scholarly journals Parallel Implementation of Katsevich's FBP Algorithm

2006 ◽  
Vol 2006 ◽  
pp. 1-8 ◽  
Author(s):  
Jiansheng Yang ◽  
Xiaohu Guo ◽  
Qiang Kong ◽  
Tie Zhou ◽  
Ming Jiang
Keyword(s):  
Numerical Experiments ◽  
Parallel Implementation ◽  
Cone Beam Ct ◽  
Computation Time ◽  
Cone Beam ◽  
Filtered Backprojection ◽  
Typical Experiment ◽  
Linux Cluster ◽  
Parallel Reconstruction ◽  
Reconstruction Time

For spiral cone-beam CT, parallel computing is an effective approach to resolving the problem of heavy computation burden. It is well known that the major computation time is spent in the backprojection step for either filtered-backprojection (FBP) or backprojected-filtration (BPF) algorithms. By the cone-beam cover method [1], the backprojection procedure is driven by cone-beam projections, and every cone-beam projection can be backprojected independently. Basing on this fact, we develop a parallel implementation of Katsevich's FBP algorithm. We do all the numerical experiments on a Linux cluster. In one typical experiment, the sequential reconstruction time is 781.3 seconds, while the parallel reconstruction time is 25.7 seconds with 32 processors.

Regularized iterative weighted filtered backprojection for helical cone-beam CT

2008 ◽  
Vol 35 (9) ◽  
pp. 4173-4185 ◽  
Author(s):  
Johan Sunnegårdh ◽  
Per-Erik Danielsson
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Filtered Backprojection

scholarly journals Importance of Some Specifications of Heterogeneous Architectures (CPU+GPU) for 3D Cone-Beam-CT Image Reconstruction Using OpenCL

2021 ◽  
Vol 15 ◽  
pp. 275-284
Author(s):  
T. Nouioua ◽  
A. H. Belbachir
Keyword(s):  
Work Groups ◽  
Cone Beam Ct ◽  
Computation Time ◽  
Daily Practice ◽  
Cone Beam ◽  
X Rays ◽  
Heterogeneous Architectures ◽  
Fdk Algorithm ◽  
3D Volume ◽  
Real Challenge

Medical imaging has found an important way for routine daily practice using cone-beam computed tomography to reconstruct a 3D volume image using the Feldkamp-Davis-Kress (FDK) algorithm. This way can minimize the patient’s time exposure to X-rays. However, its implementation is very costly in computation time, which constitutes a handicap problem in practice. For this reason, the use of acceleration methods on GPU becomes a real solution. For the acceleration of the FDK algorithm, we have used the GPU on heterogeneous platforms. To take full advantage of the GPU, we have chosen useful features of the GPUs and, we have launched the acceleration of the reconstruction according to some technical criteria, namely the work-groups and the work-items. We have found that the number of parallel cores, as well as the memory bandwidth, have no effect on runtimes speedup without being rough in the choice of the number of work-items, which represents a real challenge to master in order to be able to divide them efficiently into work-groups according to the device specifications considered as principal difficulties if we do not study technically the GPU as a hardware device. After an optimized implementation using kernels launched optimally on GPU, we have deduced that the high capacities of the devices must be chosen with a rough optimization of the work-items which are divided into several work-groups according to the hardware limitations.

GPU-based parallel implementation of FDK algorithm for cone-beam CT

2013 ◽  
Vol 32 (5) ◽  
pp. 1407-1410 ◽  
Author(s):  
Yu HAN ◽  
Bin YAN ◽  
Chao-qun YU ◽  
Lei LI
Keyword(s):  
Parallel Implementation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Fdk Algorithm

scholarly journals Exact Interior Reconstruction with Cone-Beam CT

2007 ◽  
Vol 2007 ◽  
pp. 1-5 ◽  
Author(s):  
Yangbo Ye ◽  
Hengyong Yu ◽  
Ge Wang
Keyword(s):  
Singular Value Decomposition ◽  
Convex Set ◽  
Cone Beam Ct ◽  
Singular Value ◽  
Cone Beam ◽  
Filtered Backprojection ◽  
Volume Of Interest ◽  
Interior Problem ◽  
Value Decomposition ◽  
Svd Method

Using the backprojection filtration (BPF) and filtered backprojection (FBP) approaches, respectively, we prove that with cone-beam CT the interior problem can be exactly solved by analytic continuation. The prior knowledge we assume is that a volume of interest (VOI) in an object to be reconstructed is known in a subregion of the VOI. Our derivations are based on the so-called generalized PI-segment (chord). The available projection onto convex set (POCS) algorithm and singular value decomposition (SVD) method can be applied to perform the exact interior reconstruction. These results have many implications in the CT field and can be extended to other tomographic modalities, such as SPECT/PET, MRI.

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