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

0139968Miller这个项目涉及从漫反射光波数据进行脑功能成像的建模和算法问题。其主要目标是从稀疏采样的散射波场数据中结合从fMRI扫描中收集的辅助数据高效地获得高质量的脑血流动力学过程的空间和时间三维图像图。具体目标是：1)开发利用同时可用MRI和fMRI数据的高空间分辨率以及漫射光波场数据的高时间分辨率的空间和时间反转算法，2)快速解决对漫射光散射问题的物理建模的基本正向问题，以及3)使用来自实验研究的数据来验证算法和效率。通过开发未知物理参数的低维、空间自适应参数化(具体地说，光学散射和吸收系数)，减少了逆问题的不适定性，并集中了自由度，以便将分辨率聚焦到感兴趣的皮质区域。进一步的空间精度将通过基于约束的结合由MRI和fMRI图像提供的解剖信息来实现。为了获得更好的计算效率，建议的工作还将重点放在并行的预条件Krylov子空间方法上，以求解在反演过程的每一次迭代中出现的线性化的子问题。为了摊销时间成像的成本，拟议的研究还将解决如何最佳地利用从先前重建获得的信息。所有的建模和算法工作都将基于实验获得的漫反射光学和MRI数据进行验证和修改。

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.