Tensor-based dictionary learning for dynamic tomographic reconstruction

Physics in Medicine and Biology
Shengqi TanHengyong Yu

Abstract

In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction.

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Citations

Jun 12, 2016·International Journal of Computer Assisted Radiology and Surgery·Davood Karimi, Rabab K Ward
Sep 10, 2016·Journal of X-ray Science and Technology·Xiumei TianZhaoying Bian
Aug 20, 2016·IEEE Transactions on Medical Imaging·Yanbo ZhangHengyong Yu
Jul 22, 2020·Physics in Medicine and Biology·Weiwen WuHengyong Yu

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