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

使用稀疏数据方法对时空动态生物物理过程进行 4D 成像

• 批准号: RGPIN-2017-04293
• 负责人: Beyea, Steven
• 金额: $ 3.35万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710790
• 关键词: 4D; Imaging; Spatially; Temporally; Dynamic

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.

提出的Discovery项目将利用时空数据稀疏性建立新的成像物理技术，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-即高信噪比、对比度和空间分辨率-图像通常需要大量数据，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）的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，这些地图可以准确地模拟随时间快速变化的生物物理测量。虽然在这一领域取得了巨大的进步，但许多高度动态的生物物理过程仍未得到研究，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表征它们。