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Proceedings Paper

K-means clustering for high-resolution, realistic acoustic maps
Author(s): Kevin Looby; Christopher Sandino; Tao Zhang; Shreyas Vasanawala; Jeremy Dahl
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Paper Abstract

In this work, we describe a method for converting fat-water-separated magnetic resonance imaging (MRI) volumes to acoustic maps for ultrasound simulations. An acoustic map is a mapping of acoustic imaging parameters such as speed of sound and density to grid points in the ultrasound simulations. Tissues are segmented into five primary classes of tissue in the human abdominal wall (skin, fat, muscle, connective tissue, and non-tissue). This segmentation is achieved using an unsupervised machine learning algorithm, called soft k-means clustering, on a multi-scale feature representation of the MRI volumes. We describe an automated method for utilizing soft k-means weights to produce an acoustic map that achieves approximately 90% agreement with manual segmentation. Two-dimensional (2D) and three-dimensional (3D) nonlinear ultrasound simulations are conducted, demonstrating the utility of realistic 3D maps over previously-available 2D acoustic maps.

Paper Details

Date Published: 6 March 2018
PDF: 8 pages
Proc. SPIE 10580, Medical Imaging 2018: Ultrasonic Imaging and Tomography, 1058014 (6 March 2018); doi: 10.1117/12.2293990
Show Author Affiliations
Kevin Looby, Stanford Univ. (United States)
Christopher Sandino, Stanford Univ. (United States)
Tao Zhang, Stanford Univ. (United States)
Shreyas Vasanawala, Stanford Univ. (United States)
Jeremy Dahl, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 10580:
Medical Imaging 2018: Ultrasonic Imaging and Tomography
Neb Duric; Brett C. Byram, Editor(s)

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