@inproceedings{12b109528aa8481e86e774681d051c1f,

title = "Open-source Gauss-Newton-based methods for refraction-corrected ultrasound computed tomography",

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.",

keywords = "Bayesian, Gauss-Newton method, Nonlinear inverse problems, Regularization, Resolution-lling",

author = "Rehman Ali and Scott Hsieh and Jeremy Dahl",

note = "Publisher Copyright: {\textcopyright} 2019 SPIE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; Medical Imaging 2019: Ultrasonic Imaging and Tomography ; Conference date: 17-02-2019 Through 18-02-2019",

year = "2019",

doi = "10.1117/12.2511319",

language = "English (US)",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Byram, {Brett C.} and Ruiter, {Nicole V.}",

booktitle = "Medical Imaging 2019",

}