Hyperfast parallel-beam and cone-beam backprojection using the cell general purpose hardware.

By M. Kachelriess, M. Knaup, Olivier Bockenbach. 2007

106 citations recorded by the source corpus.

Tomographic image reconstruction, such as the reconstruction of computed tomography projection values, of tomosynthesis data, positron emission tomography or SPECT events, and of magnetic resonance imaging data is computationally very demanding. One of the most time-consuming steps is the backprojection. Recently, a novel general purpose architecture optimized for distributed computing became available: the cell broadband engine (CBE). To maximize image reconstruction speed we modified our parallel-beam backprojection algorithm [two dimensional (2D)] and our perspective backprojection algorithm [three dimensional (3D), cone beam for flat-panel detectors] and optimized the code for the CBE. The algorithms are pixel or voxel driven, run with floating point accuracy and use linear (LI) or nearest neighbor (NN) interpolation between detector elements. For the parallel-beam case, 512 projections per half rotation, 1024 detector channels, and an image of size 512(2) was used. The cone-beam backprojection performance was assessed by backprojecting a full circle scan of 512 projections of size 1024(2) into a volume of size 512(3) voxels. The field of view was chosen to completely lie within the field of measurement and the pixel or voxel size was set to correspond to the detector element size projected to the center of rotation divided by square root of 2. Both the PC and the CBE were clocked at 3 GHz. For the parallel backprojection of 512 projections into a 512(2) image, a throughput of 11 fps (LI) and 15 fps (NN) was measured on the PC, whereas the CBE achieved 126 fps (LI) and 165 fps (NN), respectively. The cone-beam backprojection of 512 projections into the 512(3) volume took 3.2 min on the PC and is as fast as 13.6 s on the cell. Thereby, the cell greatly outperforms today's top-notch backprojections based on graphical processing units. Using both CBEs of our dual cell-based blade (Mercury Computer Systems) allows to 2D backproject 330 images/s and one can complete the 3D cone-beam backprojection in 6.8 s.

Subfields: Radiation, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Maximum-Likelihood Estimation With a Contracting-Grid Search Algorithm (2010, methodology): “Cell/BE processor, a powerful yet inexpensive processing unit that has been widely applied for scientific computation [19]–[ 26 ].”
Fast Model-Based X-Ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization (2011, methodology): “In an effort to speed up the iterative reconstr uction, various hardware platforms are being considered [16], [17], and a variety of iterative methods, s uch as variations of expectation maximization (EM) [18], conjugate gradients (CG) [19], ordered subsets ( OS) [20], and iterative coordinate desce”
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization (2008, background): “Recently, there has been much effort developing accelerated projectors and back projectors through streamlined algorithms and special purpose hardware (Xu and Mueller 2007, Kachelriess et al 2007, Kole and Beekman 2006, Sharp et al 2007).”
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).”