Combined algorithmic and GPU acceleration for ultra-fast circular conebeam backprojection

Brokish, J.; Sack, Paul; Bresler, Yoram

doi:10.1117/12.844028

Cited by 1 publication

(1 citation statement)

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“…Projection operators can be accelerated by optimized implementation on parallel hardware using graphical processing units (GPUs) (Keck and Hofmann 2009, Brokish et al 2010, Papenhausen et al 2011, Pratx and Xing 2011 or distributed computing (Liu et al 2013). However, for large-FOV high resolution CBCT data, both approaches suffer from latency of data transfer-either between host computer and GPU or between computational nodes in a distributed network.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution iterative reconstruction in high-resolution extremity cone-beam CT

et al. 2016

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Application of model-based iterative reconstruction (MBIR) to high resolution cone-beam CT (CBCT) is computationally challenging because of the very fine discretization (voxel size <100 µm) of the reconstructed volume. Moreover, standard MBIR techniques require that the complete transaxial support for the acquired projections is reconstructed, thus precluding acceleration by restricting the reconstruction to a region-of-interest. To reduce the computational burden of high resolution MBIR, we propose a multiresolution Penalized-Weighted Least Squares (PWLS) algorithm, where the volume is parameterized as a union of fine and coarse voxel grids as well as selective binning of detector pixels. We introduce a penalty function designed to regularize across the boundaries between the two grids. The algorithm was evaluated in simulation studies emulating an extremity CBCT system and in a physical study on a test-bench. Artifacts arising from the mismatched discretization of the fine and coarse sub-volumes were investigated. The fine grid region was parameterized using 0.15 mm voxels and the voxel size in the coarse grid region was varied by changing a downsampling factor. No significant artifacts were found in either of the regions for downsampling factors of up to 4×. For a typical extremities CBCT volume size, this downsampling corresponds to an acceleration of the reconstruction that is more than five times faster than a brute force solution that applies fine voxel parameterization to the entire volume. For certain configurations of the coarse and fine grid regions, in particular when the boundary between the regions does not cross high attenuation gradients, downsampling factors as high as 10× can be used without introducing artifacts, yielding a ~50× speedup in PWLS. The proposed multiresolution algorithm significantly reduces the computational burden of high resolution iterative CBCT reconstruction and can be extended to other applications of MBIR where computationally expensive, high-fidelity forward models are applied only to a sub-region of the field-of-view.

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Section: Introductionmentioning

confidence: 99%