Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware

By Fang Xu, Klaus Mueller. IEEE Transactions on Nuclear Science, 2005

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Evaluation of Sparse-view Reconstruction from Flat-panel-detector Cone-beam CT (2010, background): “…can be enhanced substantially through streamlining/parallelizing its implementation and/or by exploiting the available, or rapidly available, high performance computational hardware such as multi-core CPU, graphic processing unit (GPU) (Xu and Mueller 2005) and cell engines (Kachelrieß et al 2007).”
Fast, Accurate and Shift-Varying Line Projections for Iterative Reconstruction Using the GPU (2009, methodology): “[21] and cone-beam X-ray CT [24] and are applicable to PET”
Medical image processing on the GPU - Past, present and future (2013, methodology): “One year later, the work was extended to three popular reconstruction algorithms; FBP, SART, and expectation maximization (Xu and Mueller, 2005).”
MR image reconstruction using the GPU (2006, methodology): “12 In our work, we also implement the ﬁltered backprojection technique as the radial samples can be also treated as projection data, thanks to the central slice theorem 1 .”
GPU computing in medical physics: a review. (2011, methodology): “14 Several other iterative algorithms were also adapted to the GPU, including expectation-maximization, 15,24 ordered-subsets expectation-maximization, 24,25 the ordered-subsets convex algorithm, 26 and total-variation reconstruction.”