CHANGES.md

ChangeLog

Current changes

• 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

• MR/Gadgetron

  • new MR reconstruction framework sorts the ISMRMRD::Acquisitions prior to reconstruction. This ensures that only consistent images are reconstructed.
  • Encoding classes perform the Fourier transformations instead of the MRAcquisitionModel
  • Golden-angle radial phase encoding (RPE) trajectory is supported.
  • 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.

• Build system

  • fix bug with older CMake (pre-3.12?) that the Python interface was not built #939.

v3.0.0

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. The forward and backward members can still be called with the previous syntax but this will be removed in a later version. Note that default values of num_subsets and subset_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.

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](https://github.com/gschramm/parallelproj proj) 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-<version_major>.<version_minor> 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-<version_major>.<version_minor>/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.

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.

v2.2.0

• Changed CCP PETMR to SyneRBI
• updates to steepest ascent demo
• STIR.AcquisitionData.get_info() returns a string that describes the scanner etc
• documentation fixes/additions

v2.2.0-rc.1

• A passthrough for both the maximum and minimum relative change during OSMAPOSL reconstruction has been added.
• We have now corrected the geometrical information of .h5 images (coming from ISMRMRD and Gadgetron). This means we can now convert them to other SIRF image types (e.g., NiftiImageData and STIRImageData). This is necessary for any kind of synergistic reconstruction. Further, to the best of our knowledge, this is the first ISMRMRD to NIfTI converter out there!
• The adjoint transformation has now been implemented for NiftyResample through the wrapping of NiftyMoMo.
• The following methods have been added to C++, python and matlab NiftyResample:
  • out = forward(in)
  • forward(out, in)
  • out = adjoint(in)
  • adjoint(out, in)
  • out = backward(in) <- alias for adjoint
  • backward(out, in) <- alias for adjoint
• Inverse deformation images. Inverse displacements are also possible by converting to and from deformations.
• NiftyPET projector wrapped (if STIR is built with NiftyPET)
• Added set_image_data_processor to PETAcquisitionModel. This allows for instance image-based PSF modelling.
• Resampling of complex images.
• SPM registration wrapping (only SPM12 tested). If Matlab and SPM are present, the SPM wrapper is available from C++, Matlab and Python.
• Support for registering multiple floating images has been added. This is only available for certain algorithms (currently only SPM). There are therefore new methods add_floating_image and clear_floating_images on top of the original set_floating_image. Methods extracting the results of registrations can now be called with an index (get_output(idx = 0), get_transformation_matrix_forward(idx = 0), etc.). This index defaults to the first to maintain backwards compatibility.
• Ability to pad NiftiImageData, e.g., a.pad([10,10,0],[10,10,0]) to add 10 voxels to the minimum and maximum of the x- and y-directions.
• Ability to set and get STIR verbosity from python.
• Save STIR images using a parameter file (e.g., for saving as .nii)
• Default F3d to using non-symmetric version (previously, symmetric was used). Option to use the symmetric in C++, but currently exposed to python and matlab as we suspect there is an upstream bug there.

v2.1.0

• PET/STIR
  • Interfaced HKEM into SIRF
  • Interfaced SeparableGaussianImageFilter into SIRF
• MR/Gadgetron
  • Added DICOM-writing gadgets for MR images output
  • Added few Gadgetron GPU gadgets to SIRF gadget library
  • Enabled handling of 3D slices of MR images by switching to 3D FFT
• Python
  • Switched to new class style
  • Introduced contiguity checks of filled data
• CIL/SIRF Compatibility
  • added methods to AcquisitionData, ImageData and AcquisitionModel to be compatible with CCPi's Core Imaging Library (CIL)

v2.0.0

• Set CMake policy CMP0079.
• Use swig_add_library instead of swig_add_module.
• Averaging of rigid transformation matrices via quaternions (and therefore a quaternion class).
• Arrays of SIRF objects can be passed from the Python and Matlab interfaces to the C++ level (e.g., averaging a large number of matrices) via the DataHandleVector class. This is an internal class that should not be used. Simply pass a native array of objects and SIRF will convert to the DataHandleVector class if necessary.
• Image data role checks in MRAcquisitionModel introduced.
• Corrected ISMRMRD acquisition sorting.
• Added PhysioInterpolationGadget and FatWaterGadget to SIRF gadgets library.
• Wrapping of NiftyReg to allow registration/resampling in SIRF.
• Implemented new ImageData hierarchy common to PET and MR. ImageData contain geometrical info.
• MR/Gadgetron
  • Added default constructor and set_up to MRAcquisitionModel
  • Implemented sorting of MR images
  • Implemented reading of MR acquisition data from ISMRMRD file
• PET/STIR
  • projectors can now handle subsets (although with a somewhat ugly work-around)
  • added FBP2D, SSRB and the Parallel Level Sets prior
  • added TOF bins dimension to PETAcquisitionData (still fixed to have size 1)
• C++ changes
  • Removed using statements from the C++ header files
  • Created namespace sirf
  • include files are now moved to subdirectories (such as include/sirf/common).
  • Modified ObjectHandle type so that it can handle both std::shared_ptr and boost::shared_ptr.
• Python/MATLAB:
  • petmr_data_path is now obsolete. Use examples_data_path instead.
• Python:
  • everything is now in a sirf module. Use for instance import sirf.Gadgetron
• Matlab:
  • in keeping with changes to c++ and python, classes are now called with e.g., sirf.STIR.obj instead of mSTIR.obj. Aliases can be used to shorten this (e.g., PET=set_up_PET() and then PET.obj).
• CMake:
  • Updated minimum required version of CMake to 3.9.0.

v1.1.0

• Various bug fixes and corrections
• BUILD_STIR_WITH_OPENMP is now ON by default
• Gadgetron data processors check for Gadgetron server crash
• More data files in SIRF/data/examples/MR
• Grayscale plotting enabled

v1.1.0-rc.1

• Created a python sirf package (recommended way of importing)
  • aliased p(Gadgetron|STIR|Utilities) -> sirf.(Gadgetron|STIR|Utilities)
  • added setup.py
  • exposed cmake variable PYTHON_STRATEGY. Options:
    • PYTHONPATH: prefix $PYTHONPATH (default)
    • SETUP_PY: execute ${PYTHON_EXECUTABLE} setup.py install
    • CONDA: do nothing
• Added PYTHON_DEST_DIR variable, which allows the user to select the install destination of the SIRF python modules. PYTHON_DEST_DIR is a cached variable which can be updated on the GUI. If PYTHON_DEST_DIR is not set, we will install in ${CMAKE_INSTALL_PREFIX}/python. Likewise for MATLAB_DEST_DIR.
• Some improvements to the demos. Note that PET reconstruction demos have somewhat different parameters.
• Implemented PLS Prior
• Implemented 2D Filtered Back Projection

v1.0.0

• Access to all MR images and acquisition parameters
• All 8 file IO available (PET: Interfile, MR: HDF5)
• PET
  • PETAcquisitionData object creation from scanner name and parameters
  • ListmodeToSinograms converter class, also estimating randoms (from delayed coincidences)
  • Normalization from ECAT8 (Siemens mMR) and attenuation image
  • Build with OpenMP delivers stable and substantially accelerated performance
• More documentation
  • Developer's Guide
  • Doxygen inline documentation (available on SyneRBI website)
• More tests (now run via CTest), for Python, Matlab and C++.
• Coverage reporting for Python tests done by ctest

v0.9.2

• fixed version number and avoid confusing with wrong tag v0.9.1

v0.9.1

• PET data algebra implemented
• Storage scheme (file/memory) management for acquisition data implemented
• Using single precision float Matlab and Python arrays now
• Argument validity checks introduced
• Consistent naming scheme for libraries and modules adopted
• Matlab tests added
• User Guide Appendix on advanced features added
  • Storage scheme management
  • Programming Gadgetron chains
• Specific versions of dependencies (ISMRMRD, Gadgetron, STIR, SIRF) in SuperBuild
• SuperBuild update for Virtual Machine

v0.9.0

• first release