Inverse Scattering Models and Algorithms for Functional Brain Imaging with Diffuse Optical Wavefields

漫射光波场功能性脑成像的逆散射模型和算法

• 批准号: 0139968
• 负责人: Eric Miller
• 金额: $ 29.62万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0139968&HistoricalAwards=false
• 关键词: Inverse; Scattering; Models; Algorithms; Functional

0139968MillerThis project is concerned with modeling and algorithmic issues surrounding functional brain imaging from diffuse optical wave data. The primary objective is to efficiently obtain high-quality spatial and temporal three-dimensional image maps of hemodynamic processes in the brain from sparsely sampled scattered wavefield data in conjunction with auxiliary data collected from fMRI scans. The specific aims are: 1) development of spatial and temporal inversion algorithms that exploit the high spatial resolution of simultaneously available MRI and fMRI data as well as the high temporal resolution of diffuse optical wavefield data, 2) fast solution of the underlying forward problem that models that physics of the diffuse optical scattering problem and 3) validation of the algorithms and efficiency using data from experimental studies.One level of efficiency and accuracy will be achieved by developing a low-dimensional, spatially adaptive parameterization of the unknown physical parameters (specifically, optical scattering and absorption coefficients) that reduces the ill-posedness of the inverse problem and concentrates degrees of freedom so as to focus resolution over cortical regions of interest. Further spatial accuracy will be achieved by constraint-based incorporation of anatomical information provided by MRI and fMRI images. To attain better computational efficiency, the proposed work will also focus on parallel, preconditioned Krylov subspace methods for solving the linearized subproblems that arise at every iteration of the inversion process. In the interest of amortizing the cost in temporal imaging, the proposed research will also address how to optimally exploit information obtained from previous reconstructions. All modeling and algorithmic work will be validated and modified based on experimentally obtained diffuse optical and MRI data.

0139968 MillerThis项目与弥漫性光波数据有关功能性脑成像的建模和算法问题有关。主要目的是有效地从稀疏采样的散射波场数据以及从fMRI扫描中收集的辅助数据，有效地获得大脑中血液动力学过程的高质量空间和时间三维图像图。 具体目的是：1）开发空间和时间反转算法，这些算法可以利用同时可用的MRI和fMRI数据的高空间分辨率，以及高度分辨率的高度分辨率，2）使用实验级别的散射级别和3）实验级别的远程问题的快速解决方案的差异解决方案和3）实验级别的效率和3）实现了3）。通过开发未知物理参数（具体是光学散射和吸收系数）的低维，空间适应性参数化来实现效率和准确性，从而降低了反相反问题的不良性和浓度的自由度，以便将自由度的分辨率集中在利益的皮质区域上。通过基于约束MRI和FMRI图像提供的解剖信息的结合，将实现进一步的空间精度。为了提高计算效率，提议的工作还将集中在平行的，预处理的Krylov子空间方法上，以求解在反转过程的每一个迭代中都会出现的线性化子问题。为了摊销时间成像中的成本，拟议的研究还将介绍如何最佳利用从先前的重建中获得的信息。所有建模和算法工作将根据实验获得的弥漫性光学和MRI数据进行验证和修改。