The overall goal of this project is to develop the novel technology of passive cavitation imaging for guidance and control of thermal ablation. This investigation is based on the hypothesis that cavitation, or microbubble activity caused by therapeutic ultrasound beams, can be passively imaged by ultrasound arrays, providing specific information about spatially-dependent sonication intensity, temperature, and tissue viability in vivo. In passive cavitation imaging, ultrasound-induced microbubble activity within tissue is mapped noninvasively from locally occurring acoustic emissions caused by cavitation and boiling. These acoustic emissions are detected passively by an ultrasound imaging array, filtered, and synthetically focused to form images depicting locations and strengths of stable cavitation, inertial cavitation, and tissue boiling throughout the imaged region. Preliminary data indicates that these passive cavitation images can accurately depict spatial profiles of therapeutic ultrasound beams in situ, resolve individual sources of cavitation-induced acoustic emissions, and be used to predict local tissue temperature elevations causing thermal coagulation necrosis. This cavitation imaging technology will provide previously unavailable guidance and control for ultrasound ablation, greatly enhancing this modality for noninvasive and minimally invasive cancer treatment. The proposed research will begin with optimization of methods for passive cavitation imaging, including filtering and beamforming of acoustic emission signals to maximize image resolution, sensitivity, and quantitative accuracy. Optimized passive cavitation imaging methods will be used to map localized stable and inertial cavitation in saline solution and ex vivo liver tissue, measuring cavitation thresholds as functions of temperature and sonication amplitude. Passive cavitation images will be acquired during therapeutic ultrasound exposures both on bovine liver in vitro and porcine liver in vivo. Measured correlations between passive cavitation images, tissue temperature, and tissue histologic changes during ultrasound ablation, with complementary physical modeling and statistical analysis, will guide development of control strategies for ultrasound ablation. Multivariate statistical models based on experimental data will predict local tissue temperature and coagulation based on imaged acoustic emissions in the three bands considered, allowing specification of treatment progress indicators and end points for ultrasound ablation. Feasibility of this approach for closed-loop ultrasound ablation control will be assessed, based on measured accuracy of these new models for prediction of local tissue ablation. Successful completion of this project will show feasibility for future development of a clinical system providing guidance and control of ultrasound ablation by passive cavitation imaging. These guidance and control methods will provide greatly improved efficacy and safety for ultrasound ablation of liver cancer and soft tissue tumors as well as other clinical applications.
Liver cancer, both primary and metastatic, is a major public health problem, accounting for the largest cancer- related mortality in the world, with only a small fraction of patients eligible for curative resection or transplantation. Minimally invasive and noninvasive ablation methods provide an important alternative but have significant problems with incomplete treatment, tumor recurrence, and complications caused by imprecise treatment. Ultrasound ablation is a particularly promising approach, potentially offering more precise and reliable treatment, but will not realize its full potential without effective feedback, control and image guidance. Our passive cavitation imaging technology has the potential to greatly improve guidance and control of minimally-invasive and noninvasive ultrasound tumor ablation, providing more precise, selective, predictable, and consistent ablation of liver cancer as well as soft tissue tumors and other clinically important targets, and thus fewer complications, reduced tumor recurrence, and improved patient outcomes.
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