricted to GE fileformat RDF 9. This was the result of a close collaboration with GE, and is a major step forward as this file format was previously proprietary, but made open source. |
| URL | https://zenodo.org/record/4733458 |
| Title | STIR: Software for Tomographic Image Reconstruction |
| Description | bug-fix release PR 1019: fixes for SWIG and hence Python interface. PR #1012 and PR #1016: rotational changes to STIR's interface to ROOT files, breaking compatibility with 4.x (and 5.0.0 but this was broken). See below for more information. PR #1026: various fixes to the radionuclide database code. WARNING: This PR changed the file format for radionuclide_info.json file to be more future proof. |
| Type Of Technology | Software |
| Year Produced | 2022 |
| Open Source License? | Yes |
| Impact | This is a bug-fix release of our open source software STIR for image reconstruction of PET and SPECT data. |
| URL | https://zenodo.org/record/6604465 |
| Title | STIR: Software for Tomographic Image Reconstruction |
| Description | bug-fix release: fix in exported STIRConfig.cmake for HDF5 fix Block/Generic
get_phi() which could cause 180 degrees swaps |
| Type Of Technology | Software |
| Year Produced | 2022 |
| Impact | This is a bug-fix release of our open source software STIR for image reconstruction of PET and SPECT data. |
| URL | https://zenodo.org/record/6604468 |
| Title | STIR: Software for Tomographic Image Reconstruction 5.0.2 |
| Description | This is version 5.0.2 of STIR , an Open Source software for use in tomographic imaging. Its aim is to provide a Multi-Platform Object-Oriented framework for all data manipulations in tomographic imaging. Currently, the emphasis is on (iterative) image reconstruction in PET and SPECT, but other application areas and imaging modalities can and might be added. |
| Type Of Technology | Software |
| Year Produced | 2022 |
| Open Source License? | Yes |
| Impact | STIR won the 2017 Physics in Medicine & Biology Rotblat prize. |
| URL | https://github.com/UCL/STIR/releases/tag/rel_5.0.2 |
| Title | STIR: Software for Tomographic Image Reconstruction Release 5.1.0. |
| Description | This is version 5.1.0 of STIR , an Open Source software for use in tomographic imaging. Its aim is to provide a Multi-Platform Object-Oriented framework for all data manipulations in tomographic imaging. Currently, the emphasis is on (iterative) image reconstruction in PET and SPECT, but other application areas and imaging modalities can and might be added. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| Impact | STIR won the 2017 Physics in Medicine & Biology Rotblat prize. |
| URL | https://github.com/UCL/STIR/releases |
| Title | Synergistic Image Reconstruction Framework (SIRF) Release 3.3.0 |
| Description | This is version 3.3.0 of SIRF, an image reconstruction software developed by CCP SyneRBI that is designed to be simple enough in use for educational and research purposes, thus reducing the "barrier for entry" for new contributors to imaging research and development, and at the same time powerful enough to process real scanner data. |
| Type Of Technology | Software |
| Year Produced | 2022 |
| Open Source License? | Yes |
| Impact | CCP SyneRBI Hackathons and training courses have demonstrated that our software package SIRF allows biomedical researchers to implement a new reconstruction algorithm and test it on real scanner data within days. Without SIRF, this would take months even for a person with advanced programming skills, which very few biomedical researchers possess. The acceleration of reconstruction algorithms development will translate into faster progress in accuracy of scanners' images, which enable early diagnostics of cancer and other serious illnesses and lead to better quality and longevity of life for people affected by these illnesses. |
| URL | https://github.com/SyneRBI/SIRF/releases/tag/v3.3.0 |
| Title | Synergistic Image Reconstruction Framework (SIRF) Release 3.4.0 |
| Description | SIRF is an image reconstruction software developed by CCP SyneRBI that is designed to be simple enough in use for educational and research purposes, thus reducing the "barrier for entry" for new contributors to imaging research and development, and at the same time powerful enough to process real scanner data. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| Impact | Hackathons and training courses held by CCP SyneRBI have demonstrated that SIRF allows biomedical researchers to implement a new reconstruction algorithm and test it on real scanner data within days. Without SIRF, this would take months even for a person with advanced programming skills, which very few biomedical researchers possess. The acceleration of reconstruction algorithms development will translate into faster progress in accuracy of scanners' images, which enable early diagnostics of cancer and other serious illnesses and lead to better quality and longevity of life for people affected by these illnesses. |
| URL | https://github.com/SyneRBI/SIRF/releases/tag/v3.4.0 |
| Description | 1st ETSI hackathon |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The Emission Tomography Standardization Initiative (ETSI), established end 2021, is working towards a standard for PET listmode and associated data, called PETSIRD. CCP SyneRBI strongly supports this initiative. This first hackathon, co-organised with CCP SyneRBI, engaged multiple researchers and industrial participants in this project. We developed use-case software and provided proof-of-concept. We also submitted an abstract to a major international conference with the results and experiences from this hackathon. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://etsinitiative.org/2023/06/22/1st-etsi-hackathon/ |
| Description | 2021 Training School for the Synergistic Image Reconstruction Framework (SIRF) and Core Imaging Library (CIL) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | This was an online training school held in June-July 2021 spread over 3 weeks with trainers from various institutions from the UK and Germany. This was organised together with the CCPi team as well as many people within the SyneRBI network. This course covered SIRF together with the image reconstruction, optimisation and regularisation software library CIL (Core Imaging Library) of the CCPi team. The course was taken by 50 people, 15 of whom were very active and 20 left positive feedback. We had three live Zoom sessions per week complemented by self-teaching via Jupyter notebooks. Several new developments were put in place including ability to deploy on the STFC Cloud using JupyterHub. This course and its material will provide the foundation for future training courses and also for self-training. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.ccpsynerbi.ac.uk/SIRFCIL2021 |
| Description | 2nd ETSI hackathon |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This event focussed on ETSI's PETSIRD standardized format for PET raw data. The primary objectives were to demonstrate in-silico end-to-end proof of principle application of the PETSIRD format. Outcomes included feedback on PETSIRD, including identification of some weaknesses that need to be addressed, expanded use-case software, a submission to an international conference, as well as outreach to local undergraduate students. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://etsinitiative.org/2nd-etsi-hackathon-november-3-4th-2024-tampa-fl-usa/ |
| Description | Hackathon in preparation of the SyneRBI Reconstruction Challenge |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | This hackathon was a collaboration between CCP SyneRBI and CCPi. During this event we progressed the development of the stochastic optimisation framework in CIL and SIRF, our open source software packages, providing metrics for the assessment of the algorithm performance with known real data phantoms. The participants worked on several parts including the optimisation software, metrics and data preparation. This prepares us for an open challenge to be held during the summer on fast image reconstruction for PET. |
| Year(s) Of Engagement Activity | 2024 |
| Description | IEEE NPSS School on Advanced Topics in PET/CT and PET/MR |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | This 1 week in-person school was organised by the IEEE NPSS board. About 25 students attended, covering a range of MSc, PhD students and practicing medical physicists. We contributed lectures and interactive training sessions on PET image reconstruction and the use of AI techniques to increase image quality. We used our open source software on a cloud platform for the exercises. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://indico.cern.ch/event/1209524/ |
| Description | Invited seminar by KT on Motion Management in Brain Multimodality Imaging, 29 January 2022, 14th annual scientific meeting of the Thai Medical Physics society, Nan, Thailand |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | In this talk, I gave an overview of existing techniques, both on research and commercially available, for handling movement in brain studies with PET. The target audience was mostly clinical practioners and scientists using PET in Thailand, and served as an educational talk. |
| Year(s) Of Engagement Activity | 2023 |
| Description | PET Rapid Image Reconstruction Challenge |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | We organised an open Challenge (or competition) related to PET image reconstruction. The aim was to investigate and optimise algorithms for fast optimisation. We assembled a data-set of "phantom data" (i.e. test-objects) acquired on several PET scanners, for which the measured data were made openly available. A open source software framework was established for running the challenge, with examples, instructions for participants, evaluation software etc. 6 international teams participated in the Challenge, with 4 teams submitting. The Challenge has led to several insights into relevant parameters for reconstruction algorithms, which will be published in a Special Issue (submissions open in 2025). |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://github.com/SyneRBI/PETRIC/wiki |
| Description | PET Rapid Image Reconstruction Workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | In this hybrid workshop, invited experts presented recent advances in image reconstruction, covering developments in CT, MRI and PET. In addition, the 3 winning teams of our PET Rapid Image Reconstruction Challenge (PETRIC) gave presentation on their work. The workshop was attended by about 120 researchers from mixed background. Recordings of the presentations are available on our website. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.ccpsynerbi.ac.uk/petric-workshop |
| Description | QUIERO Workshop on Cardiac MRF (Magnetic Resonance Fingerprinting) Simulation & Evaluation |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | CCP SyneRBI, together with CCPi, supported the QUIERO Workshop on Cardiac MRF Simulation & Evaluation, held on 6 July 2022, that provided participants with hands-on experience on simulation of MRF data acquisition and evaluation of T1 and T2 maps in clinical practice and using advanced machine learning approaches. More than 20 mainly early career researchers joined this workshop organised by Charité and PTB. As part of this one-day online event the latest features of CCP-SyneRBI's software package SIRF (Synergistic Image Reconstruction Framework) regarding simulation of realistic data for quantitative MRF in the heart were shown. Participants could also simulate their own cardiac MRF scans using interactive jupyter notebooks which will also be part of SIRF in the near future. |
| Year(s) Of Engagement Activity | 2022 |
| Description | Regular meetings for methods and software for synergistic image reconstruction |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | As part of our network, we hold regular meetings covering topical issues for synergistic reconstruction in PET/MR and other modalities. They often have an educational component, some seminars, combined with active discussions. The meetings occurred roughly 6 weekly and have an average attendance of around 20 people, about 30% remotely of which about one third internationally. |
| Year(s) Of Engagement Activity | 2015,2016,2017,2018,2019,2020,2021 |
| URL | https://www.ccppetmr.ac.uk/node/228 |
| Description | STIR User's and Developer's Meeting 2020 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | About 40 attendants, ranging from academic researchers, students and industry representatives attended an online 2 hour workshop on our open source software STIR and SIRF. Our own group and external researchers presented their recent work with the software, with lots of interactions with the audience on capabilties, difficulties encountered, and how to access these contributions in the future. This was the first time the event was online which created some challenges with timezones etc, compared to our usual association to a conference. However, it allowed us to get presenters and attendees from all over the world. |
| Year(s) Of Engagement Activity | 2020 |
| URL | http://stir.sourceforge.net/2020UsersMeeting/ |
| Description | STIR User's and Developer's Meeting 2023 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This annual workshop brings together users and developers of our open source software STIR for PET and SPECT image reconstruction. It led to Q&A on recent features and requests fpr the future, as well as increased interaction within the community. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://stir.sourceforge.net/2023UsersMeeting/ |
| Description | STIR User's and Developer's Meeting 2024 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This yearly workshop brings together reserachers, students and industry representatives who are interested in our open source software for image reconstruction STIR. presentations covered experiences with and additions to STIR. It was attended by about 35 people, several of whom asked about capabilities to see if it would be suitable for their own work. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://stir.sourceforge.net/2024UsersMeeting/ |
| Description | STIR User's and Developer's meeting 2022 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | During this annual meeting users and developers presented their recent work with STIR with the emphasis on software and algorithmic development. This meeting aims to foster participation and sharing. This year, the meeting attracted about 40 researchers from all over the world, and was associated to the IEEE Medical Imaging Conference, held in Milan, Italy. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://stir.sourceforge.net/2022UsersMeeting/ |
| Description | Seminar by Dr Ahmad Rezaei (KUL) on Quantitative reconstructions of activity and attenuation from time-of-flight PET data |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Time-of-flight positron emission tomography (PET) has enabled reconstructions of an activity/attenuation pair from the emission data. This has the potential advantage of removing the need for CT (and therefore radiation dose to the patient) for certain applications. However, the method is more sensitive to scanner calibrations and corrections compared to the gold-standard reconstructions. Dr Rezaei is an internationally acknowledged expert in the field and he gave an overview of the relevant issues. His work feeds into a lot of our research on PET including dose reduction, MR hardware coil location estimation and motion correction. Discussions after this seminar led to ideas for further research. |
| Year(s) Of Engagement Activity | 2024 |
| Description | Seminar by Dr Joaquin Lopez Herraiz: New Methods in PET Image Reconstruction: Going Beyond the State-of-the-Art |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | This was a talk by Joaquin Lopez Herraiz ( https://www.ucm.es/gfn/jlherraiz ) who is an associate professor at Faculty of Physics at Complutense University of Madrid (UCM). He presented some of the work of his group on Positron Emission Tomography (PET), including methods to improve resolution and a method to extract information out of dynamic data. It was a hybrid talk with about 15 in-person attendees and 15 online. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.ccpsynerbi.ac.uk/node/321 |
| Description | Seminar by Prof. Craig S. Levin on Concepts and systems to advance coincidence time resolution for time-of-flight positron emission tomography |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Dr. Craig S. Levin is a Professor of Radiology and, by Courtesy, of Physics, Electrical Engineering, and Bioengineering at Stanford University, U.S.A., and was at the time of this talk Visiting Professor at University of Leeds, UK. In the talk, Prof. Levin present the research directions of his group to advance time-of-flight (TOF) positron emission tomography (PET) towards next generation systems. It was a hybrid talk with about 15 in-person attendees and 20 online. |
| Year(s) Of Engagement Activity | 2022 |
| URL | http://www.ccpsynerbi.ac.uk/node/318 |
| Description | Seminar on SIRF by KT at the ISMRM PET-MR Business Group June 2021 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This was an invited talk to a mostly US but international audience on our Synergistic Image Reconstruction Framework. With this presentation we reached a new audience for our software but also our network. ISMRM (International Society of Magnetic Resonance in Medicine) is the major society on MR imaging with an increasing interest in PET/MR. |
| Year(s) Of Engagement Activity | 2021 |
| Description | SyneRBI hackathon 6 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | This hackathon was jointly organised between CCP PET/MR, SyneRBI and CCPi and investigated use of advanced optimization algorithms from the Core Imaging Library (CCPi) using SIRF acquisition data and models. This expanded our capabilities considerably, with examples for both PET and MR. During the hackathon we resolved software issues that enabled this,and planned for some remaining issues for the future. |
| Year(s) Of Engagement Activity | 2020 |
| Description | SyneRBI hackathon 7 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | In our 7th Hackathon we worked with various students and postdocs from the UK, Germany and Australia to improve support for recent PET scanners. We looked at advanced topics including estimation of normalisation factors and random coincidences. We used both Monte Carlo simulations and measured data. Feedback afterwards include that this was a unique opportunity to learn about advanced PET acquisition modelling. While we made some progress here, more work was still needed on the software afterwards. |
| Year(s) Of Engagement Activity | 2020 |
| Description | SyneRBI hackathon 8 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | This 8th hackathon was jointly organised between the CCP SyneRBI, CCPi as well as the EPSRC-funded PET++ project. We investigated Stochastic Optimisation algorithms for both PET and CT image reconstruction. This is a new class of algorithms that allows speeding-up image reconstruction based on the use of "subsets" of the data. We started the hackathon with an introduction of of these algorithms with presentations from experts, disseminating their knowledge. We then moved towards design of the software to be as flexible and modular as possible, together with prototyping a few cases. We attracted several new numbers to the network. Feedback afterwards included that the discussion on software design was very useful for many of the attendants. Future impact will include faster image reconstruction and/or use of more sophisticated regularisation mechanisms. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://petpp.github.io/hackathon#NovemberHackathon |
| Description | SyneRBI hackathon 9 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | The goal of this hackathon was to establish a suitable reconstruction evaluation strategy, including metrics for image quality and algorithm performance (run-time, memory etc.), taking into account parameter selection for algorithms used. This will lead to an open framework for evaluation of image reconstruction algorithms, as well as at least one journal paper. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://petpp.github.io/hackathon#NovemberHackathon |
| Description | SyneRBI-XNAT hackathon 2023 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Our teams joined the XNAT workshop 2022 https://wiki.xnat.org/workshop-2022/ hackathon with a topic on uploading MR raw data into the XNAT database. Participants benefited from learning about the XNAT software as well as MR raw data formats and requirements. This will lead to increased capabilities of the XNAT database and wider use of MR raw data by researchers, enabling integrated reconstruction pipelines in the future. It is also a first step towards integrating PET raw data into XNAT. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://wiki.xnat.org/workshop-2022/xnat-workshop-2022-hackathon-projects#XNATWorkshop2022HackathonP... |
| Description | Training School on PET/MR reconstruction at PSMRTBP 2022 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | This 1 day training school covered basic principles of the physics behind the acquisition process and image reconstruction methods used for PET and SPECT, with specific information on the challenges for TotalBody PET. We also briefly covered MR aspects. The school included practical sessions with the Open Source software Synergistic Image Reconstruction Framework (SIRF). The school consisted of a half day of lecture-style presentations followed by project-based work using SIRF (in Python). We had about 25 participants ranging from Msc & PhD students to academics. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.ccpsynerbi.ac.uk/node/316 |
| Description | Workshop on modern image reconstruction algorithms and practices for medical imaging |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This workshop covered recent image reconstruction algorithms and practices for medical imaging, concentrating on PET, MRI, and CT. The workshop was organised on the occasion of the visit of Prof Jeffrey Fessler from the University of Michigan, to UCL and was intended to bring together researchers, present exciting new research directions, and foster future collaborative projects. |
| Year(s) Of Engagement Activity | 2022 |
| URL | http://www.ccpsynerbi.ac.uk/node/322 |
| Description | seminar by Dr Simon Rit on Pairwise data consistency conditions in x-ray cone-beam CT and SPECT |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Data consistency conditions (DCCs) are mathematical equations characterizing the redundancy in projections acquired by computed tomography (CT) scanners. DCCs can be used to detect and correct physical effects, which makes the projections inconsistent, e.g. motion, scatter, or beam hardening. This talk summarised recent developments by the group of Dr Rit with DCCs of projections acquired by a cone-beam CT scanner and a parallel single photon emission CT (SPECT) with application to spectral CT calibration or motion detection during the acquisitions. The intention was to create awareness of this method and explore opportunities for later collaborations. |
| Year(s) Of Engagement Activity | 2024 |
| Description | seminars by Tommaso Ferri on Building a SPECT System For Real Time Dose Monitoring During Boron Neutron Capture Therapy Treatment |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | About 40 people, mixed between academics and PhD students attended a hybrid seminar in October on BNCT, a growing alternative therapy for cancer. The seminar led to requests for collaboration. A follow-up seminar was held in March 2024, including recent results on the design of a SPECT imaging system. |
| Year(s) Of Engagement Activity | 2023,2024 |