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; 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物理过程的生物物理理解。获得高质量的高品质 - 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临时分辨率数据似乎是一致的。最近，利用空间和临时冗余（即数据稀疏性）的数据采样和图像重建的新策略都提高了我们获得高质量定量图的能力，这些图能准确地模拟生物物理测量值，从而快速及时改变。尽管在这一领域取得了长足的进步，但许多高度动态的生物物理过程仍然没有研究，因为当前工具没有提供足够的空间和临时分辨率来准确地表征它们。 此外，在研究个人而不是平均人群时，这个问题变得更加具有挑战性，因为获得和分析数据的最佳策略部分是由该精确数据集中的时空特征驱动的。至关重要的是，尽管新颖的数据采样技术可以导致高速度因素，但物理学家必须警惕简单地尝试更快的速度，因为它们可以更快。是将测量数据与感兴趣的实际生物物理特性联系起来的。确定正确的策略本身可能是一个重大挑战。因此，模拟和与生物学的相关性至关重要。 该程序一致地将基础研究汇总到新的数据采集技术，图像重建技术和分析工具中，以改善人体中空间临时动态系统的图像表征。该程序中的所有项目都将源于假设产生理论模拟，随后将为并指导开发新技术以获取和/或分析经验数据，并通过与基本生物物理特性相关的验证。 这项工作将应用于看似多样的生理过程（例如，动态对比度增强和功能性神经成像）和成像技术（例如MRI和MEG）。但是，该程序中的所有研究都有一个共同的线程，即它需要4D数据，必须获取和分析，以便可以将其连接到个人感兴趣的物理参数。这样做将基于成像物理和数据压缩领域的新数学和物理方法，并将直接从我以前的Nserc Discovery Grant中进行的研究直接流动。