Tensor-based dictionary learning for dynamic tomographic reconstruction

• Published in: Physics in medicine & biology Vol. 60; no. 7; pp. 2803 - 2818
• Main Authors: Tan, Shengqi, Zhang, Yanbo, Wang, Ge, Mou, Xuanqin, Cao, Guohua, Wu, Zhifang, Yu, Hengyong
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 07.04.2015
• Subjects: Algorithms; Animals; computed tomography; dictionary learning; Heart - diagnostic imaging; Least-Squares Analysis; Mice; Models, Statistical; Pattern Recognition, Automated; Radiographic Image Interpretation, Computer-Assisted - methods; Sheep; Signal Processing, Computer-Assisted; sparse representation; tensor decomposition; Tomography, X-Ray Computed - methods
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/0031-9155/60/7/2803

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.