Open-source Gauss-Newton-based methods for refraction-corrected ultrasound computed tomography

Rehman Ali; Scott Hsieh; Jeremy Dahl

doi:10.1117/12.2511319

15 March 2019 Open-source Gauss-Newton-based methods for refraction-corrected ultrasound computed tomography

Rehman Ali, Scott Hsieh, Jeremy Dahl

Proceedings Volume 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography; 1095508 (2019) https://doi.org/10.1117/12.2511319
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

This work presents refraction-corrected sound speed reconstruction techniques for transmission-based ultrasound computed tomography using a circular transducer array. Pulse travel times between element pairs can be calculated from slowness (the reciprocal of sound speed) using the eikonal equation. Slowness reconstruction is posed as a nonlinear least squares problem where the objective is to minimize the error between measured and forward-modeled pulse travel times. The Gauss-Newton method is used to convert this problem into a sequence of linear least-squares problems, each of which can be efficiently solved using conjugate gradients. However, the sparsity of ray-pixel intersection leads to ill-conditioned linear systems and hinders stable convergence of the reconstruction. This work considers three approaches for resolving the ill-conditioning in this sequence of linear inverse problems: 1) Laplacian regularization, 2) Bayesian formulation, and 3) resolution-filling gradients. The goal of this work is to provide an open-source example and implementation of the algorithms used to perform sound speed reconstruction, which is currently being maintained on Github: https://github.com/ rehmanali1994/refractionCorrectedUSCT.github.io

Conference Presentation

Citation Download Citation

Rehman Ali, Scott Hsieh, and Jeremy Dahl "Open-source Gauss-Newton-based methods for refraction-corrected ultrasound computed tomography", Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 1095508 (15 March 2019); https://doi.org/10.1117/12.2511319

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