Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 57-61). / We propose and validate a non-invasive method to diagnose Non-Alcoholic Fatty Liver Disease (NAFLD). The proposed method is based on two fundamental concepts: 1) the speed of sound in a fatty liver is lower than that in a healthy liver and 2) the quality of an ultrasound image is maximized when the beamforming speed of sound used in image formation matches the speed in the medium under examination. The proposed method uses image brightness and sharpness as quantitative image-quality metrics to predict the true sound speed and capture the effects of fat infiltration, while accounting for the transmission through subcutaneous fat. Validation using nonlinear acoustic simulations indicated the proposed method's ability to predict the speed of sound within a medium under examination with little sensitivity to the transducer's frequency (errors less than 2%). Additionally, ex vivo testing on sheep liver, mice livers, and tissue-mimicking phantoms indicated the method's ability to predict the true speed of sound with errors less than 0.5% (despite the presence of subcutaneous fat) and its ability to quantify the relationship between fat content and speed of sound. Additionally, this work starts to create a framework which allows for the determination of the spatial distribution of the longitudinal speed of sound, thereby providing a promising method for diagnosing NAFLD over time. / by Alex Benjamin. / S.M.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/111506 |
| Date | January 2017 |
| Creators | Benjamin, Alex (Alex Robert) |
| Contributors | Brian W. Anthony., Massachusetts Institute of Technology. Computation for Design and Optimization Program., Massachusetts Institute of Technology. Computation for Design and Optimization Program. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 61 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
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