4D Imaging of Spatially and Temporally Dynamic Biophysical Processes using Sparse Data Methods

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The proposed Discovery program will establish new imaging physics technologies utilizing spatiotemporal data sparsity, and correspondingly improve our biophysical understanding of specific rapidly dynamic physiological processes. Acquiring high quality - i.e., high signal-to-noise, contrast and spatial resolution - images typically requires large amounts of data, and is therefore seemingly at odds with obtaining the high temporal resolution data needed to accurately characterize dynamic systems. Recently, novel strategies for both data sampling and image reconstruction that take advantage of spatial and temporal redundancies (i.e. data sparsity), have improved our ability to acquire high quality quantitative maps that accurately model biophysical measurements that change quickly in time. While great strides have been made in this area, many highly dynamic biophysical processes remain unstudied, as the current tools do not provide sufficient spatial and temporal resolution to accurately characterize them. Furthermore, this problem becomes even more challenging when studying individuals, rather than averaged populations, as the optimal strategy to both acquire and analyze the data is in part driven by the spatiotemporal features within that exact data set. Critically, while novel data sampling techniques can lead to high acceleration factors, physicists must be wary of simply trying to go faster because they can go faster. The “correct” data sampling and reconstruction scheme is the one that optimally connects the measured data with the actual biophysical property of interest. Determining what the correct strategy is can be a significant challenge in its own right. Hence, simulation and correlation to biology are critical. This program coherently brings together basic research into new data acquisition technologies, image reconstruction techniques, and analysis tools for improving image characterization of spatiotemporally dynamic systems in the human body. All projects within this program will build from hypothesis generating theoretical simulations that will subsequently inform and guide the development of new technologies for the acquisition and/or analysis of empirical data, with validation through correlation to the underlying biophysical properties. This work will be applied to a seemingly diverse spectrum of physiologic processes (e.g., Dynamic Contrast Enhancement and Functional Neuroimaging) and imaging technologies (e.g., MRI and MEG). However all research in this program has the common thread that it requires 4D data that must be acquired and analyzed such that it can be connected to a physiological parameter of interest in an individual. Doing so will build off of novel mathematical and physics approaches from the fields of imaging physics and data compression, and will flow directly from the research performed in my previous NSERC Discovery grant.