Poster — Thur Eve — 62: Analysis of a Photoacoustic Imaging System by Singular Value Decomposition

Photoacoustic imaging (PAI) is a hybrid imaging modality capable of producing contrast similar to optical imaging techniques but with increased penetration depth and resolution in turbid media by encoding the optical information as acoustic waves. PAI employs a pulsed laser to diffusely irradiate a...

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Bibliographic Details
Published in:Medical Physics 2010-07, Vol.37 (7), p.3899-3899
Main Authors: Roumeliotis, M, Stodilka, RZ, Anastasio, MA, Carson, JJL
Format: Article
Language:English
Subjects:
Acoustic analysis
Image analysis
Image detection systems
Image reconstruction
Image sensors
Materials analysis
Medical image reconstruction
Medical imaging
Photoacoustic imaging
Singular values
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Summary:Photoacoustic imaging (PAI) is a hybrid imaging modality capable of producing contrast similar to optical imaging techniques but with increased penetration depth and resolution in turbid media by encoding the optical information as acoustic waves. PAI employs a pulsed laser to diffusely irradiate a volume of interest making the resulting images inherently three‐dimensional. While PAI is a relatively new field, potential applications include the characterization of tumours surrounded by soft tissue, such as breast tissue, as well as imaging vasculature structure. We have developed a PAI system that utilizes a staring, hemispherical array of detectors combined with parallel data acquisition and an iterative image reconstruction algorithm to produce three‐dimensional photoacoustic images using only a single laser pulse. Since our imaging system collects a limited number of data projections and has a shift‐variant response through object space, our objective was to characterize system performance beyond classic metrics such as sensitivity, resolution and contrast by implementing singular value decomposition. Using a robotically placed photoacoustic point source we experimentally captured the imaging operator over a defined object space. Decomposition of the imaging operator was done via singular value decomposition and provided insight into the capability of the PAI system to reconstruct objects and the inherent sensitivity of the PAI system to those objects. Preliminary reconstruction of simple objects is shown utilizing the calibration data in our reconstruction algorithm.
ISSN:0094-2405
2473-4209
DOI:10.1118/1.3476167