Limited-view multi-source quantitative photoacoustic tomography

被引:35
|
作者
Gao, Hao [1 ,2 ]
Feng, Jing [1 ]
Song, Liang [3 ,4 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai 200030, Peoples R China
[2] Shanghai Jiao Tong Univ, Dept Math, Shanghai 200030, Peoples R China
[3] Chinese Acad Sci, Res Lab Biomed Opt & Mol Imaging, Shenzhen Inst Adv Technol, Shenzhen, Guangdong, Peoples R China
[4] Beijing Ctr Math & Informat Interdisciplinary Sci, Beijing, Peoples R China
关键词
photoacoustic tomography; numerical inverse problem; numerical optimization; OPTICAL-ABSORPTION COEFFICIENT; BIOLUMINESCENCE TOMOGRAPHY; RECONSTRUCTION; DISTRIBUTIONS; TRANSPORT; CT;
D O I
10.1088/0266-5611/31/6/065004
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
A limited-view scheme is proposed for multi-source quantitative photoacoustic tomography (MS-QPAT), in which the acoustic measurements following each optical illumination are acquired on the partial boundary near the optical source instead of the entire boundary, namely the limited-view MS-QPAT. The proposed limited-view scheme has an improved signal-to-noise ratio when the data are measured near the optical source, and reduces the acquisition time of the imaging system with a single or limited-view acoustic detector. A limited-view MS-QPAT example is to acquire 4 degrees acoustic data following each of 90 optical illuminations, in contrast to 360 degrees acoustic data for each of 90 optical illuminations under the conventional MS-QPAT setting. However, due to the incomplete data, the initial acoustic pressure can no longer be stably reconstructed that serves as an intermediate step in the conventional two-step reconstruction that first reconstructs the initial acoustic pressure and then the optical coefficients. Therefore the direct reconstruction of optical coefficients is considered using the coupled opto-acoustic forward model. The reconstruction algorithm is based on the quasi-Newton method, i.e. limited-memory BFGS with efficient adjoint computations of objective function gradients, and the sparsity-regularized formulation is also considered with tensor framelet sparsity transform and solved by the alternating direction method of multipliers.
引用
收藏
页数:23
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