Optimization for limited angle tomography in medical image processing

Authors:
Xiaoqiang Lu;Yi Sun;Yuan Yuan
Affiliations:
School of Electronic and Information Engineering, Dalian University of Technology, China;School of Electronic and Information Engineering, Dalian University of Technology, China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...
Venue:
Pattern Recognition
Year:
2011

Citing 9
Cited 2

Incomplete data problems in X-ray computerized tomography. I. Singular value decomposition of the limited angle transform

Numerische Mathematik
Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Singularities of the X-ray transform and limited data tomography in R2 and R3

SIAM Journal on Mathematical Analysis
Mathematical methods in image reconstruction

Mathematical methods in image reconstruction
Kaczmarz extended algorithm for tomographic image reconstruction from limited-data

Mathematics and Computers in Simulation
Adaptive wavelet-Galerkin methods for limited angle tomography

Image and Vision Computing
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

IEEE Transactions on Information Theory
Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography

IEEE Transactions on Image Processing
Total variation blind deconvolution

IEEE Transactions on Image Processing

Local tomography based on grey model

Neurocomputing
Nonparametric optimization of constrained total variation for tomography reconstruction

Computers in Biology and Medicine

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Abstract

This paper aims to reduce the problems of incomplete data in computed tomography, which happens frequently in medical image process and analysis, e.g., when the high-density region of objects can only be penetrated by X-rays at a limited angular range. As the projection data are available only in an angular range, the incomplete data problem can be attributed to the limited angle problem, which is an ill-posed inverse problem. Image reconstruction based on total variation (TV) reduces the problem and gives better performance on edge-preserving reconstruction; however, the artificial parameter can only be determined through considerable experimentation. In this paper, an effective TV objective function is proposed to reduce the inverse problem in the limited angle tomography. This novel objective function provides a robust and effective reconstruction without any artificial parameter in the iterative processes, using the TV as a multiplicative constraint. The results demonstrate that this reconstruction strategy outperforms some previous ones.