Multicomponent Modeling of High-Dimensional Multiparametric MRI Data

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PROJECT SUMMARY Multicomponent Modeling of High-Dimensional Multiparametric MRI Data MRI generates images with millimeter-scale spatial resolution, while many important biological features occur at much smaller (microscopic) scales. Over the past several decades, MRI practitioners have used the information derived from biophysical parameters (like relaxation and diffusion) to indirectly probe microscopic tissue compartments using millimeter-scale data. While these approaches have been somewhat successful, an innovative new paradigm has emerged in recent years that leverages multiparametric MRI data (e.g., using relaxation and diffusion jointly in a higher-dimensional experiment) to probe tissue microstructure with an unprecedented level of detail. Although such multicomponent multiparametric methods can be quite powerful, substantial improvements in image acquisition and analysis methods and improvements in accessibility are needed for these approaches to be used routinely for practical applications by the broader community. The proposed project involves the development of novel analysis methods to identify and separate multiple microstructural tissue compartments from MRI data using advanced constrained estimation techniques, the development of a novel end-to-end image preprocessing pipeline (including steps like registration, distortion correction, denoising, etc.) that is especially designed for multiparametric acquisitions, the development of novel tools to evaluate estimation quality and optimize multiparametric acquisition protocols, and the application of this approach to ex vivo mouse brains and spinal cords to provide new insights into sex differences and the role of oxidative stress in a mouse model of multiple sclerosis. Further, the new methods we develop will be integrated into the open-source BrainSuite Diffusion Pipeline software package, which will, for the first time, provide the broader imaging community with easy access to these powerful approaches.