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EM+TV for Reconstruction of Cone-beam CT with Curved Detectors using GPU

Jianwen Chen, Ming Yan, Luminita A. Vese, John Villasenor, Alex Bui, Jason Cong
Department of Electrical Engineering, University of California, Los Angeles, CA 90095, USA
Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2011

@inproceedings{chen2011em,

   title={EM+ TV for reconstruction of cone-beam CT with curved detectors using GPU},

   author={Chen, J. and Yan, M. and Vese, L.A. and Villasenor, J. and Bui, A. and Cong, J.},

   booktitle={Proceedings of International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},

   pages={363–366},

   year={2011}

}

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Computerized tomography (CT) plays a critical role in the practice of modern medicine. However, the radiation associated with CT is significant. Methods that can enable CT imaging at reduced radiation exposure without sacrificing image quality are therefore extremely important. This paper introduces a novel method for enabling improved reconstruction at lower radiation exposure levels. The method is based on the combination of 1) expectation maximization (EM), an iterative method used for CT image reconstruction that maximizes the likelihood function under a Poisson noise assumption, and 2) total variation (TV) regularization, which has been used to preserve edges, given the assumption that most images are piecewise constant. While both EM and TV are known, their combination, as described here, is novel. We show that EM+TV can reconstruct a better image using fewer views, thus reducing the overall dose of radiation. Numerical results show the efficiency of the EM+TV method in comparison to classic EM. In addition, the EM+TV algorithm is implemented on the GPU platform; related implementation methods are also discussed.
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