Computational Collaborative Project in Synergistic PET-MR Reconstruction
Lead Research Organisation:
University College London
Department Name: Medicine
Abstract
Magnetic Resonance (MR) and radionuclide imaging using Positron Emission Tomography (PET) have established roles in medical diagnosis, clinical research and drug development. In recognition of the complementary nature of these two modalities, which have historically been used separately, integrated PET-MR scanners have been designed and marketed by manufacturers. These devices open-up exciting avenues to exploit the synergy between these two modalities in many areas, including dementia, cardiology, and investigation of dynamic processes such as the uptake of contrast agents by tumours.
Both modalities 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. Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data requires considerable expertise and effort in software implementation. We will establish a new Collaborative Computational Project (CCP) to connect researchers working at different sites and on the different modalities of PET and/or MR in the area of image reconstruction, concentrating on the logistical and computational aspects of integrated PET-MR.
The 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 acquired by PET and MR scanners. It will be straightforward to work with data in a standardized format, massively aiding and accelerating innovative developments in image reconstruction and processing for PET-MR, and ultimately enabling the possibility of synergistic image reconstruction.
Both modalities 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. Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data requires considerable expertise and effort in software implementation. We will establish a new Collaborative Computational Project (CCP) to connect researchers working at different sites and on the different modalities of PET and/or MR in the area of image reconstruction, concentrating on the logistical and computational aspects of integrated PET-MR.
The 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 acquired by PET and MR scanners. It will be straightforward to work with data in a standardized format, massively aiding and accelerating innovative developments in image reconstruction and processing for PET-MR, and ultimately enabling the possibility of synergistic image reconstruction.
Planned Impact
Medical imaging has had a phenomenal impact upon healthcare over the last 30 years, with inestimable socioeconomic benefits for the UK. Multi-modality imaging has been of particular benefit, as witnessed by the impact of PET-CT on the treatment of cancer due to its combination of functional and anatomical information. We are now on the threshold of another revolution: PET and MR have recently been combined into a single system, combining the remarkable molecular sensitivity of PET with the enormous flexibility and diverse imaging capabilities of MR. It is expected that the UK government will announce a major investment in PET-MR as part of its strategy to tackle dementia, a disease of which around 800,000 people in the UK suffer. PET-MR is uniquely suitable to study dementia and provide early diagnosis. However, there are significant challenges to be solved, many of them computational, before the full potential of PET-MR will be realised.
This is the crucial clinical context of this proposed CCP, which concerns the way medical imaging data from both the PET and MR imaging modalities is actually used to produce the end point images which are of such utility to clinical researchers, clinicians and ultimately of benefit to patients.
The following are expected beneficiaries beyond the academic community:
1) Patients
The CCP will accelerate research into novel algorithms for improved reconstructed image quality. 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. This will lead to faster translation of successful algorithms into clinical research and practice. This ultimately delivers benefit to patients, such as those suffering from cancer, heart conditions, or brain disorders. In addition, the lower dose associated with PET-MR over PET-CT, especially when combined with synergistic algorithms to optimise image quality, has the benefit of reduced radiation risk to all patients and in particular paediatrics and those undergoing repeat examinations.
2) Pharmaceutical industry and advanced imaging centres
PET and MR are increasingly used in trials for new therapeutic agents. Synergistic reconstruction promises enhanced image quality and more accurate quantification on PET-MR devices. By providing early access to these new methods, such as sophisticated motion correction techniques, the CCP will enable advanced imaging centres to assist pharmaceutical companies in exploratory drug trials by detecting smaller effects. In the long term, the platform will enable pooling of data from multiple centres / scanners, increasing statistical power in later-stage drug trials, and hence reduce the huge costs associated with testing new therapies.
3) Imaging industry
PET-MR has not yet demonstrated a clear diagnostic benefit over conventional PET-CT or MR only, restricting the market size for PET-MR systems. However, the synergistic PET-MR developments of this CCP could well showcase PET-MR capabilities in a diagnostic context, which could be highly profitable to the manufacturers of the new generation of PET-MR scanners. The publically accessible knowledge and Open Source software will benefit SMEs by reducing the cost of creating new products, for instance for pre-clinical imaging or for dedicated reconstruction hardware. Finally, the training provided to young UK researchers will enlarge the skill base for future recruitment into the imaging industry.
4) 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 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.
This is the crucial clinical context of this proposed CCP, which concerns the way medical imaging data from both the PET and MR imaging modalities is actually used to produce the end point images which are of such utility to clinical researchers, clinicians and ultimately of benefit to patients.
The following are expected beneficiaries beyond the academic community:
1) Patients
The CCP will accelerate research into novel algorithms for improved reconstructed image quality. 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. This will lead to faster translation of successful algorithms into clinical research and practice. This ultimately delivers benefit to patients, such as those suffering from cancer, heart conditions, or brain disorders. In addition, the lower dose associated with PET-MR over PET-CT, especially when combined with synergistic algorithms to optimise image quality, has the benefit of reduced radiation risk to all patients and in particular paediatrics and those undergoing repeat examinations.
2) Pharmaceutical industry and advanced imaging centres
PET and MR are increasingly used in trials for new therapeutic agents. Synergistic reconstruction promises enhanced image quality and more accurate quantification on PET-MR devices. By providing early access to these new methods, such as sophisticated motion correction techniques, the CCP will enable advanced imaging centres to assist pharmaceutical companies in exploratory drug trials by detecting smaller effects. In the long term, the platform will enable pooling of data from multiple centres / scanners, increasing statistical power in later-stage drug trials, and hence reduce the huge costs associated with testing new therapies.
3) Imaging industry
PET-MR has not yet demonstrated a clear diagnostic benefit over conventional PET-CT or MR only, restricting the market size for PET-MR systems. However, the synergistic PET-MR developments of this CCP could well showcase PET-MR capabilities in a diagnostic context, which could be highly profitable to the manufacturers of the new generation of PET-MR scanners. The publically accessible knowledge and Open Source software will benefit SMEs by reducing the cost of creating new products, for instance for pre-clinical imaging or for dedicated reconstruction hardware. Finally, the training provided to young UK researchers will enlarge the skill base for future recruitment into the imaging industry.
4) 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 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)
- University of Manchester (Collaboration)
- University Hospital Leipzig (Collaboration)
- Technical University of Munich (Collaboration)
- Leeds Test Objects Ltd (Collaboration)
- Newcastle University (Collaboration)
- National Physical Laboratory (Collaboration)
- Imanova (Collaboration)
- Microsoft Research (Collaboration)
- Mediso Medical Imaging Systems (Collaboration)
- GE Heatlhcare (Collaboration)
- Positrigo (Collaboration)
- Commonwealth Scientific and Industrial Research Organisation (Collaboration)
- UNIVERSITY OF EDINBURGH (Collaboration)
- Maastricht University Medical Center+ (Collaboration)
- GE Healthcare Limited (Collaboration)
- UNIVERSITY OF LEEDS (Collaboration)
- National Institutes of Health (NIH) (Collaboration)
- Physikalisch-Technische Bundesanstalt (Collaboration)
- Invicro (United Kingdom) (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
Atkinson D
(2018)
Geometry in MR Reconstruction
Bertolli O
(2017)
Sign determination methods for the respiratory signal in data-driven PET gating.
in Physics in medicine and biology
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
| Description | This grant provides funding for networking and software development for image reconstruction in PET/MR, an example of dual-modality imaging. An important output of this work is the experience with jointly developing a large open source project with contributions from several universities, relying on other existing open source projects. The associated training of about 20 PhD students, postdocs and staff members in active software development is substantial. In addition, the software itself is open source and therefore accessible to the whole community. Our training events using the software have now reached about 400 people. We have established a UK-wide network with several international partners (currently most active international partners are in Germany, Australia and USA). Cross-fertilisation of ideas and methods is still growing. the open source software is a considerably help and motivator. We have successfully lobbied with 2 major scanner manufacturers to disclose file format and some processing methods, allowing us (and others) to make open or closed source software available. |
| Exploitation Route | The software itself will remain open of course. The capabilities of the software provides a major enhancement in the way researchers can validate their new methods on clinical data, with hopefully major impact on accelerating translation towards clinical practice, and therefore patient benefit. We will continue to present our experience at conferences to help other collaborative software projects. We have secured follow-up funding to expand our network to other medical imaging modalities. |
| Sectors | Digital/Communication/Information Technologies (including Software) Education Healthcare |
| URL | http://www.ccppetmr.ac.uk/ |
| Description | The software is now being used by at least one startup company for development of a PET head scanner. It is expected that the first device will be released with image reconstruction software based on our open source software. This device (and associated software) is currently (2024) close to submission for CE/FDA approval. 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 | Alan Turing Institute Fellowship |
| Amount | £10,000 (GBP) |
| Organisation | Alan Turing Institute |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 09/2018 |
| End | 09/2020 |
| 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 | CCP on Synergistic Reconstruction for Biomedical Imaging |
| Amount | £464,610 (GBP) |
| Funding ID | EP/T026693/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2020 |
| End | 03/2025 |
| Description | Computational Science and Engineering: Software Flagship Project Call |
| Amount | £523,639 (GBP) |
| Funding ID | EP/P022200/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 05/2017 |
| End | 02/2020 |
| 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 | 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 | Industrial PhD funding |
| Amount | £150,000 (GBP) |
| Organisation | Siemens Healthcare |
| Sector | Private |
| Country | Germany |
| Start | 09/2016 |
| End | 09/2020 |
| Description | Open Source Software for quantitative reconstruction of SPECT data |
| Amount | £90,043 (GBP) |
| Organisation | National Physical Laboratory |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 03/2018 |
| End | 03/2019 |
| Description | PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation |
| Amount | £821,421 (GBP) |
| Funding ID | EP/S026045/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2019 |
| End | 08/2023 |
| 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 | NEMA image quality phantom acquisition on the Siemens mMR scanner |
| Description | NEMA image quality (IQ) phantom data acquired on the Siemens Biograph mMR PET/MR scanner. 60 minutes of PET data were acquired. The list mode acquisition and associated files required for reconstruction are provided. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Impact | This dataset allows researchers to test PET image reconstruction software, in particular performance with quantification of PET data. It was acquired on a Siemens mMR PET/MR scanner. In particular, it can be used with our own STIR and SIRF open source reconstruction software. |
| URL | https://zenodo.org/record/1304453 |
| 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 | SIRF: Synergistic Image Reconstruction Framework |
| Description | The combination of positron emission tomography (PET) with magnetic resonance (MR) imaging opens the way to more accurate diagnosis and improved patient management. At present, the data acquired by PET-MR scanners are essentially processed separately, but the opportunity to improve accuracy of the tomographic reconstruction via synergy of the two imaging techniques is an active area of research. In this paper, we present Release 2.1.0 of the CCP-PETMR Synergistic Image Reconstruction Framework (SIRF) software suite, providing an open-source software platform for efficient implementation and validation of novel reconstruction algorithms. SIRF provides user-friendly Python and MATLAB interfaces built on top of C++ libraries. SIRF uses advanced PET and MR reconstruction software packages and tools. Currently, for PET this is Software for Tomographic Image Reconstruction (STIR); for MR, Gadgetron and ISMRMRD; and for image registration tools, NiftyReg. The software aims to be capable of reconstructing images from acquired scanner data, whilst being simple enough to be used for educational purposes. The most recent version of the software can be downloaded from http://www.ccppetmr.ac.uk/downloads and https://github.com/CCPPETMR/. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://data.mendeley.com/datasets/s45f5jh55j |
| Title | SIRF: Synergistic Image Reconstruction Framework |
| Description | The combination of positron emission tomography (PET) with magnetic resonance (MR) imaging opens the way to more accurate diagnosis and improved patient management. At present, the data acquired by PET-MR scanners are essentially processed separately, but the opportunity to improve accuracy of the tomographic reconstruction via synergy of the two imaging techniques is an active area of research. In this paper, we present Release 2.1.0 of the CCP-PETMR Synergistic Image Reconstruction Framework (SIRF) software suite, providing an open-source software platform for efficient implementation and validation of novel reconstruction algorithms. SIRF provides user-friendly Python and MATLAB interfaces built on top of C++ libraries. SIRF uses advanced PET and MR reconstruction software packages and tools. Currently, for PET this is Software for Tomographic Image Reconstruction (STIR); for MR, Gadgetron and ISMRMRD; and for image registration tools, NiftyReg. The software aims to be capable of reconstructing images from acquired scanner data, whilst being simple enough to be used for educational purposes. The most recent version of the software can be downloaded from http://www.ccppetmr.ac.uk/downloads and https://github.com/CCPPETMR/. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://data.mendeley.com/datasets/s45f5jh55j/1 |
| 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 | Data-driven methods for head motion correction in PET/MR |
| Organisation | Commonwealth Scientific and Industrial Research Organisation |
| Country | Australia |
| Sector | Public |
| PI Contribution | Open source software for PET/MR image reconstruction (SIRF and STIR).Training in use and development of the software. Expertise in PET data-driven motion detection techniques. Financial assistance with travel and subsistence of PhD student. |
| Collaborator Contribution | Contributions to the STIR open source software for handling data of the Siemens PET/MR scanner, with specific attention on co-registration between PET reconstructed by STIR and the scanner software. Methods for continuous head position estimation based on the PET data only. |
| Impact | Several pull request to STIR open source software. Oral presentation at IEEE Medical Imaging Conference 2019. |
| Start Year | 2018 |
| 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 | Maastricht University Medical Center Cardiac PET-MR |
| Organisation | Maastricht University Medical Center+ |
| Country | Netherlands |
| Sector | Academic/University |
| PI Contribution | We have developed a framework for efficient free-breathing simultaneous whole-heart coronary magnetic resonance angiography (CMRA) and cardiac positron emission tomography (PET) on a 3T PET-MR system. The acquisition and reconstruction methods are implemented inline in the scanner software. Validation of the proposed framework in patients with cardiovascular disease is being performed in collaboration with Dr. Eline Kooi at the Maastricht University Medical Center . |
| Collaborator Contribution | Dr Eline Kooi and Dr Rik Moonen will be acquiring clinical data to validate the proposed framework. |
| Impact | No outcomes yet. The collaboration involves Physicists experts in Magnetic Resonance Imaging and Nuclear Medicine and clinicians, both cardiologists and radiologists. |
| Start Year | 2017 |
| 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 | Phantoms for validation of synergistic image reconstruction methods |
| Organisation | Leeds Test Objects Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | This initiative is a first step towards validation our open source software STIR and SIRF, and potentially others, for imaging in PET, SPECT, MR and CT. We provide the software, support and advise on acquisition protocols. |
| Collaborator Contribution | Leeds Test Object is a manufacturer of "phantoms" for imaging with different modalities. They have contributed with ideas on scans and phantoms. In the future, they will loan out phantoms to diffferent partners in the SyneRBI network. Univ of Manchester has a lot of expertise on this subject via harmonisation grants for PET/MR. They lead this project. University of Leeds has good relations with LTO and provided initial contact. They will also work on pre-clinical systems as part of this initiative. |
| Impact | This initiative was delayed by COVID19. We have co-organised a discussion session on this topic at the PET, SPECT, MR and TotalBody PET conference 2022. We are currently in planning stage of organising another meeting, followed by scanning etc. |
| Start Year | 2021 |
| Description | Phantoms for validation of synergistic image reconstruction methods |
| Organisation | University of Leeds |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | This initiative is a first step towards validation our open source software STIR and SIRF, and potentially others, for imaging in PET, SPECT, MR and CT. We provide the software, support and advise on acquisition protocols. |
| Collaborator Contribution | Leeds Test Object is a manufacturer of "phantoms" for imaging with different modalities. They have contributed with ideas on scans and phantoms. In the future, they will loan out phantoms to diffferent partners in the SyneRBI network. Univ of Manchester has a lot of expertise on this subject via harmonisation grants for PET/MR. They lead this project. University of Leeds has good relations with LTO and provided initial contact. They will also work on pre-clinical systems as part of this initiative. |
| Impact | This initiative was delayed by COVID19. We have co-organised a discussion session on this topic at the PET, SPECT, MR and TotalBody PET conference 2022. We are currently in planning stage of organising another meeting, followed by scanning etc. |
| Start Year | 2021 |
| Description | Phantoms for validation of synergistic image reconstruction methods |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | This initiative is a first step towards validation our open source software STIR and SIRF, and potentially others, for imaging in PET, SPECT, MR and CT. We provide the software, support and advise on acquisition protocols. |
| Collaborator Contribution | Leeds Test Object is a manufacturer of "phantoms" for imaging with different modalities. They have contributed with ideas on scans and phantoms. In the future, they will loan out phantoms to diffferent partners in the SyneRBI network. Univ of Manchester has a lot of expertise on this subject via harmonisation grants for PET/MR. They lead this project. University of Leeds has good relations with LTO and provided initial contact. They will also work on pre-clinical systems as part of this initiative. |
| Impact | This initiative was delayed by COVID19. We have co-organised a discussion session on this topic at the PET, SPECT, MR and TotalBody PET conference 2022. We are currently in planning stage of organising another meeting, followed by scanning etc. |
| Start Year | 2021 |
| 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 | Reconstructing PET data of the GE Signa PET/MR scanner |
| Organisation | GE Healthcare Limited |
| Department | Molecular Imaging/CT |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Open source software STIR (integrated into SIRF) for PET image reconstruction, test data to validate the software, and expertise on how to handle data from GE scanners. |
| Collaborator Contribution | Leeds has contributed time from a PhD student to extend STIR to data from the GE PET/MR scanner. Imanova (now part of Invicro) and Newcastle University contributed scanning time, data and expertise. GE Healthcare waved confidentiality rights on data formats and some processing methods, and allowed us and Leeds to convert our knowledge into open source software. In addition, they have provided technical assistance and support. Estimating the value of this support is virtually impossible, but it has potentially tremendous impact in opening raw PET data to the research community, especially in Big Data/AI projects. |
| Impact | several pull requests to open source software STIR several contributions in international conferences and a recently accepted paper in Methods. extension of methodology to other GE PET/CT scanners, benefitting other grants. |
| Start Year | 2017 |
| Description | Reconstructing PET data of the GE Signa PET/MR scanner |
| Organisation | Imanova |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Open source software STIR (integrated into SIRF) for PET image reconstruction, test data to validate the software, and expertise on how to handle data from GE scanners. |
| Collaborator Contribution | Leeds has contributed time from a PhD student to extend STIR to data from the GE PET/MR scanner. Imanova (now part of Invicro) and Newcastle University contributed scanning time, data and expertise. GE Healthcare waved confidentiality rights on data formats and some processing methods, and allowed us and Leeds to convert our knowledge into open source software. In addition, they have provided technical assistance and support. Estimating the value of this support is virtually impossible, but it has potentially tremendous impact in opening raw PET data to the research community, especially in Big Data/AI projects. |
| Impact | several pull requests to open source software STIR several contributions in international conferences and a recently accepted paper in Methods. extension of methodology to other GE PET/CT scanners, benefitting other grants. |
| Start Year | 2017 |
| Description | Reconstructing PET data of the GE Signa PET/MR scanner |
| Organisation | Newcastle University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Open source software STIR (integrated into SIRF) for PET image reconstruction, test data to validate the software, and expertise on how to handle data from GE scanners. |
| Collaborator Contribution | Leeds has contributed time from a PhD student to extend STIR to data from the GE PET/MR scanner. Imanova (now part of Invicro) and Newcastle University contributed scanning time, data and expertise. GE Healthcare waved confidentiality rights on data formats and some processing methods, and allowed us and Leeds to convert our knowledge into open source software. In addition, they have provided technical assistance and support. Estimating the value of this support is virtually impossible, but it has potentially tremendous impact in opening raw PET data to the research community, especially in Big Data/AI projects. |
| Impact | several pull requests to open source software STIR several contributions in international conferences and a recently accepted paper in Methods. extension of methodology to other GE PET/CT scanners, benefitting other grants. |
| Start Year | 2017 |
| Description | Reconstructing PET data of the GE Signa PET/MR scanner |
| Organisation | University of Leeds |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Open source software STIR (integrated into SIRF) for PET image reconstruction, test data to validate the software, and expertise on how to handle data from GE scanners. |
| Collaborator Contribution | Leeds has contributed time from a PhD student to extend STIR to data from the GE PET/MR scanner. Imanova (now part of Invicro) and Newcastle University contributed scanning time, data and expertise. GE Healthcare waved confidentiality rights on data formats and some processing methods, and allowed us and Leeds to convert our knowledge into open source software. In addition, they have provided technical assistance and support. Estimating the value of this support is virtually impossible, but it has potentially tremendous impact in opening raw PET data to the research community, especially in Big Data/AI projects. |
| Impact | several pull requests to open source software STIR several contributions in international conferences and a recently accepted paper in Methods. extension of methodology to other GE PET/CT scanners, benefitting other grants. |
| Start Year | 2017 |
| 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 | TUM (Technische Universität München) Cardiac PET-MR |
| Organisation | Technical University of Munich |
| Department | Department of Nuclear Medicine |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | We have developed a framework for efficient free-breathing simultaneous whole-heart coronary magnetic resonance angiography (CMRA) and cardiac positron emission tomography (PET) on a 3T PET-MR system. The acquisition and reconstruction methods are implemented inline in the scanner software. Validation of the proposed framework in patients with cardiovascular disease is being performed in collaboration with Dr. Stephan Nekolla Head of Multimodal Cardiac Imaging at the Department of Nuclear Medicine in Munich. |
| Collaborator Contribution | Karl Kunze, PhD student from TUM Munich, spent two weeks at KCL to learn how to use the framework and acquire the data. Currently he is acquiring data on patients in TUM. |
| Impact | The collaboration involves Physicists experts in Magnetic Resonance Imaging and Nuclear Medicine and clinicians, both cardiologists and radiologists. From this collaboration two abstracts were accepted for the international conference ISMRM in 2017. The journal article "Motion-corrected whole-heart PET-MR for the simultaneous visualisation of coronary artery integrity and myocardial viability: an initial clinical validation" is currently under revision in EJNMMI. |
| Start Year | 2016 |
| Description | University of Edinburgh Cardiac PET-MR |
| Organisation | University of Edinburgh |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We have developed a framework for efficient free-breathing simultaneous whole-heart coronary magnetic resonance angiography (CMRA) and cardiac positron emission tomography (PET) on a 3T PET-MR system. The acquisition and reconstruction methods are implemented inline in the scanner software. Validation of the proposed framework in patients with cardiovascular disease is being performed in collaboration with Dr Mark Dweck and Dr Scott Semple at the University of Edinburgh. |
| Collaborator Contribution | Dr Mark Dweck and Dr Scott Semple will be acquiring clinical data to validate the proposed framework. |
| Impact | No outcomes yet. The collaboration is multidisciplinary as involves Physicists experts in Magnetic Resonance Imaging and Nuclear Medicine and clinicians, both cardiologists and radiologists. |
| Start Year | 2017 |
| Description | quantitative reconstruction of PET data from GE PET/CT scanners |
| Organisation | GE Heatlhcare |
| 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 |
| Description | testing motion correction for neuro PET-MR |
| Organisation | University Hospital Leipzig |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | We provide open source software for image reconstruction and motion estimate, as well as know-how. We also hosted and trained an MSc student. |
| Collaborator Contribution | The PET centre at Leipzig has developed a head-phantom suitable for PET-MR acquisitions, as well as a robot to move the phantom in reproducible ways. This is an excellent tools for testing robustness of different motion correction methods, including the manufacturer's, but also our own. |
| Impact | Currently one oral presentation at the IEEE Medical Imaging Conference and a paper in preparation. Ultimately, this will give researchers way a comparison between different methods. |
| Start Year | 2020 |
| Title | CCP |
| Description | Open Source software for reconstruction of PET and MR raw data. Developed under an EPSRC Collaborative Computing Project. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | Further funding for a 'flagship' application. |
| URL | http://www.ccppetmr.ac.uk |
| Title | CCP PETMR reconstruction software |
| Description | Prototype release of research software for image reconstruction for Positron Emission Tomography and Magnetic Resonance Imaging (PET-MR). The software is intended to allow synergistic reconstruction, i.e. data from both modalities is used together. |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Open Source License? | Yes |
| Impact | This software has been created based on discussions within the research community, bringing together experts from many universities and institutions. This process has engaged researchers in our network, but also has made many to think for the first time on how to design software that will be used by many and will be co-developed by a team. As this is a prototype, further impact will be for the future. |
| URL | https://github.com/CCPPETMR |
| 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 | 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 | PETPVC: Software for Partial Volume Correction |
| Description | First public release of software for Partial Volume Correction (correcting for quantification problems due to a blurring effect) in Positron Emission Tomography. The software implements a wide variety of image-based PVC methods. Many of these are capable to use an image with anatomical "labels" corresponding to different structures in the body. This is commonly derived from MRI images using "parcellation" software (for which we rely on existing open source projects). PETPVC was mostly developed at University College London, with additional input from the Clinical Imaging Research Centre, A*STAR-NUS. |
| Type Of Technology | Software |
| Year Produced | 2015 |
| Open Source License? | Yes |
| Impact | This software has only just been released and a publication is currently under review. |
| URL | https://github.com/UCL/PETPVC |
| Title | SIRF v2.0 |
| Description | SIRF (Synergistic Image Reconstruction Framework) wraps various open source projects in one consistent C++/Python/MATLAB framework. v2.0 adds image registration and resampling as a basis for motion correction. |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | This software is still in relatively early stage but is already enabling researchers in UK, Germany and Australia to perform research on novel methods for processing of PET/MR data. |
| URL | http://www.ccppetmr.ac.uk/ |
| Title | SIRF v2.1 |
| Description | SIRF (Synergistic Image Reconstruction Framework) wraps various open source projects in one consistent C++/Python/MATLAB framework. v2.1 adds integration with the CCPi Core Imaging Library (CIL), an interface to the Hybrid Kernel Method for PET image reconstruction using anatomical information from MR, and capability of reconstruction of 3D MR sequences. This new version makes the software much more useful for researchers. The integration with CIL opens the window towards application of advanced optimisation algorithms in PET/MR. |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | This version of the software allowed us to train a substantial of ECRs in PET/MR/CT reconstruction with highlight our training school in Chester, November 2019 with about 50 attendees, see https://www.ccppetmr.ac.uk/node/200 |
| URL | http://www.ccppetmr.ac.uk/ |
| 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 | 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 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 | Research Software for image reconstruction for Positron Emission Tomography (PET) and Single Photon Emission Tomography (SPECT). STIR is a long-running project (first release in 2001) with contributions from various universities and companies over the years. A major update has now been made available that uses OpenMP for multi-threading and has an interface to Python and MATLAB. |
| Type Of Technology | Software |
| Year Produced | 2015 |
| Open Source License? | Yes |
| Impact | STIR is widely used as a research tool with a total of more than 260 citations in journals and conference proceedings. It has been used as the initial reconstruction software for the ClearPET preclinical PET scanner, allowing much faster introduction to the market. The scatter correction component of STIR has been used as tools for evaluation of a 3D method and convinced GE Healthcare to modify their own scatter correction software accordingly. The current update considerably enhances usability for research purposes. Previous versions of STIR was command-line based. Cluster-based parallelisation using MPI was already available in STIR. However, many researchers prefer to use interactive languages such that it is easier to try new ideas and get easier feedback via data examination. The multi-threading enhancement speeds-up computation time for interactive use. |
| URL | http://stir.sourceforge.net/ |
| Description | 2nd International Training School on PET/MRI |
| 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 second international training school on PET-MR software engineering for early stage researchers (ESRs) was organised in Leeds from 27th to 30th April. The school was well attended by 35 ESRs from European and Canadian Institutes. The school was financially supported by the EU COST Action TD1007 and the EPSRC CCP-PET-MRI. The school provided 1 week intensive training on most aspects of PET/MR engineering and physics, although hardware was only briefly covered. |
| Year(s) Of Engagement Activity | 2015 |
| URL | http://petmrieu.teiath.gr/PETMR_School_15.html |
| Description | Hackathon 1 - 2018 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | A two-day Hackathon, in which developers met to tackle outstanding features. This allowed developers to exchange ideas and competencies, which benefitted new users in particular. Furthermore, the general public benefitted as this concentrated effort of code improvement increased the available features. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.ccppetmr.ac.uk/hackathon1 |
| Description | Hackathon 2 - 2018 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | A three-day Hackathon, in which developers met to tackle outstanding features. This allowed developers to exchange ideas and competencies, which benefitted new users in particular. Furthermore, the general public benefitted as this concentrated effort of code improvement increased the available features. Particular effort was made in this Hackathon to include researchers that had not previously used the software. These efforts helped increase the user-base of the framework (benefitting the project), and allowed the new users to learn in a hands-on environment. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.ccppetmr.ac.uk/node/162 |
| Description | Hackathon 3 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | A three-day Hackathon, in which developers met to get experience with our software SIRF and various Machine Learning frameworks. This hackathon allowed participants to get hands-on experience with novel methods for using Machine Learning for image reconstruction. In addition, we developed extra functionality within our software SIRF to enable this functionality, including GPU computing capability. |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://www.ccppetmr.ac.uk/node/190 |
| Description | Hackathon 4 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | This three day hackathon was a joint event between our CCP PETMR network and CCPi. Its aim was to prepare for a training school on synergistic image reconstruction. We added functionality to both SIRF and CIL and in particular prepared Jupyter notebooks with demos and exercises, lowering the entry-level for using our software, but also expanding its use for educational purposes. |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://www.ccppetmr.ac.uk/node/194 |
| Description | Hackathon 5 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | This three day hackathon involved researchers from UK and Germany and concentrated on adding motion correction capabilities to our SIRF software. |
| Year(s) Of Engagement Activity | 2020 |
| URL | http://www.ccppetmr.ac.uk/node/233 |
| Description | IEEE MIC 2015 Satellite Workshop on Open Source Software in Medical Imaging, San Diego, USA |
| 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 workshop, we invited the main developers of 6 different open-source projects of image reconstruction in various imaging modalities (PET, SPECT, CT and MR) to present the capabilities and design of their software. Our intention was to disseminate knowledge about these packages, learn from practices by others and to have a platform for discussion on future collaborations. A concrete outcome of this process is the collaboration between our CCP and the Gadgetron project (developing MR reconstruction software). Hopefully others will follow in the future. |
| Year(s) Of Engagement Activity | 2015 |
| URL | http://www.ccppetmr.ac.uk/MIC2015WS_OSSMedImaging |
| 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 | PET-MRI Image reconstruction Workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | A one day workshop educating researchers and industry about PET-MR and image reconstruction techniques, including demonstration of our software. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://www.ccppetmr.ac.uk/node/37 |
| 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 2018 |
| 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 | 48 attendants, ranging from academic researchers, students and industry representatives attended a 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. |
| Year(s) Of Engagement Activity | 2018 |
| URL | http://stir.sourceforge.net/MIC2018UsersMeeting/ |
| Description | STIR User's and Developer's Meeting 2019 |
| 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 55 attendants, ranging from academic researchers, students and industry representatives attended a 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. |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://stir.sourceforge.net/MIC2019UsersMeeting/ |
| 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 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 | STIR Users and Developers Meeting 2017 |
| 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 | About 40 people attended the annual STIR User's Meeting, with presentations from our team but also others using the software. We also had a presentation on our SIRF open source software. The event generated questions on how to use this software in their research and how they could extended. There were also several new registrations for our software. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://stir.sourceforge.net/MIC2017UsersMeeting/ |
| Description | Seminar (Johan Nuyts): Reconstruction with MR-prior for PET brain imaging |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Seminar given by external expert (Johan Nuyts - KUL, Belgium) on the subject of: Reconstruction with MR-prior for PET brain imaging. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Seminar (Simon Stute): CASToR - Customizable and Advanced Software for Tomographic Reconstruction |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Undergraduate students |
| Results and Impact | Semiar given by external expert on alternative PET reconstruction toolkit. Converstation followed seminar on possibility of collaboration/incorporation of CASToR into our framework. |
| Year(s) Of Engagement Activity | 2018 |
| URL | http://www.ccppetmr.ac.uk/node/153 |
| Description | Seminar (Simon Stute): PET reconstruction of the posterior image probability, including multimodal images |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Postgraduate students |
| Results and Impact | Seminar by external expert on the subject: PET reconstruction of the posterior image probability, including multimodal images |
| Year(s) Of Engagement Activity | 2018 |
| URL | http://www.ccppetmr.ac.uk/node/152 |
| 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 from Prof Michel Defrise on Time of Flight imaging with PET |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Prof Defrise is a world-authority on advanced methods for imaging. He gave a seminar on his recent advances in estimating scanner characteristics, enabling better image quality, but also clarifying instabilities in previous methods. Audience reported that the seminar raised many questions, but also answered most of these in a very didactic manner. Future research opportunities were identified. |
| Year(s) Of Engagement Activity | 2020 |
| URL | http://www.ccppetmr.ac.uk/node/254 |
| Description | Stand (touchtable interactions) at Manchester Day Parade (MRI Imaging to the public) |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Manchester Brain Box; data showing on touchcreens presentations - two touchtable systems were on offer with PET/MRI data https://mcrbrainbox.wordpress.com/ 19 June 2016 This showed XCT / MRI and PET images for brains from flies to us; all interactively sliced and segmented. Total through the Town Hall was in excess of 5400: 90 people crawled through the 'pretend' MRI scanner and received a scanned image of 'their' brain. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://mcrbrainbox.wordpress.com/ |
| Description | Symposium on Synergistic Image Reconstruction |
| 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 | Traditionally, image reconstruction has focussed on estimating 2D or 3D images from a single modality data-set and acquisition. In recent years however, there have been significant developments in hardware and systems that allow extracting multi-parametric images from multiple data-sets. Examples include multi-spectral CT, multi-sequence MR, multi-modality such as PET-MR, and acquisitions from multiple-time points. While it is possible with such data to reconstruct several images independently, each corresponding to a different parameter or time-point, it is often advantageous, although challenging, to synergistically reconstruct all images. This event brought a group of researchers together from these different fields and application areas, including medical and industrial imaging, to disseminate ideas to, and learn from, researchers both within and outside their usual research field. This was a joint activity organised together with CCPi. |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://www.ccppetmr.ac.uk/symposium2019 |
| Description | Symposium on current technical challenges in clinical research using PET/MR |
| 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 symposium provided a platform for discussions on how to overcome the remaining barriers in adopting PET/MR in large clinical trials, while also looking at mechanisms for translation of research methods into practice. There was excellent interaction between speakers and audience (many of whom were international), including a discussion panel. Future collaborations were discussed. |
| Year(s) Of Engagement Activity | 2020 |
| URL | http://www.ccppetmr.ac.uk/node/231 |
| 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 | Training School in 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 | In this 2 day School we presented theory and practice of up-to-date synergistic reconstruction algorithms for PET, MR and (multi-spectral) CT. A special feature was the hands-on training using the SIRF and CIL software packages. The half day was dedicated for participants to tackle relevant research questions in groups. The School was organised together with CCPi |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.ccppetmr.ac.uk/node/200 |
| 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 | Training school on PET/MR reconstruction at PSMR 2018 |
| 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 | We organised a one day PET-MRI School for students and early stage researchers at PSMR 2018, the 7th Conference on PET-MRI and SPECT-MRI in May 2018, Isle de Elba, Italy, (24 attendants) with a hands-on PET-MR software training session using SIRF. We funded UK attendants to the school. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.ccppetmr.ac.uk/psmr2018 |
| Description | UCL PET/MRI Methodology 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 | This 3 day workshop focused on development of analysis methods for PET/MRI with the following objectives: • To present relevant current research at UCL and other centres • To provide a forum for networking and future planning • To encourage national collaboration on PET/MRI methods We had several international of high standing. The workshop was very well received. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://www.ucl.ac.uk/nuclear-medicine/upcomingevents/uclpetmrimethodologyworkshop |
| Description | hands-on PET-MR software training session using SIRF |
| 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 | We contributed to the PET-MRI School for students and early stage researchers at PSMR 2017, the 6th Conference on PET-MRI and SPECT-MRI in May 2017, Lisbon, Portugal (25 attendants) with a hands-on PET-MR software training session using SIRF. We funded UK attendants to the school. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://psmr2017.pt/index.php/pet-mri-school |
| Description | participation in the Siemens PET/MR User's Meeting |
| 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 participated in organising a User's Meeting together with Siemens Healthineers. The aim of the meeting was to give a status report of where PET/MR is in clinical practice, but also determine future directions. We also provided some information on our software or image reconstruction SIRF as an open source alternative for the manufacturer's dedicated platform. |
| Year(s) Of Engagement Activity | 2020 |
| Description | seminar Dr Christoph Kolbitsch on Motion correction for cardiac PET-MR |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Dr Kolbitsch and his PhD student Johannes Mayer gave a joint seminar on their recent work on enabling cardiac PET/MR imaging with motion correction for both respiration and cardiac contraction. The talk included information on clinical progress, but also a software framework, based on our SIRF software, for simulation of the methods, allowing validation with ground truth data. |
| Year(s) Of Engagement Activity | 2020 |
| URL | http://www.ccppetmr.ac.uk/node/237 |