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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689191
• 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.

拟议的发现计划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。获得高质量的图像通常需要大的信噪比、对比度和空间分辨率。数据量，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相矛盾。最近，利用空间和时间冗余（即，数据采样和图像重建）的新策略。稀疏性）提高了我们获取高质量定量图的能力，这些图可以准确地模拟随时间快速变化的生物物理测量结果。 ******此外，当研究个体而不是平均群体时，这个问题变得更具挑战性，因为获取和分析数据的最佳策略部分是由时空驱动的。该确切数据中的特征至关重要的是，虽然新颖的数据采样技术可以导致高加速因子，但物理学家必须警惕简单地尝试更快，因为“正确”的数据采样和重建方案是最佳连接测量数据的方案。确定正确的策略本身就是一个重大挑战，因此，模拟和与生物学的相关性至关重要。 ******该计划将基础研究连贯地整合到新数据中。采集技术、图像重建技术和分析工具为了改善人体时空动态系统的图像特征，该计划中的所有项目都将从假设生成理论模拟为基础，这些模拟随后将为采集和/或分析经验数据提供信息和指导新技术的开发，并通过验证进行验证。 ******这项工作将应用于看似不同的生理过程（例如动态对比度增强和功能神经成像）和成像技术（例如 MRI 和 MEG）。该计划中的所有研究都有一个共同点，即它需要获取和分析 4D 数据，以便将其与个人感兴趣的生理参数联系起来，这样做将建立在该领域的新颖数学和物理方法的基础上。成像物理和数据压缩，并将直接来自我之前在 NSERC 发现资助中进行的研究。