CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction

Thibaut Merlin, Simon Stute, Didier Benoit, Julien Bert, Thomas Carlier, Claude Comtat, Marina Filipovic, Frédéric Lamare, Dimitris Visvikis
Phys. Med. Biol.. 2018-09-10; 63(18): 185005
DOI: 10.1088/1361-6560/aadac1

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1. Phys Med Biol. 2018 Sep 10;63(18):185005. doi: 10.1088/1361-6560/aadac1.

CASToR: a generic data organization and processing code framework for multi-modal
and multi-dimensional tomographic reconstruction.

Merlin T(1), Stute S, Benoit D, Bert J, Carlier T, Comtat C, Filipovic M, Lamare
F, Visvikis D.

Author information:
(1)INSERM, UMR1101, LaTIM, CHRU de Brest, Brest, France. Equally contributed.
Author to whom any correspondence should be addressed.

In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition
and organization are a major hurdle for the development of tomographic
reconstruction software. The implementation of a given reconstruction algorithm
is usually limited to a specific set of conditions, depending on the modality,
the purpose of the study, the input data, or on the characteristics of the
reconstruction algorithm itself. It causes restricted or limited use of
algorithms, differences in implementation, code duplication, impractical code
development, and difficulties for comparing different methods. This work attempts
to address these issues by proposing a unified and generic code framework for
formatting, processing and reconstructing acquired multi-modal and
multi-dimensional data. The proposed iterative framework processes in the same
way elements from list-mode (i.e. events) and histogrammed (i.e. sinogram or
other bins) data sets. Each element is processed separately, which opens the way
for highly parallel execution. A unique iterative algorithm engine makes use of
generic core components corresponding to the main parts of the reconstruction
process. Features that are specific to different modalities and algorithms are
embedded into specific components inheriting from the generic abstract
components. Temporal dimensions are taken into account in the core architecture.
The framework is implemented in an open-source C++ parallel platform, called
CASToR (customizable and advanced software for tomographic reconstruction).
Performance assessments show that the time loss due to genericity remains
acceptable, being one order of magnitude slower compared to a manufacturer’s
software optimized for computational efficiency for a given system geometry.
Specific optimizations were made possible by the underlying data set organization
and processing and allowed for an average speed-up factor ranging from 1.54 to
3.07 when compared to more conventional implementations. Using parallel
programming, an almost linear speed-up increase (factor of 0.85 times number of
cores) was obtained in a realistic clinical PET setting. In conclusion, the
proposed framework offers a substantial flexibility for the integration of new
reconstruction algorithms while maintaining computation efficiency.

DOI: 10.1088/1361-6560/aadac1
PMID: 30113313 [Indexed for MEDLINE]

Auteurs Bordeaux Neurocampus