Low-rank matrix estimation-based spatio-temporal image reconstruction for dynamic photoacoustic computed tomography
Wang, Kun and Xia, Jun and Li, Changhui and Wang, Lihong V. and Anastasio, Mark A. (2014) Low-rank matrix estimation-based spatio-temporal image reconstruction for dynamic photoacoustic computed tomography. In: Photons Plus Ultrasound: Imaging and Sensing 2014. Proceedings of SPIE. No.8943. Society of Photo-Optical Instrumentation Engineers , Bellingham, WA, Art. No. 89432I. ISBN 9780819498564. https://resolver.caltech.edu/CaltechAUTHORS:20180912-104942705
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.
https://doi.org/10.1117/12.2041850 DOI Article
Wang, Lihong V. 0000-0001-9783-4383
Anastasio, Mark A. 0000-0002-3192-4172
© 2014 Society of Photo-Optical Instrumentation Engineers. This work was supported in part by NIH awards EB016963, EB010049, and CA167446. K. W. would also like to thank Dr. Robert W. Schoonover for useful discussions.
NIH EB016963
NIH CA167446
photoacoustic computed tomography; optoacoustic tomography; dynamic imaging; low-rank matrix estimation
Series Name:
Proceedings of SPIE
Kun Wang, Kun Wang, Jun Xia, Jun Xia, Changhui Li, Changhui Li, Lihong V. Wang, Lihong V. Wang, Mark A. Anastasio, 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); doi: 10.1117/12.2041850; https://doi.org/10.1117/12.2041850