Low-rank matrix estimation-based spatio-temporal image reconstruction for dynamic photoacoustic computed tomography

Kun Wang; Jun Xia; Changhui Li; Lihong V. Wang; Mark A. Anastasio

doi:10.1117/12.2041850

3 March 2014 Low-rank matrix estimation-based spatio-temporal image reconstruction for dynamic photoacoustic computed tomography

Kun Wang, Jun Xia, Changhui Li, Lihong V. Wang, Mark A. Anastasio

Author Affiliations +

Proceedings Volume 8943, Photons Plus Ultrasound: Imaging and Sensing 2014; 89432I (2014) https://doi.org/10.1117/12.2041850
Event: SPIE BiOS, 2014, San Francisco, California, United States

Abstract

In order to monitor dynamic physiological events in near-real time, a variety of photoacoustic computed tomography (PACT) systems have been developed that can rapidly acquire data. Previously reported studies of dynamic PACT have employed conventional static methods to reconstruct a temporally ordered sequence of images on a frame-by-frame basis. Frame-by-frame image reconstruction (FBFIR) methods fail to exploit correlations between data frames and are known to be statistically and computationally suboptimal. In this study, a low-rank matrix estimation-based spatio-temporal image reconstruction (LRME-STIR) method is investigated for dynamic PACT applications. The LRME-STIR method is based on the observation that, in many PACT applications, the number of frames is much greater than the rank of the ideal noiseless data matrix. Using computer-simulated photoacoustic data, the performance of the LRME-STIR method is compared with that of conventional FBFIR method. The results demonstrate that LRME-STIR method is not only computationally more efficient but also produces more accurate dynamic PACT images than a conventional FBFIR method.

Citation Download Citation

Kun Wang, Jun Xia, Changhui Li, Lihong V. Wang, and Mark A. Anastasio "Low-rank matrix estimation-based spatio-temporal image reconstruction for dynamic photoacoustic computed tomography", Proc. SPIE 8943, Photons Plus Ultrasound: Imaging and Sensing 2014, 89432I (3 March 2014); https://doi.org/10.1117/12.2041850

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