EM+TV based reconstruction for cone-beam CT with reduced radiation

Authors:
Ming Yan;Jianwen Chen;Luminita A. Vese;John Villasenor;Alex Bui;Jason Cong
Affiliations:
Department of Mathematics, University of California, Los Angeles, CA;Department of Electrical Engineering, University of California, Los Angeles, CA;Department of Mathematics, University of California, Los Angeles, CA;Department of Electrical Engineering, University of California, Los Angeles, CA;Department of Radiological Sciences, University of California, Los Angeles, CA;Department of Computer Science, University of California, Los Angeles, CA
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Year:
2011

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Abstract

Computerized tomography (CT) plays a critical role in modern medicine. However, the radiation associated with CT is significant. Methods that can enable CT imaging with less radiation exposure but without sacrificing image quality are therefore extremely important. This paper introduces a novel method for enabling image reconstruction at lower radiation exposure levels with convergence analysis. The method is based on the combination of expectation maximization (EM) and total variation (TV) regularization. While both EM and TV methods are known, their combination as described here is novel. We show that EM+TV can reconstruct a better image using much fewer views, thus reducing the overall dose of radiation. Numerical results show the efficiency of the EM+TV method in comparison to filtered backprojection and classic EM. In addition, the EM+TV algorithm is accelerated with GPU multicore technology, and the high performance speed-up makes the EM+TV algorithm feasible for future practical CT systems.