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

提出的发现者计划将建立利用时空数据稀疏性的新的成像物理技术，并相应地提高我们对特定快速动态生理过程的生物物理理解。获取高质量的图像--即高信噪比、对比度和空间分辨率--通常需要大量数据，因此似乎与获得准确表征动态系统所需的高时间分辨率数据不一致。最近，利用空间和时间冗余(即数据稀疏性)进行数据采样和图像重建的新策略提高了我们获得高质量定量地图的能力，这些地图准确地模拟了随时间快速变化的生物物理测量。虽然在这一领域已经取得了长足的进步，但许多高度动态的生物物理过程仍然没有得到研究，因为目前的工具不能提供足够的空间和时间分辨率来准确地描述它们。

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.