Tissue Characterization Using H-scan Ultrasound Imaging



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Breast cancer is the second leading cause of mortality among women and affects more women than any other type of cancer. Around 43,600 women in the U.S. died in 2021 from breast cancer. Clinical studies have demonstrated that an early neoadjuvant response is a better predictor of the patient’s recurrence-free survival than pathological complete response. Therefore, mammography, ultrasound (US), and magnetic resonance imaging (MRI) have been widely used to determine tumor response by tracking changes in tumor size using guidelines provided by the Response Evaluation Criteria in Solid Tumors (RECIST). However, measurable changes in tumor size may not be detectable until after multiple cycles of chemotherapy. In the interim, high cost and unnecessary patient toxicity may be incurred for therapy regimens. Further, intratumor heterogeneity poses a fundamental treatment challenge because different tumor subregions might have different drug sensitivities. This implies that some therapeutic strategies might not be effective against the whole tumor. Therefore, the use of noninvasive US for quantitative tissue characterization has become an exciting research prospect. Herein the challenge is to find hidden patterns in the US data to reveal more information about tissue function and pathology that cannot be seen in the conventional US images. Circumventing some of the limitations associated with traditional tissue characterization approaches, a new modality has been proposed for the US classification of acoustic scatterers, such as cancer cells. Termed H-scan US imaging, this technique relies on matching a model that describes US image formation to the mathematics of a class of Gaussian-weighted Hermite polynomials. In short, it reveals the local frequency dependence of different sized scatterers in soft tissue. In this dissertation work we demonstrate: (1) application of a novel frequency-dependent attenuation correction technique improves H-scan US imaging sensitivity to subtle changes at tissue depth. (2) propose 3-D H-scan imaging technique to capture data from the entire tumor burden, visualization of any heterogenous tissue patterns, and fundamentally improve any tissue characterization strategy and treatment response determination and (3) propose volumetric H-scan US imaging to visualize breast cancer changes during response to drug treatment including apoptotic activity, which is a hallmark feature of effective anticancer therapy. Our overarching hypothesis is that volumetric H-scan US imaging can detect early response to chemotherapy in breast cancer tumors and provide vital prognostic data on treatment response and tumor progression. Consequently, this would provide a new and safe approach to exploring the tumor response to chemotherapy as early as possible and maximize effective therapy for an individual patient, reduce morbidity, and constrain escalating health care costs associated with overtreatment.



Artificial Intelligence, Physics, Electricity and Magnetism