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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=632960
• 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.

拟议的发现计划将建立新的成像物理技术，利用时空数据稀疏，并相应地提高我们的生物物理学的理解特定的快速动态生理过程。获得高质量-即，高信噪比、对比度和空间分辨率-图像通常需要大量数据，因此似乎与获得精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，提高了我们获取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。虽然在这一领域取得了很大进展，但许多高度动态的生物物理过程仍未得到研究，因为目前的工具没有提供足够的空间和时间分辨率来准确地描述这些过程。