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Proceedings Paper

Novel deep learning architecture for optical fluence dependent photoacoustic target localization
Author(s): Kerrick Johnstonbaugh; Sumit Agrawal; Deepit Abhishek; Matthew Homewood; Sri Phani Krisna Karri; Sri-Rajasekhar Kothapalli
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Paper Abstract

Photoacoustic imaging shows great promise for clinical environments where real-time position feedback is critical, including the guiding of minimally invasive surgery, drug delivery, stem cell transplantation, and the placement of metal implants such as stents, needles, staples, and brachytherapy seeds. Photoacoustic imaging techniques generate high contrast, label-free images of human vasculature, leveraging the high optical absorption characteristics of hemoglobin to generate measurable longitudinal pressure waves. However, the depth-dependent decrease in optical fluence and lateral resolution affects the visibility of deeper vessels or other absorbing targets. This poses a problem when the precise locations of vessels are critical for the application at hand, such as navigational tasks during minimally invasive surgery. To address this issue, a novel deep neural network was designed, developed, and trained to predict the location of circular chromophore targets in tissue mimicking a strong scattering background, given measurements of photoacoustic signals from a linear array of ultrasound elements. The network was trained on 16,240 samples of simulated sensor data and tested on a separate set of 4,060 samples. Both our training and test sets consisted of optical fluence-dependent photoacoustic signal measurements from point sources at varying locations. Our network was able to predict the location of point sources with a mean axial error of 4.3 μm and a mean lateral error of 5.8 μm.

Paper Details

Date Published: 27 February 2019
PDF: 8 pages
Proc. SPIE 10878, Photons Plus Ultrasound: Imaging and Sensing 2019, 108781L (27 February 2019); doi: 10.1117/12.2511015
Show Author Affiliations
Kerrick Johnstonbaugh, The Pennsylvania State Univ. (United States)
Sumit Agrawal, The Pennsylvania State Univ. (United States)
Deepit Abhishek, The Pennsylvania State Univ. (United States)
Matthew Homewood, The Pennsylvania State Univ. (United States)
Sri Phani Krisna Karri, National Institute of Technology, Arunachal Pradesh (India)
Sri-Rajasekhar Kothapalli, The Pennsylvania State Univ. (United States)
PennState Health Milton S. Hershey Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 10878:
Photons Plus Ultrasound: Imaging and Sensing 2019
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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