Results 1 
4 of
4
A Parallel Implementation of the Katsevich algorithm for 3D
 CT Image Reconstruction”, The Journal of Supercomputing
, 2006
"... Abstract. Yu and Wang [1, 2] implemented the first theoretically exact spiral conebeam reconstruction algorithm developed by Katsevich [3, 4]. This algorithm requires a high computational cost when the data amount becomes large. Here we study a parallel computing scheme for the Katsevich algorithm ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Abstract. Yu and Wang [1, 2] implemented the first theoretically exact spiral conebeam reconstruction algorithm developed by Katsevich [3, 4]. This algorithm requires a high computational cost when the data amount becomes large. Here we study a parallel computing scheme for the Katsevich algorithm to facilitate the image reconstruction. Based on the proposed parallel algorithm, several numerical tests are conducted on a high performance computing (HPC) cluster with thirty two 64bit AMDbased Opteron processors. The standard phantom data [5] is used to establish the performance benchmarks. The results show that our parallel algorithm significantly reduces the reconstruction time, achieving high speedup and efficiency.
Parallel ART for image reconstruction in CT using processor arrays
, 2004
"... Algebraic Reconstruction Technique (ART) is a widelyused iterative method for solving sparse systems of linear equations. This method (originally due to Kaczmarz) is inherently sequential according to its mathematical definition since, at each step, the current iterate is projected toward one of th ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Algebraic Reconstruction Technique (ART) is a widelyused iterative method for solving sparse systems of linear equations. This method (originally due to Kaczmarz) is inherently sequential according to its mathematical definition since, at each step, the current iterate is projected toward one of the hyperplanes defined by the equations. The main advantages of ART are its robustness, its cyclic convergence on inconsistent systems, and its relatively good initial convergence. ART is widely used as an iterative solution to the problem of image reconstruction from projections in computerized tomography (CT), where its implementation with a small relaxation parameter produces excellent results. It is shown that for this particular problem, ART can be implemented in parallel on a linear processor array. pffiffiffi Reconstructing an image of n pixels from Q(n) equations can be done on a linear array of p Oð nÞ processors with optimal efficiency (linear speedup) and O(n/p) memory for each processor. The parallel technique can be applied to various geometric models of image reconstruction, as well as to 3D reconstruction with spherically symmetric volume elements, using a 2D rectangular meshconnected array of processors.
Review of Parallel Computing Techniques for Computed Tomography Abstract Image Reconstruction
"... After we briefly review representative analytic and iterative reconstruction algorithms for Xray computed tomography (CT), we address the need for faster reconstruction by parallel computing techniques. For a decent, a conebeam reconstruction usually takes hours on a regular PC, since most of algo ..."
Abstract
 Add to MetaCart
After we briefly review representative analytic and iterative reconstruction algorithms for Xray computed tomography (CT), we address the need for faster reconstruction by parallel computing techniques. For a decent, a conebeam reconstruction usually takes hours on a regular PC, since most of algorithms take more than 60 iterations even longer. In order to speedup the performance, people introduce various acceleration methodologies including algorithm improvements, chip utilization, and parallel computing technique. This paper focuses on the speedup the computation using parallel computing. The first generation of parallel computing systems was based on a centralized parallel configuration. The second generation of such systems employed a cluster of generalpurpose computers that are connected by a fast local area network (LAN). Hereby, we highlight distributed parallel computing techniques: from a locally distributed clientserver topology to a peertopeer (P2P) enhanced network model. With the P2P technology, the client would be directly connected to all other computing peers seamlessly, forming a virtual parallel computer. There are multiple Internet connections between the client and other computing peers. This way, a single failure of node wouldn’t cause the entire failure of computation. Finally, we state that by integrating the largescale geographically distributed systems such as Grid computing technology the future of the CT reconstruction will be highly parallel, efficient, scalable over the Internet, so will be other biomedical imaging tasks. 1.
1.1 The Computational Burden of Regularized
"... Xray computerized tomography (CT) and related imaging modalities (e.g., PET) are notorious for their excessive computational demands, especially when noiseresistant probabilistic methods such as regularized tomography are used. The basic idea of regularizated tomography is to compute a smooth imag ..."
Abstract
 Add to MetaCart
Xray computerized tomography (CT) and related imaging modalities (e.g., PET) are notorious for their excessive computational demands, especially when noiseresistant probabilistic methods such as regularized tomography are used. The basic idea of regularizated tomography is to compute a smooth image whose simulated projections (line integrals) approximate the observed, noisy Xray projections. The computational expense in previous methods stems from explicitly applying a large sparse projection matrix to enforce these smoothness and data fidelity constraints during each of many iterations of the algorithm. Here we review our recent work in regularized tomography in which the smoothness constraint is analytically transformed from the image to the projection domain, before any computations begin. As a result, iterations take place entirely in the projection domain, avoiding the repeated sparse matrixvector products. A more surprising benefit is the decoupling of a large system of regularization equations into many small systems of simpler independent equations, whose solution requires an “embarassingly parallel ” computation. In this paper, we demonstrate that this method provides linear speedup of regularized tomography for up to 20 compute nodes (Pentium 4, 1.5 GHz) on a 100 Mb/s network using a Matlab MPI implementation.