Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer

Alexis Cheng; Younsu Kim; Emran M. A. Anas; Arman Rahmim; Emad M. Boctor; Reza Seifabadi; Bradford J. Wood

doi:10.1117/12.2512533

15 March 2019 Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer

Alexis Cheng, Younsu Kim, Emran M. A. Anas, Arman Rahmim, Emad M. Boctor, Reza Seifabadi, Bradford J. Wood

Proceedings Volume 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography; 1095516 (2019) https://doi.org/10.1117/12.2512533
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Alexis Cheng, Younsu Kim, Emran M. A. Anas, Arman Rahmim, Emad M. Boctor, Reza Seifabadi, and Bradford J. Wood "Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer", Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 1095516 (15 March 2019); https://doi.org/10.1117/12.2512533

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available