X-RAY CT Reconstruction by using Spatially Non Homogeneous ICD Optimization
U. Satheeshwaran1, N. Sreekanth2, J.Surendiran3
1J. Surendiran, Professor, Department of ECE, HKBKCE, Bangalore (Karnataka), India.
2Dr. U. Satheeshwaran, Professor, Department of ECE, Malla Reddy Engineering College for Women Autonomous, Secunderabad (Telangana), India.
3Dr. N. Sreekanth, Professor, Department of ECE, Malla Reddy Engineering College for Women Autonomous, Secunderabad (Telangana), India.
Manuscript received on 01 November 2019 | Revised Manuscript received on 13 November 2019 | Manuscript Published on 22 November 2019 | PP: 2043-2046 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13520986S319/19©BEIESP | DOI: 10.35940/ijeat.F1352.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recent applications of conventional iterative coordinate descent (ICD) algorithms to multislice helical CT reconstructions have shown that conventional ICD can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of conventional algorithm in the practical applications. Among the various iterative methods that have been studied for conventional, ICD has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 3-D optimization algorithm that uses a quadratic substitute function to upper bound the local 3-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.
Keywords: Computed Tomography, Coordinate Descent, Iterative Algorithm, Conventional Algorithm.
Scope of the Article: Cross Layer Design and Optimization