2026-09-21T15:14:59Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/105027 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Oelze, Michael L. Oelze, Michael L. Do, Minh N. Boppart, Stephen A. Liang, Zhi-Pei Nguyen, Trong Ngoc 2019-08-23T20:35:54Z 2019-08-23T20:35:54Z 2021-08-24T09:15:16Z 2019-04-16 2019-05 Current spectral-based quantitative ultrasound (QUS) techniques rely on a reference or a calibration signal to obtain system-independent ultrasonic scattering parameters. Adoption of QUS clinically has been hindered by (1) the need to limit clinical ultrasonic scanner settings to account for a finite number of calibration signals or (2) the need to acquire calibration signals immediately before or after a setting is changed on an ultrasonic scanner, which can interrupt the workflow in the busy clinical environment. An additional factor that hinders the effectiveness of QUS clinically is the presence of layers over the tissue regions to be interrogated. Layer effects are not accounted for when using a reference phantom, reducing the technique's reliability. The dissertation presents experimental and computational approaches to improve the accuracy of QUS estimates in the presence of intervening layers using an in situ calibration target and the feasibility of reference-free quantitative ultrasound using a convolutional neural network. The sensitivity of CNN in a reference-free environment is assessed and experimentally validated. A model-free approach through the use of principal component analysis is proposed as a general method for tissue characterization using QUS and compared to a model-based approach. To address the effects of intervening layers on QUS accuracy and precision, an in situ calibration approach is proposed and experimentally verified to improve the QUS estimates. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01 The student, Trong Nguyen, accepted the attached license on 2019-04-15 at 14:20. The student, Trong Nguyen, submitted this Dissertation for approval on 2019-04-15 at 14:29. This Dissertation was approved for publication on 2019-04-16 at 08:25. DSpace SAF Submission Ingestion Package generated from Vireo submission #13632 on 2019-08-22 at 15:06:26 Made available in DSpace on 2019-08-23T20:35:54Z (GMT). No. of bitstreams: 3 NGUYEN-DISSERTATION-2019.pdf: 8162664 bytes, checksum: 80764bbe1e22777ac1cdf7a4dfe024dc (MD5) LICENSE.txt: 4209 bytes, checksum: c7a922575e89450a2b4643027334c4f7 (MD5) PROQUEST_LICENSE.txt: 4555 bytes, checksum: 236b23b107844e9fe60f7eaf3b50d95d (MD5) Previous issue date: 2019-04-16 Embargo set by: Seth Robbins for item 112146 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 112146 on 2021-08-24T09:15:16Z. application/pdf http://hdl.handle.net/2142/105027 en Copyright 2019 Trong Nguyen Quantitative ultrasound, reference-free, fatty liver, in situ calibration Toward model-free and reference-free quantitative ultrasound text text Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Dissertation Ph.D.