Characterization of Sparse-Array detection Photoacoustic Tomography using the Singular Value Decomposition

被引：1

作者：

Chaudhary, G. ^{[1
]}

Roumeliotis, M. ^{[3
,4
]}

Ephrat, P. ^{[3
,4
]}

Stodilka, R. ^{[3
,4
]}

Carson, J. J. L. ^{[3
,4
]}

Anastasio, M. A. ^{[1
,2
]}

机构：

[1] IIT, Med Imaging Res Ctr, Dept Elect & Comp Engn, Chicago, IL 60616 USA

[2] IIT, Med Imaging Res Ctr, Dept Biomed Engn, Chicago, IL 60616 USA

[3] St Josephs Hlth Care, Lawson Hlth Res Inst, Imaging Program, London, ON N6A 4V2, Canada

[4] Univ West Ontario, Dept Med Biophys, London, ON N6A 5C1, Canada

来源：

PHOTONS PLUS ULTRASOUND: IMAGING AND SENSING 2010 | 2010年 / 7564卷

关键词：

Photoacoustic tomography; singular value decomposition; LANCZOS algorithm; pseudo-inverse solution;

D O I：

10.1117/12.842663

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

A photoacoustic tomography (PAT) method that employs a sparse two-dimentional (2D) array of detector elements has recently been employed to reconstruct images of simple objects from highly incomplete measurement data. However, there remains an important need to understand what type of object features can be reliably reconstructed from such a system. In this work, we numerically compute the singular value decomposition (SVD) of different system matrices that are relevant to implementations of sparse-array PAT. For a given number and arrangement of measurement transducers, this will reveal the type of object features that can reliably be reconstructed as well as those that are invisible to the imaging system.

引用

页数：6

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