Differentiating between benign and malignant breast tumors is the main purpose of this dissertation. Currently, biopsy is the most practical and reliable way. However, two disadvantages are associated with this method. First, as biopsy is an invasive method, pain and discomfort that patients experience are unavoidable. Second, biopsy is based on small samples that are taken locally; thus it is not able to produce a reliable map of a large area of tissue. Nearly 70% of suspicious cases that are referred for breast biopsy turn out to be benign, not malignant, which means it was unnecessary for those patients to undergo this invasive procedure with all the aforementioned side effects. Technological advances in imaging are prime candidates to replace biopsy with a noninvasive, affordable, more accessible procedure with no side effects. Ultrasound shares all these characteristics. In addition to these benefits, ultrasound-based methods can assess the local mechanical properties of tissue, making this modality a promising tool for differentiating between malignant and benign cases in breast patients. My Ph.D. dissertation focused on developing a device combined with ultrasound to discriminate between benign and malignant breast tumors according to their mechanical properties. Our patient study in the Mayo Clinic has confirmed the effectiveness of this device in discriminating between benign and malignant cases. Both the hardware and software of this device were designed and developed at the Mayo Clinic ultrasound lab. In this dissertation the whole process of designing and using this device will be explained through five Chapters.
University of Minnesota Ph.D. dissertation. 2016. Major: Biomedical Informatics and Computational Biology. Advisors: Mostafa Fatemi, Cluadia Neuhauser. 1 computer file (PDF); 106 pages.
Ultrasound-Based Estimation Of Soft Tissue Mechanical Properties In Sub-Hertz Frequency Range.
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