Novel Ultrasound Imaging Methods for the Characterization of Tissue Microvascular Networks



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The structure and function of tissue microcirculation are critical in most major disease developments and in the patient-specific treatment response. Adequate quantitative characterization of tissue microvasculature may therefore assist to better understand various types of disorders, to develop new therapeutic strategies, and to monitor early response to therapy. Currently, the greatest challenge is to accurately and precisely quantify the microvascular properties in a noninvasive manner. To address this challenge, this dissertation proposes characterizing the tissue microvascular network morphology using contrast-enhanced ultrasound (CEUS). We hypothesize that the development of morphological image analysis methods using CEUS images will improve in vivo quantitative analysis of tissue microvascular networks for early treatment monitoring. This work demonstrates (1) the potential of CEUS-derived morphological features as a predictor of anti-cancer therapy response, (2) the development and use of multiparametric CEUS image analysis, and (3) the implementation of three-dimensional superresolution US visualization and quantification using advanced image analysis methods.



Machine learning, Diagnostic imaging, Contrast-enhanced ultrasound, Cancer, Image analysis