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Proceedings Paper

Quantification of tissue texture with photoacoustic spectrum analysis
Author(s): Xueding Wang; Guan Xu; Zhuo-Xian Meng; Jiandie Lin; Paul Carson
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Paper Abstract

Photoacoustic (PA) imaging is an emerging technology that could map the functional contrasts in deep biological tissues in high resolution by “listening” to the laser induced thermoelastic waves. Almost all of the current studies in PA imaging are focused on the intensity of the PA signals as an indication of the optical absorbance of the biological tissues. Our group has for the first time demonstrated that the frequency domain power distribution of the broadband PA signals encode the texture information within the regions-of-interest (ROI). Following the similar method of ultrasound spectral analysis (USSA), photoacoustic spectrum analysis (PASA) could evaluate the relative concentrations and, more importantly, the dimensions of microstructures of the optically absorbing materials in biological tissues, including lipid, collagen, water and hemoglobin. By providing valuable insights into tissue pathology, PASA should benefit basic research and clinical management of many diseases, and may help achieve eventual “noninvasive biopsy”. In this work, taking advantage of the optical absorption contrasts contributed by lipid and hemoglobin at 1200-nm and 532-nm wavelengths respectively, we investigated the capability of PASA in identifying histological changes corresponding to fat accumulation livers through the study on ex vivo and in situ mouse models. The PA signals from the mouse livers were acquired using our PA and US dual-modality imaging system, and analyzed in the frequency domain. After quantifying the power spectrum by fitting it to a first order model, three spectral parameters, including the intercept, the midband fit and the slope, were extracted and used to differentiate fatty livers from normal livers. The comparison between the PASA parameters from the normal and the fatty livers supports our hypotheses that PASA can quantitatively identify the microstructure changes in liver tissues for differentiating normal and fatty livers.

Paper Details

Date Published: 8 May 2014
PDF: 7 pages
Proc. SPIE 9129, Biophotonics: Photonic Solutions for Better Health Care IV, 91291L (8 May 2014); doi: 10.1117/12.2051188
Show Author Affiliations
Xueding Wang, Univ. of Michigan Medical School (United States)
Guan Xu, Univ. of Michigan Medical School (United States)
Zhuo-Xian Meng, Univ. of Michigan Medical School (United States)
Jiandie Lin, Univ. of Michigan Medical School (United States)
Paul Carson, Univ. of Michigan Medical School (United States)

Published in SPIE Proceedings Vol. 9129:
Biophotonics: Photonic Solutions for Better Health Care IV
Jürgen Popp; Valery V. Tuchin; Dennis L. Matthews; Francesco Saverio Pavone; Paul Garside, Editor(s)

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