Implementation of GPU accelerated SPECT reconstruction with Monte Carlo-based scatter correction
- 38 Downloads
Statistical SPECT reconstruction can be very time-consuming especially when compensations for collimator and detector response, attenuation, and scatter are included in the reconstruction. This work proposes an accelerated SPECT reconstruction algorithm based on graphics processing unit (GPU) processing.
Ordered subset expectation maximization (OSEM) algorithm with CT-based attenuation modelling, depth-dependent Gaussian convolution-based collimator-detector response modelling, and Monte Carlo-based scatter compensation was implemented using OpenCL. The OpenCL implementation was compared against the existing multi-threaded OSEM implementation running on a central processing unit (CPU) in terms of scatter-to-primary ratios, standardized uptake values (SUVs), and processing speed using mathematical phantoms and clinical multi-bed bone SPECT/CT studies.
The difference in scatter-to-primary ratios, visual appearance, and SUVs between GPU and CPU implementations was minor. On the other hand, at its best, the GPU implementation was noticed to be 24 times faster than the multi-threaded CPU version on a normal 128 × 128 matrix size 3 bed bone SPECT/CT data set when compensations for collimator and detector response, attenuation, and scatter were included.
GPU SPECT reconstructions show great promise as an every day clinical reconstruction tool.
KeywordsSPECT reconstruction Scatter correction Monte Carlo Graphics processing unit (GPU)
Tobias Bexelius works for HERMES Medical Solutions and Antti Sohlberg has a consulting agreement with HERMES Medical Solutions.
- 8.Jambor I, Kuisma A, Ramadan S, Huovinen R, Sandell M, Kajander S. et. al. Prospective evaluation of planar bone scintigraphy, SPECT, SPECT/CT, 18F-NaF PET/CT and whole body 1.5T MRI, including DWI, for the detection of bone metastases in high risk breast and prostate cancer patients: SKELETA clinical trial. Acta Oncol. 2016;55:59–67.CrossRefPubMedGoogle Scholar
- 10.Pedemonte S, Bousse A, Erlandsson K, Modat M, Arridge S, Hutton BF, et al. GPU accelerated rotation-based emission tomography reconstruction. IEEE Nuclear Science Symposium Conference Record 2010;2657–2661.Google Scholar
- 15.Woliner-van der Weg W, Deden LN, Meeuwis AP, Koenrades M, Peeters LH, Kuipers H, Laanstra GJ, Gotthardt M, Slump CH, Visser EP. A 3D-printed anatomical pancreas and kidney phantom for optimizing SPECT/CT reconstruction settings in beta cell imaging using 111In-exendin. EJNMMI Phys. 2016;3:29.CrossRefPubMedPubMedCentralGoogle Scholar
- 16.Woodcock E, Murphy T, Hemmings P, Longworth S. Techniques used in the GEM code for Monte Carlo neutronics calculations in reactors and other systems of complex geometry. Proc Conf Appl Comput Methods Reactor Probl. 1965;557:2.Google Scholar
- 17.Berger M, Hubbell J. XCOM. Photon cross sections on a personal computer. Natl Bur Stand Washington, DC (USA). Cent Radiat Res. 1987.Google Scholar
- 19.Salmon JK, Moraes MA, Dror RO, Shaw DE. Parallel random numbers: as easy as 1, 2, 3. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis 2011;1–12.Google Scholar
- 21.Satish N, Harris M, Garland M. Designing efficient sorting algorithms for manycore GPUs. In: Proceedings of the 2009 IEEE International Symposium on Parallel and Distributed Processing. 2009. pp. 1–10.Google Scholar
- 22.Zelen M, Severo NC. Probability functions. Handb Math Funct 1964 5;925–995.Google Scholar