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Proceedings Paper

Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer
Author(s): Alexis Cheng; Younsu Kim; Emran M. A. Anas; Arman Rahmim; Emad M. Boctor; Reza Seifabadi; Bradford J. Wood
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Paper Abstract

Problem: The gold standard for prostate cancer diagnosis is B-mode transrectal ultrasound-guided systematic core needle biopsy. However, cancer is indistinguishable under ultrasound and thus additional costly imaging methods are necessary to perform targeted biopsies. Speed of sound is a potential biomarker for prostate cancer and has the potential to be measured using ultrasound tomography. Given the physical constraints of the prostate’s anatomy, this work explores a simulation study using deep learning for limited-angle ultrasound tomography to reconstruct speed of sound. Methods: A deep learning-based image reconstruction framework is used to address the limited-angle ultrasound tomography problem. The training data is generated using the k-wave acoustic simulation package. The general network structure is composed of a series of dense fully-connected layers followed by an encoder and a decoder network. The basic idea behind this neural network is to encode a time of flight map into a lower dimension representation that can then be decoded into a speed of sound image. Results and Conclusions: We show that limited-angle UST is feasible in simulation using an auto-encoder-like DL framework. There was a mean absolute error of 7.5 ± 8.1 m/s with a maximum absolute error of 139.3 m/s. Future validation on experimental data will further assess their ability in improving limited-angle ultrasound tomography.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 1095516 (15 March 2019); doi: 10.1117/12.2512533
Show Author Affiliations
Alexis Cheng, National Institutes of Health (United States)
Younsu Kim, Johns Hopkins Univ. (United States)
Emran M. A. Anas, Johns Hopkins Univ. (United States)
Arman Rahmim, Johns Hopkins Univ. (United States)
Univ. of British Columbia (Canada)
Emad M. Boctor, Johns Hopkins Univ. (United States)
Reza Seifabadi, National Institutes of Health (United States)
Bradford J. Wood, National Institutes of Health (United States)

Published in SPIE Proceedings Vol. 10955:
Medical Imaging 2019: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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