Technology development for multiparametric and multimodality image guidance

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The proposed research targets developing technological tools based on "signal and image processing" and "machine learning" to advance MRI and ultrasound-based tissue typing for cancer detection, in a multimodal and data-driven framework. MRI modalities reveal physiologic features characterizing perfusion and diffusion, while ultrasound-based parameters are related to physical properties such as elasticity, viscosity, power law, elastic anisotropy, heat dissipation and speed of sound. Together, MR and ultrasound could provide a profile of physics and physiology of the tissue. The research program has two major engineering components. One component deals with the problem of accurate registration for combining MRI and ultrasound. Current methods of MR-ultrasound registration are surface-based and subjective due to dependence on contouring of an anatomical organ, for example prostate, from MR and ultrasound images. I plan to develop a new image similarity measure based on maximal information coefficient to enable intensity-based registration. The second component builds a data-driven method to enhance the process of tissue typing in MRI, and in the combined MR-ultrasound radiologic profile. This approach relies on learning the cancer stage from data. It removes the necessity for physical or physiological modeling and derivation of analytic solutions to describe cancer progress. The method will be applied both in calculation of tissue typing features from dynamic contrast enhanced MRI, and in building a computer-based diagnosis system. Many clinical applications exist for the proposed technics. In prostate cancer, a non-invasive image-based method for prognosis is desperately needed. In breast cancer, an image-based solution for subtyping of the disease can enable patient-specific therapy. Two ongoing graduate student positions and one undergraduate summer co-op position will be created in my research program. One graduate position will focus on image-based computer-aided diagnosis with emphasis on statistical machine learning, signal and image processing. The second position will focus on computer-aided interventions with focus on image registration.