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SIRF Synergistic Image Reconstruction Framework

Authors: Ovtchinnikov, Evgueni; Brown, Richard; Mayer, Johannes; Pasca, Edoardo; da Costa-Luis, Casper; Atkinson, David; Kolbitsch, Christoph; +17 Authors

SIRF Synergistic Image Reconstruction Framework

Abstract

MR: Added support for spiral trajectories that are pre-computed by the user (by exposing a trajectory setter). Writing images to .dcm files fixed. Running image processing chains after reconstruction fixed. Use C++ 17 if Gadgetron-support is enabled Support Gadgetron master of end November 2022 using find_package(gadgetron) in CMake, but still support old version by looking for toolboxes. PET/SPECT Require STIR 5.0 and support 5.1 If SIRF is compiled with STIR 5.1, we now support Pinhole SPECT via the PinholeSPECTUBMatrix acquisition model. Examples are provided (but the code is not yet tested). sirf.STIR.AcquisitionData constructor taking a scanner now has an extra optional argument tof_mash_factor (defaulting to 1). This is only functional if a STIR version supporting TOF is used. C++: Renamed PETAcquisitionData and derived classes to STIRAcquisitionData* as STIR now supports SPECT in addition to PET modality. Backward compatibility ensured by defining old nomenclature via typedefs until the release of SIRF 4. Documentation revision of READMEs for the examples changed Python test framework to pytest

If you use this software, please cite it using the metadata from this file.

Keywords

pet-mr, spect, medical imaging, synergistic reconstruction, image reconstruction

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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