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Title: Iterative algorithms for volumetric X-ray computed tomography
Author: Qiu, Wei
ISNI:       0000 0004 2734 5607
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2012
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Cone beam computed tomography (CBCT) enables a volumetric image reconstruction from a set of 2D projection data. This thesis studies the performance of a wide range iterative algorithms in various aspects, aiming to generate a better CBCT image reconstruction, especially when projection data is limited. We have implemented a wide range of algebraic iterative algorithms. Hence, the performance of ART, SART and OS-SART is studied based on a range of image quality measures. The major limitations of traditional iterative methods are their computational time. The conjugate gradients (CG) algorithm and its variants can be used to solve linear systems of equations arising from CBCT. Their applications can be found in a general linear algebra context, but in tomography problems (e.g. CBCT reconstruction) they have not widely been used. Hence, CBCT reconstruction using the CG-type algorithm LSQR was implemented and studied. In CBCT reconstruction, the main computational challenge is that the matrix A usually is very large, and storing it in full requires an amount of memory well beyond the reach of commodity computers. Because of these memory capacity constraints, only a small fraction of the weighting matrix A is typically used, leading to a poor reconstruction. In this final part of the thesis, to overcome this diculty, the matrix A is partitioned and stored blockwise, and blockwise matrix-vector multiplications are implemented within LSQR. This implementation allows us to use the full weighting matrix A for CBCT reconstruction without further enhancing computer standards. Tikhonov regularization has been developed in this framework, and can produce significant improvement in the reconstructed images for limited data case.
Supervisor: Soleimani, Manuchehr Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available