NiftyRec is a software for tomographic reconstruction, providing the fastest GPU-accelerated reconstruction tools for emission and transmission computed tomography. NiftyRec supports a wide range of modalities:
- Positron Emission Tomography (PET) – with depth-dependent resolution modelling
- Single Photon Emission Computed Tomography (SPECT) – with depth-dependent resolution modelling
- X-Ray Computed Tomography and Tomosynthesis – cone-beam, fan-beam, helical cone-beam geometries
- Synchrotron X-Ray Tomography – parallel beam geometry
- Neutron Tomography – parallel beam geometry
- Optical Projection Tomography – parallel beam geometry
NiftyRec offers optimized projection, back-projection and core iterative reconstruction routines. Computationally intensive tasks are GPU accelarated to achieve accelerated performance. Fully 3-dimensional OSEM SPECT and PET iterative reconstructions can be obtained within a few seconds.
The Matlab and Python interfaces of NiftyRec are intended to enable fast prototyping and development of reconstruction algorithms. The Matlab and Python interfaces include simple demos of standard iterative reconstruction algorithms such as Maximum Likelihood Expectation Maximisation (MLEM), Ordered Subsets Expectation Maximisation (OSEM) and One Step Late Maximum A Posteriori Expectation Maximisation (OSL-MAPEM), applied to PET, SPECT, cone-beam X-Ray CT and parallel-beam X-Ray CT. Other imaging modalities and reconstruction algorithms can be easily implemented in a few lines of Matlab and Python.
NiftyRec was initially developed at the Centre for Medical Image Computing (CMIC) – University College London (UCL) during the period 2009-2012 and is currently being developed at the Martinos Center for Biomedical Imaging – Massachusetts General Hospital (MGH) – Harvard University.
Source Code: Download Page, GitHub (old version v2.3.2), SourceForge (older versions), OmicX Page, NiftyRec v2.3.2 (zip source)
Tutorials: Documentation, Wiki, Video Tutorials, Programming Manual, Doxygen
References: Bibliography, Reference Paper