CCP in Synergistic Reconstruction for Biomedical Imaging

Lead Research Organisation: University College London
Department Name: Medicine

Abstract

Biomedical imaging has a crucial role in (pre)clinical research, drug development, medical diagnosis and assessment of therapy response. Often, the images are tomographic: from the measured data, (stacks of) slices or volumes representing anatomical and functional properties of the patient can be reconstructed using sophisticated algorithms. Increasingly, images from multiple types of systems such as Magnetic Resonance (MR), radionuclide imaging using Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) and X-ray Computed Tomography (CT) are analysed together.

Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data require considerable expertise and effort in software implementation. In our previous CCP on synergistic reconstruction for PET-MR, we created a network of UK and international researchers working towards integrating image reconstruction of data from integrated, simultaneous, PET-MR scanners. New multi-modality systems are now available or under development, for instance SPECT-MR or even tri-modality PET-SPECT-CT systems. At the same time, top-of-the-range multi-modality systems are expensive and instead combining single-modality scans from different time-points and systems can provide more cost-effective solutions in some cases.

Synergistic image reconstruction aims to exploit the commonalities between the data from the different modalities and time points. However, cross-modality methods are particularly challenging. We will therefore extend the network to exploit synergy in multi-modal, multi-contrast, multi-time point information for biomedical applications, concentrating on the logistical and computational aspects of synergistic image reconstruction.

The Open Source Software platform to be provided by this CCP will be an enabling technology which removes the frequent obstacles encountered when working with the raw medical imaging datasets, accelerating innovative developments in image reconstruction, and ultimately enabling the possibility of synergistic image reconstruction by establishing validated pipelines for processing raw data of multiple data-sets.

Planned Impact

Medical imaging has had significant impact upon healthcare over the last 40 years. Multi-modality imaging has been of particular benefit, for example as witnessed by the impact of PET-CT on the treatment of cancer due to its combination of functional and anatomical information. Now, the forefront of imaging research focuses on the opportunities that combined PET-MR systems can provide. In the UK, eight scanners have been installed with primary focus to study dementias, both as a tool to understand disease processes and to can provide early diagnosis.

Imaging also transformed preclinical research by providing biology and pharmacology researchers the building blocks to fundamental knowledge. Successful imaging techniques in this arena beyond PET-CT, include also SPECT-CT and multi-sequence MRI which allow to study a wide range of diseases at multiple time points during their evolution or therapeutic processes.

Our previous CCP network developed open source software for PET-MR data and in this renewal, we plan to integrate this more closely with the now-installed systems and expand to other joint imaging modalities such as SPECT-CT and multi-sequence MRI. The proposed network will strongly benefit its academic partners and is open to all. The following are expected beneficiaries beyond the academic community:

- Patients: The CCP will accelerate research into novel algorithms for improved reconstructed image quality, faster scanning and dose reduction. Furthermore, researchers will be able to use our software platform to evaluate new algorithms on a much larger number of patient data sets than currently possible. In collaboration with clinical researchers, we will establish proof-of-concept processing pipelines for specific studies. This will enable us to bridge the gap from theory to translation of successful algorithms into clinical research and practice. This delivers benefit to patients, such as those suffering from cancer, heart conditions, or brain disorders.

- Pharmaceutical industry & advanced imaging centres: PET and MR are increasingly used in trials for new therapeutic agents and in understanding of disease. Synergistic reconstruction promises enhanced image quality and more accurate quantification as well as the synthesis of information from multiple imaging time points and modalities. In the long term, these improvements will help increase statistical power at early-stage drug trials, and hence reduce the huge costs associated with testing new therapies.

- Imaging industry: The synergistic developments of this CCP could showcase capabilities of multimodality imaging in a diagnostic context, which could be highly profitable to the manufacturers of a new generation of scanners. The publicly accessible knowledge and Open Source software will reduce the cost of creating new products, for instance for preclinical imaging. Finally, the training provided to young UK researchers will enlarge the skill base for future recruitment into the imaging industry.

- Developing researchers: The training of use in standardised UK-wide software and its exploitation for modelling and algorithms to respond to the needs of clinical researchers (academia and industry) will help the development of interdisciplinary researchers. The seminars and training from leading experts will enhance the research skills of participants in the network, providing ample opportunity to pursue careers in, for example, advanced imaging sciences, including fields beyond the medical arena.

Publications

10 25 50
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Akerele MI (2020) Comparison of Correction Techniques for the Spill in Effect in Emission Tomography. in IEEE transactions on radiation and plasma medical sciences

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Arridge SR (2021) (An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods. in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

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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

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Celledoni E (2021) Equivariant neural networks for inverse problems. in Inverse problems

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CELLEDONI E (2021) Structure-preserving deep learning in European Journal of Applied Mathematics

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Ehrhardt M (2021) Inexact Derivative-Free Optimization for Bilevel Learning in Journal of Mathematical Imaging and Vision

 
Description Attenuation Estimation of MRI hardware in high resolution PET-MRI
Amount £110,000 (GBP)
Funding ID 2532272 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 04/2021 
End 05/2025
 
Description Deep Learning for Joint Reconstruction for PET-MR
Amount £105,000 (GBP)
Funding ID 2407114 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 08/2020 
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 Synergistic Triple Modality PET/SPECT/CT Reconstruction for Early Detection and Treatment of Cancer
Amount £110,000 (GBP)
Funding ID 2580067 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 09/2021 
End 10/2025
 
Description Whole Body dynamic PET
Amount £252,000 (GBP)
Organisation GE Healthcare Limited 
Sector Academic/University
Country United Kingdom
Start 02/2020 
End 02/2022
 
Title 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 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 softwrare, 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 has just started. We are currently in planning stage of organising larger meetings etc, 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 softwrare, 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 has just started. We are currently in planning stage of organising larger meetings etc, 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 softwrare, 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 has just started. We are currently in planning stage of organising larger meetings etc, 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 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 SyneRBI SIRF 
Description Minor patch on 3.1.0 added external project astra-python-wrapper to allow updates of ASTRA without conflicts #605 fix docker/entrypoint for case where a user has a GID that already exists in the Docker image #606 
Type Of Technology Software 
Year Produced 2021 
Open Source License? Yes  
Impact This is an update to our software for synergistic image reconstruction. SIRF 3.1.0 (and this patch release 3.1.1) main addition is the support of Golden-angle radial phase encoding (RPE) trajectories for MR. This opens up possibilities for advanced applications in MR and PET/MR, in particular for motion compensation. This was illustrated for cardiac PET/MR where the motion was obtained from an RPE sequence and used for motion compensation of both MR and PET data. 
URL https://zenodo.org/record/5075290
 
Title 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 GATE Connection 
Description The first release of the STIR-GATE-Connection. 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
Impact This software provides a set of example files and scripts for bridging the gap between the PET Monte Carlo simulator GATE and our open source Software for Tomographic Image Reconstruction STIR. While this was possible before, many researchers struggled to get the two software programs to work together and give good results. This software was presented at the STIR User's Meeting 2020 with high acclaim. Several researchers have provided positive feedback after using it. 
URL https://zenodo.org/record/4271795
 
Title STIR: Software for Tomographic Image Reconstruction 
Description These notes only describe a summary of the changes. Please check documentation/release_4.1.htm New functionality Almost all modifications and additions since version 0.92 in 2001 are now licensed under the Apache 2.0 license as opposed to the (L)GPL. Reading of GE HDF5 data (GE fileformat RDF 9) for the GE Signa PET/MR and recent GE PET/CT scanners (but depending on their software version). We currently read listmode, sinograms and calibration files (except for the WCC (well-counter calibration) files). This code is TOF-ready, but TOF is not enabled pending merge of the TOF Pull Request. Therefore, all reconstructions will be non-TOF. Addition of the Relative Difference Prior [J. Nuyts, et al., 2002]. Interfile and ITK image IO now understand some date/time fields. This required a change to our Interfile version specification to be able to handle time-zones. These can now be added via the same suffix as used by DICOM, e.g. 10:01:01.00+0130. NiftyPET's GPU projectors have been wrapped. stir_math now supports the --output-format option for dynamic and parametric images as well. (An example file using the Multi format is given in the examples/samples folder). Added a utility list_lm_info - A new script create_fdef_from_listmode.sh that can be used to create a single-time frame definition file for a given listmode file. Implementation of ax+by methods xapyb and sapyb where a and b can be scalar or vector types. The LmToProjData hierarchy has a new member function set_up(), and more set_*functions, such that it can be used without parsing. Changed functionality Modification of log-likelihood computation to use more doubles, improving numerical stability, see PR 528. Reconstruction algorithms perform now more checks during set_up() etc. However, this means that you have to call set_up() also for analytic reconstruction algorithms. (This was already documented, but not yet enforced). copy energy information from the template in the forward_project utility. PET scatter improvements: the scatter estimation parsing now prefers specifying normalisation and attenuation via two separate keywords (as opposed to via a chained bin normalisation), (keyword normalisation type) and the attenuation factors. Check the updated sample file. The old parsing keyword Bin normalisation type will still work but is deprecated and will be removed in a future version. scatter estimation uses now more defaults such that you need to have less files. ScatterEstimation has now many more set* functions such that most (all?) parameters can be set from C++/Python (i.e. without parsing). The SinglesRates hierarchy has been revised. The SinglesRates classes assumed that stored singles were rates but in practice were totals-per-time-interval (for both ECAT7 and RDF9). It is convenient to be able to access totals and rates, so SinglesRates has now get_singles (returning the singles in the time interval) and get_singles_rates (returning the rate per second). Due to the previous confusion, "client"-code will need to be adapted to use either get_singles or get_singles_rate, depending on what is needed 
Type Of Technology Software 
Year Produced 2021 
Open Source License? Yes  
Impact This is an update on the Software for Tomographic Image Reconstruction, covering both PET and SPECT reconstruction. The main addition in this release is support for PET data of PET/CT or PET/MR scanners from General Electric, one of the main scanner manufacturers. This is currently restricted to GE fileformat RDF 9. This was the result of a close collaboration with GE, and is a major step forward as this file format was previously proprietary, but made open source. 
URL https://zenodo.org/record/4733458
 
Description 2021 Training School for the Synergistic Image Reconstruction Framework (SIRF) and Core Imaging Library (CIL) 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact This was an online training school held in June-July 2021 spread over 3 weeks with trainers from various institutions from the UK and Germany. This was organised together with the CCPi team as well as many people within the SyneRBI network. This course covered SIRF together with the image reconstruction, optimisation and regularisation software library CIL (Core Imaging Library) of the CCPi team. The course was taken by 50 people, 15 of whom were very active and 20 left positive feedback. We had three live Zoom sessions per week complemented by self-teaching via Jupyter notebooks. Several new developments were put in place including ability to deploy on the STFC Cloud using JupyterHub. This course and its material will provide the foundation for future training courses and also for self-training.
Year(s) Of Engagement Activity 2021
URL https://www.ccpsynerbi.ac.uk/SIRFCIL2021
 
Description Regular meetings for methods and software for synergistic image reconstruction 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact As part of our network, we hold regular meetings covering topical issues for synergistic reconstruction in PET/MR and other modalities. They often have an educational component, some seminars, combined with active discussions. The meetings occurred roughly 6 weekly and have an average attendance of around 20 people, about 30% remotely of which about one third internationally.
Year(s) Of Engagement Activity 2015,2016,2017,2018,2019,2020,2021
URL https://www.ccppetmr.ac.uk/node/228
 
Description STIR User's and Developer's Meeting 2020 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact About 40 attendants, ranging from academic researchers, students and industry representatives attended an online 2 hour workshop on our open source software STIR and SIRF. Our own group and external researchers presented their recent work with the software, with lots of interactions with the audience on capabilties, difficulties encountered, and how to access these contributions in the future. This was the first time the event was online which created some challenges with timezones etc, compared to our usual association to a conference. However, it allowed us to get presenters and attendees from all over the world.
Year(s) Of Engagement Activity 2020
URL http://stir.sourceforge.net/2020UsersMeeting/
 
Description Seminar on SIRF by KT at the ISMRM PET-MR Business Group June 2021 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This was an invited talk to a mostly US but international audience on our Synergistic Image Reconstruction Framework. With this presentation we reached a new audience for our software but also our network. ISMRM (International Society of Magnetic Resonance in Medicine) is the major society on MR imaging with an increasing interest in PET/MR.
Year(s) Of Engagement Activity 2021
 
Description SyneRBI hackathon 6 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact This hackathon was jointly organised between CCP PET/MR, SyneRBI and CCPi and investigated use of advanced optimization algorithms from the Core Imaging Library (CCPi) using SIRF acquisition data and models. This expanded our capabilities considerably, with examples for both PET and MR. During the hackathon we resolved software issues that enabled this,and planned for some remaining issues for the future.
Year(s) Of Engagement Activity 2020
 
Description SyneRBI hackathon 7 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact In our 7th Hackathon we worked with various students and postdocs from the UK, Germany and Australia to improve support for recent PET scanners. We looked at advanced topics including estimation of normalisation factors and random coincidences. We used both Monte Carlo simulations and measured data. Feedback afterwards include that this was a unique opportunity to learn about advanced PET acquisition modelling. While we made some progress here, more work was still needed on the software afterwards.
Year(s) Of Engagement Activity 2020
 
Description SyneRBI hackathon 8 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact This 8th hackathon was jointly organised between the CCP SyneRBI, CCPi as well as the EPSRC-funded PET++ project. We investigated Stochastic Optimisation algorithms for both PET and CT image reconstruction. This is a new class of algorithms that allows speeding-up image reconstruction based on the use of "subsets" of the data. We started the hackathon with an introduction of of these algorithms with presentations from experts, disseminating their knowledge. We then moved towards design of the software to be as flexible and modular as possible, together with prototyping a few cases. We attracted several new numbers to the network. Feedback afterwards included that the discussion on software design was very useful for many of the attendants.

Future impact will include faster image reconstruction and/or use of more sophisticated regularisation mechanisms.
Year(s) Of Engagement Activity 2021
URL https://petpp.github.io/hackathon#NovemberHackathon