Date of Award
5-21-2020
Publication Type
Doctoral Thesis
Degree Name
Ph.D.
Department
Electrical and Computer Engineering
Keywords
deconvolution, digital signal processing, ultrasound
Supervisor
Roman Gr Maev
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
The assessment of soft and hard tissues is critical when selecting appropriate protocols for restorative and regenerative therapy in the field of dental surgery. The chosen treatment methodology will have significant ramifications on healing time, success rate and overall long-time oral health. Currently used diagnostic methods are limited to visual and invasive assessments; they are often user-dependent, inaccurate and result in misinterpretation. As such, the clinical need has been identified for objective tissue characterization, and the proposed novel ultrasound-based approach was designed to address the identified need. The device prototype consists of a miniaturized probe with a specifically designed ultrasonic transducer, electronics responsible for signal generation and acquisition, as well as an optimized signal processing algorithm required for data analysis. An algorithm where signals are being processed and features extracted in real-time has been implemented and studied. An in-depth algorithm performance study has been presented on synthetic signals. Further, in-vitro laboratory experiments were performed using the developed device with the algorithm implemented in software on animal-based samples. Results validated the capabilities of the new system to reproduce gingival assessment rapidly and effectively. The developed device has met clinical usability requirements for effectiveness and performance.
Recommended Citation
Slak, Bartosz, "Development, Optimization and Clinical Evaluation Of Algorithms For Ultrasound Data Analysis Used In Selected Medical Applications." (2020). Electronic Theses and Dissertations. 8339.
https://scholar.uwindsor.ca/etd/8339