A framework for efficient synergistic spatiotemporal reconstruction of PET-MR dynamic data
Lead Research Organisation:
University College London
Department Name: Medicine
Abstract
Magnetic resonance (MR) and radionuclide imaging using positron emission tomography (PET) are now essential in many areas of medical diagnosis, clinical research and drug development. The complementary information provided by PET and MR has recently lead to a new generation of dual-modality systems. Clinical applications of these devices are emerging in many priority areas for human health, including dementia, cardiology, and oncology.
The previously established Collaborative Computational Project (CCP) for synergistic PET-MR reconstruction connects researchers working on sophisticated algorithms to estimate stacks of slices through the patient from the measured data. In this flagship project, we extend this work towards imaging of dynamic processes. Measuring the redistribution over time of contrasts agents or labelled molecules while taking organ movement into account provides considerable information on the biology, allowing extraction of parameters such as blood flow, receptor density and tissue elasticity.
By exploiting correlations between these parameters in both space and time, we can probe fast dynamic processes at higher accuracy than achievable in single modality imaging. This flagship will provide freely available software and methods to the community to accelerate research and development in this area.
The previously established Collaborative Computational Project (CCP) for synergistic PET-MR reconstruction connects researchers working on sophisticated algorithms to estimate stacks of slices through the patient from the measured data. In this flagship project, we extend this work towards imaging of dynamic processes. Measuring the redistribution over time of contrasts agents or labelled molecules while taking organ movement into account provides considerable information on the biology, allowing extraction of parameters such as blood flow, receptor density and tissue elasticity.
By exploiting correlations between these parameters in both space and time, we can probe fast dynamic processes at higher accuracy than achievable in single modality imaging. This flagship will provide freely available software and methods to the community to accelerate research and development in this area.
Planned Impact
Biomedical imaging plays a key role in patient management, the assessment of new therapies and improved understanding of disease. PET-MR imaging is a newly developed technology that may provide new information that increases the effectiveness of medical imaging.
Patients
In the long-term, new imaging can benefit patients through more accurate and earlier diagnosis, and assessment of response to therapy. An early assessment of therapy response allows for a change in dose or management to match the response of the patient. This could lead to effective treatment occurring faster, or prevent prolonged unnecessary treatments with negative side-effects.
Health Service
Improved diagnosis and monitoring of disease and therapy response allows a more rational use of healthcare resources. Although imaging costs for PET-MR are of the order £1K/scan, the potential saving in costs of drugs or other interventions through better and earlier decisions is likely to far exceed this cost. In the UK, the NHS spends only about 7% of its budget on imaging.
Pharmaceutical Industry & Advanced imaging centres
The combined use of PET and MR offers the possibility of making measurements from PET and MR images more reliable and reproducible between different patients and imaging centres. Changes within a patient's images can be used as biomarkers for disease, potentially showing disease changes before symptoms are manifest. These quantitative imaging biomarkers are used in drug development and thus our methods will be useful to the Pharmaceutical industry and Advanced Imaging Centres. Improved quantification and accuracy will also improve statistical power in clinical trials, potentially allowing studies using smaller numbers of subjects or able to reach significant conclusions earlier.
The clinical imaging industry
Impact is envisaged via the adoption of techniques we develop into the continuous improvement of commercial scanners by the main manufacturers (General Electric, Siemens and Philips). Industry representatives attend the Collaborative Computational Project (CCP) which is integrated with this project and our results and algorithms are freely available with a software licence that permits commercial use. Whilst aimed at dual-modality PET-MR systems, some of our techniques will be applicable to single modality systems which currently have a much larger market share.
The preclinical imaging industry
Major leading companies such as General Electric and Siemens who were originally leading the preclinical PET-MR imaging industry have disengaged due to lack of financial growth in the preclinical imaging market. Three main preclinical PET-MR scanner developers are currently leading the market: Mediso, Bruker and MRsolutions. These are relatively medium size enterprises which strive to resource image reconstruction due to the complexity and resource demands involved. It is harder for these small preclinical PET-MR companies to maintain and extend advanced software, which needs to include detailed modelling of physical and physiological phenomena. Thus, open access of reconstruction software will foster the preclinical PET-MR imaging sector.
Patients
In the long-term, new imaging can benefit patients through more accurate and earlier diagnosis, and assessment of response to therapy. An early assessment of therapy response allows for a change in dose or management to match the response of the patient. This could lead to effective treatment occurring faster, or prevent prolonged unnecessary treatments with negative side-effects.
Health Service
Improved diagnosis and monitoring of disease and therapy response allows a more rational use of healthcare resources. Although imaging costs for PET-MR are of the order £1K/scan, the potential saving in costs of drugs or other interventions through better and earlier decisions is likely to far exceed this cost. In the UK, the NHS spends only about 7% of its budget on imaging.
Pharmaceutical Industry & Advanced imaging centres
The combined use of PET and MR offers the possibility of making measurements from PET and MR images more reliable and reproducible between different patients and imaging centres. Changes within a patient's images can be used as biomarkers for disease, potentially showing disease changes before symptoms are manifest. These quantitative imaging biomarkers are used in drug development and thus our methods will be useful to the Pharmaceutical industry and Advanced Imaging Centres. Improved quantification and accuracy will also improve statistical power in clinical trials, potentially allowing studies using smaller numbers of subjects or able to reach significant conclusions earlier.
The clinical imaging industry
Impact is envisaged via the adoption of techniques we develop into the continuous improvement of commercial scanners by the main manufacturers (General Electric, Siemens and Philips). Industry representatives attend the Collaborative Computational Project (CCP) which is integrated with this project and our results and algorithms are freely available with a software licence that permits commercial use. Whilst aimed at dual-modality PET-MR systems, some of our techniques will be applicable to single modality systems which currently have a much larger market share.
The preclinical imaging industry
Major leading companies such as General Electric and Siemens who were originally leading the preclinical PET-MR imaging industry have disengaged due to lack of financial growth in the preclinical imaging market. Three main preclinical PET-MR scanner developers are currently leading the market: Mediso, Bruker and MRsolutions. These are relatively medium size enterprises which strive to resource image reconstruction due to the complexity and resource demands involved. It is harder for these small preclinical PET-MR companies to maintain and extend advanced software, which needs to include detailed modelling of physical and physiological phenomena. Thus, open access of reconstruction software will foster the preclinical PET-MR imaging sector.
Organisations
- University College London (Lead Research Organisation)
- University Hospital Leipzig (Collaboration)
- Microsoft Research (Collaboration)
- National Institutes of Health (NIH) (Collaboration)
- GE Heatlhcare (Collaboration)
- Physikalisch-Technische Bundesanstalt (Collaboration)
- Commonwealth Scientific and Industrial Research Organisation (Collaboration)
Publications
Deidda D
(2019)
Hybrid PET/MR Kernelised Expectation Maximisation Reconstruction for Improved Image-Derived Estimation of the Input Function from the Aorta of Rabbits.
in Contrast media & molecular imaging
Einspänner E
(2022)
Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a robotic system.
in EJNMMI physics
Abascal J
(2021)
Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach
in IEEE Access
Twyman R
(2023)
An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction.
in IEEE transactions on medical imaging
Twyman R
(2023)
An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction.
in IEEE transactions on medical imaging
Brusaferri L
(2022)
Detection Efficiency Modeling and Joint Activity and Attenuation Reconstruction in Non-TOF 3-D PET From Multiple-Energy Window Data
in IEEE Transactions on Radiation and Plasma Medical Sciences
Brusaferri L
(2020)
Joint Activity and Attenuation Reconstruction From Multiple Energy Window Data With Photopeak Scatter Re-Estimation in Non-TOF 3-D PET
in IEEE Transactions on Radiation and Plasma Medical Sciences
Akerele MI
(2020)
Comparison of Correction Techniques for the Spill in Effect in Emission Tomography.
in IEEE transactions on radiation and plasma medical sciences
Tsai Y
(2021)
Penalized PET/CT Reconstruction Algorithms With Automatic Realignment for Anatomical Priors
in IEEE Transactions on Radiation and Plasma Medical Sciences
Arridge S
(2020)
Preface to special issue on joint reconstruction and multi-modality/multi-spectral imaging
in Inverse Problems
| Description | This Software Flagship project contributes heavily to the open source software SIRF (Synergistic Image Reconstruction Framework) and its underlying component STIR (Software for Tomographic Image Reconstruction) which is used for PET reconstruction. It also contributes various fixes and enhancements to other supporting packages. The contributions of this project concentrate on image registration, motion correction, simulation, but also increasing support for various file formats to be able to compare images with DICOM images from the scanner. We have also developed a method for PET/MR head motion correction that uses both MR and PET data for motion detection and estimation in separate parts of the acquisition. Part of this work has been presented at international conferences and journals, with a further publication under review. In collaboration with CSIRO we have developed a novel method for continuous head pose estimation from PET data (using a surrogate signal obtained from the data and various position estimates). This work was presented during an oral presentation at the IEEE Medical Imaging Conference 2019 to excellent response. Taken together, this work has the potential to remove the need for external monitoring equipment for head motion, simplifying set-up and workflow, especially important in PET/MR. |
| Exploitation Route | The methods developed here are part of our open source package SIRF for others to explore and validate. Novel methods could be taken up by other researchers as well industry to improve image quality and therefore patient outcome. |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare |
| URL | https://www.ccpsynerbi.ac.uk/ |
| 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 | 04/2020 |
| End | 03/2025 |
| Description | Deep Learning for Joint Reconstruction for PET-MR |
| Amount | £105,000 (GBP) |
| Funding ID | 2407114 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2020 |
| End | 09/2024 |
| Description | PhD Studentship on PET Motion Correction |
| Amount | £100,000 (GBP) |
| Organisation | GE Healthcare Limited |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 10/2018 |
| End | 03/2022 |
| 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 | 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 | SpatialCalibration MR 2D Sagittal |
| Description | Included are: vendor-reconstructed DICOM images in NIfTI format (dicom_as_nifti.nii) the scanner raw MR data (meas_MID00749_FID151779_t2_tse_sag.dat) the raw MR data in h5 format, converted using siemens_to_ismrmrd (meas_MID00749_FID151779_t2_tse_sag.h5) the MR data reconstructed with SIRF in .h5 format (SIRF_recon.h5) the MR data reconstructed with SIRF that has been converted to NIfTI format (SIRF_recon.nii) the script used to generate the data (SIRF_recon.sh) |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3571228 |
| Title | SpatialCalibration MR 2D Sagittal |
| Description | Included are: vendor-reconstructed DICOM images in NIfTI format (dicom_as_nifti.nii) the scanner raw MR data (meas_MID00749_FID151779_t2_tse_sag.dat) the raw MR data in h5 format, converted using siemens_to_ismrmrd (meas_MID00749_FID151779_t2_tse_sag.h5) the MR data reconstructed with SIRF in .h5 format (SIRF_recon.h5) the MR data reconstructed with SIRF that has been converted to NIfTI format (SIRF_recon.nii) the script used to generate the data (SIRF_recon.sh) |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3571227 |
| 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 | 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 | Software collaboration with the Gadgetron project for Open Source software for MR image reconstruction |
| Organisation | Microsoft Research |
| Country | Global |
| Sector | Private |
| PI Contribution | Our contributions have been small at present. We have contributed bug fixes and documentation enhancements and provide extra testing. We are also suggesting alternative ways for building the interface between the Gadgetron and Python and MATLAB, based on our own work. |
| Collaborator Contribution | The Gadgetron is one of the best-established open source projects for MR image reconstruction and used widely in the MR community. It is able to reconstruct data from many clinical systems. Moreover, we have had discussions on software architecture that help us in building a computationally efficient tool for the research community. |
| Impact | As our CCP is developing synergistic PET-MR reconstruction, being able to use the Gadgetron is a large time and money saver in terms of man-years, but also in terms of available expertise. |
| Start Year | 2015 |
| Description | Software collaboration with the Gadgetron project for Open Source software for MR image reconstruction |
| Organisation | National Institutes of Health (NIH) |
| Department | National Heart, Lung, and Blood Institute (NHLBI) |
| Country | United States |
| Sector | Public |
| PI Contribution | Our contributions have been small at present. We have contributed bug fixes and documentation enhancements and provide extra testing. We are also suggesting alternative ways for building the interface between the Gadgetron and Python and MATLAB, based on our own work. |
| Collaborator Contribution | The Gadgetron is one of the best-established open source projects for MR image reconstruction and used widely in the MR community. It is able to reconstruct data from many clinical systems. Moreover, we have had discussions on software architecture that help us in building a computationally efficient tool for the research community. |
| Impact | As our CCP is developing synergistic PET-MR reconstruction, being able to use the Gadgetron is a large time and money saver in terms of man-years, but also in terms of available expertise. |
| Start Year | 2015 |
| 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 SyneRBI SIRF |
| Description | Minor patch on 3.1.0 added external project astra-python-wrapper to allow updates of ASTRA without conflicts #605 fix docker/entrypoint for case where a user has a GID that already exists in the Docker image #606 |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | This is an update to our software for synergistic image reconstruction. SIRF 3.1.0 (and this patch release 3.1.1) main addition is the support of Golden-angle radial phase encoding (RPE) trajectories for MR. This opens up possibilities for advanced applications in MR and PET/MR, in particular for motion compensation. This was illustrated for cardiac PET/MR where the motion was obtained from an RPE sequence and used for motion compensation of both MR and PET data. |
| URL | https://zenodo.org/record/5075290 |
| Title | CCP SyneRBI SIRF |
| Description | New features PET Addition of
sirf.STIR.ScatterEstimation and
ScatterSimulation to allow (non-TOF) scatter estimation in PET GE Signa PET/MR reading of listmode data, sinograms, normalisation and randoms support added. If STIR is at least version 5 or built from the master branch, Georg Schramm's parallel (computing) projector is now made available from SIRF (use
AcquisitionModelUsingParallelproj). This uses Joseph interpolation, but importantly can use your GPU (if CUDA was found during building). Implemented extraction of the operator representing the linear part of PET acquisition model and computation of its norm. When adding a shape to a
sirf.STIR.ImageData, optionally give the number of times to sample a voxel. This is useful when the shape partially - but not completely - fills a voxel. If
storage_scheme is set to
memory,
PETAcquisitionData allows direct modification, whereas before a copy would need to be created first. (Internally, it uses STIR
ProjDataInMemory, instead of
ProjDataFromStream). Registration Registration of 2d images is now supported with aladin and f3d. examples data: Installs
examples,
data and
doc to the install directory, i.e.
${CMAKE_INSTALL_PREFIX}/share/SIRF-. directory. If the
SIRF_DATA_PATH environment variable is set,
examples_data_path will search for the examples data there, or in
SIRF_INSTALL_PATH/share/SIRF-./data directory. In MATLAB, the
example_data_path function has the version set by CMake at install time. Other Python features: Define
__version__ in
sirf python package. Added implementation of division and multiplication for
NiftiImageData. Data validity checks return
NotImplemented instead of throwing error, opening the door for future implementations of operations on data. Backwards incompatible changes STIR version 4.1.0 is now required. Python 2 is no longer supported. Most code might still work, but we do not check. A warning is written when the Python version found is 2. This will be changed to
FATAL_ERROR at a later stage. Handling of coil images and sensitivities in C++ code simplified by inheriting CoilImagesVector from GadgetronImagesVector and replacing CoilSensitivitiesAsImages with CoilSensitivitiesVector, also inheriting from GadgetronImagesVector. All methods of CoilImagesVector and CoilSensitivitiesVector other than those inherited from GadgetronImagesVector are no longer supported except methods named compute(), which are renamed to calculate(). Deprecations (will be errors in SIRF 4.0)
Registration: renamed
Resample to
Resampler and
NiftyResample to
NiftyResampler. Old names are now deprecated but should still work. STIR
AcquisitionModel
forward,
direct,
backward and
adjoint signatures have changed in Python. Subset information should now be set via
num_subsets and
subset_num members.
Theforward
andbackward
members can still be called with the previous syntax but this will be removed in a later version. Note that default values ofnum_subsets
andsubset_num` are 0 and 1 respectively, such that default behaviour is default behaviour (i.e. process all data) is unchanged. MR acquisition data storage scheme restricted to memory only (a message will be printed but no error thrown) Use CMake variable names from
find_package(Python) which are available with CMake 3.12+. SIRF CMake files will accept both
Python_EXECUTABLE or
PYTHON_EXECUTABLE, for the latter it will send a deprecation warning. Other changes When registering, internally the forward displacement is no longer stored, replaced by the forward deformation. The inverse is no longer stored, and is calculated as needed.
PETAcquisitionData.axpby now uses STIR's
axpby and is therefore faster. Speed-up in
stir::AcquisitionDataInMemory of
as_array,
fill,
dot,
norm, etc. (by using STIR iterators). Added common Python
DataContainer algebra unit tests for all
DataContainer inherited classes. Continuous Integration now uses Github Actions. Travis-CI has been dropped. New
CMake option
BUILD_DOCUMENTATION to use doxygen to build C++ documentation. It will be installed in the
share/SIRF-version/doc/doxygen. Bug fixes Python
fill method in MR
DataContainer accepts
numpy array, number or
DataContainer.
get_index_to_physical_point_matrix() returned a wrong matrix in MATLAB and Python. path manipulation of
examples_data_path now should work for any platform, not just linux. |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | This major upgrade to our Synergistic Image Reconstruction Framework (SIRF) added several new features. Most notable were the addition of scatter simulation and estimation, and an interface to an external project providing a GPU projector, both for Positron Emission Tomography (PET). The former enables quantitative reconstruction of PET data, independent of the vendor of the scanners. The latter provides considerable speed-up of the image reconstruction process if GPU hardware is available. |
| URL | https://zenodo.org/record/4776289 |
| Title | CCP SyneRBI SIRF |
| Description | v3.1.0 MR/Gadgetron Golden-angle radial phase encoding (RPE) trajectory is supported if
Gadgetron toolboxes were found during building
WARNING if Gadgetron was compiled with CUDA support, you need to build SIRF with the Gadgetron_USE_CUDA CMake variable set to
ON. Automatic calling of
sort_by_time() in most places. This ensures that only consistent images are reconstructed. Encoding classes perform the Fourier transformations instead of the
MRAcquisitionModel
CoilSensitivitiesVector class now has forward and backward method using the encoding classes getting rid of the duplicate FFT code used to compute
coil sensitivities from
MRAcquisitionData. Added constructor for
GadgetronImagesVector from
MRAcquisitionData. This allows setting up an MR acquisition model without having to perform a reconstruction first. PET/STIR iterative reconstructors
set_current_estimate and
get_current_estimate now create a clone to avoid surprising modifications of arguments. The old behaviour of
set_current_estimate can still be achieved by
set_estimate.
Warning This is backwards incompatible, but arguably a bug fix. SIRF Python interface
range_geometry and
domain_geometry methods of
AcquisitionModel classes, required by CIL algorithms, now obtain data via respective C++
AcquisitionModel classes accessors, in line with our strategy of keeping interface code minimal
sirf.Gadgetron.AcquisitionData.get_info was renamed to
get_ISMRMRD_info to avoid confusion with the other
get_info() methods that return a string. (
get_info still works but issues a deprecation warning). Build system fix bug with older CMake (pre-3.12?) that the Python interface was not built #939. |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | The addition of support for MR Golden-angle radial phase encoding (RPE) trajectories enables a new class of MR sequencies for advanced applications in thoracic, and in particular cardiac, MR, where organ motion prevents good image quality with normal sequences. |
| URL | https://zenodo.org/record/5028210 |
| Title | PET Raw data tools v2.0 |
| Description | pet-rd-tools provides a set of tools for handling raw data from PET scanners. It enables researchers to use the data from their own scanners, unpack them etc, and then use as input for their own image reconstruction software, including our own STIR and SIRF packages. This second release adds support for data from GE PET/CT scanners. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | This software has enabled various researchers to handle data from their scanner with greater easy, leading to several publications on validation of the software and novel methods. It is now part of the suite of packages provided by our CCP PETMR/SyneRBI. |
| URL | https://github.com/UCL/pet-rd-tools/ |
| Title | SIRF 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 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/ |
| 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 | 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 | 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 2022 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | During this annual meeting users and developers presented their recent work with STIR with the emphasis on software and algorithmic development. This meeting aims to foster participation and sharing. This year, the meeting attracted about 40 researchers from all over the world, and was associated to the IEEE Medical Imaging Conference, held in Milan, Italy. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://stir.sourceforge.net/2022UsersMeeting/ |
| Description | Seminar (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 | 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 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 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 | 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 |