A novel multi-modal, multi-scale imaging pipeline for the validation of diffusion MRI of the brain

项目摘要 在这个项目中，我们建议验证和表征来自扩散张量图像的fiBER方向估计。 ING(DTI)通过优化用于整个小鼠大脑的多模式、多尺度成像流水线。DTI 是一种用于在宏观上非侵入性地报告神经组织的3D微观结构特性的强大工具 量表，并在了解和诊断一些神经系统疾病方面发挥了重要作用 流程。可以对DTI数据的现代采集进行处理，以生成3D扩散PROfiLE 每个体素的取向扩散函数(ODF)。用ODF来推断局部轴突fi的方向。 恭喜你。以前验证这些取向估计的努力主要依赖于连续光学组织学 作为地面实况数据集。基于组织学的管道涉及到物理切片的劳动密集型任务 将组织切成薄片，导致样品的物理破坏和各向异性分辨率。这些限制 潜在地混淆了3D方向估计的准确性，使空间配准 地面实况和DTI数据集，并将定量比较限制为选择感兴趣区域(ROI) 大脑样本。 近年来，同步辐射x射线微计算机层析成像(Microct)已经成为一种强有力的工具。 高分辨率组织成像。利用马赛克投影拼接方法，可以对整个小鼠大脑进行成像 在之前的DTI成像后，以1.2微米的各向同性3D分辨率进行成像。为了增强MicroCT对比度，组织- 对Sue标本进行fi染色，并用电子显微镜(EM)中使用的相同类型的金属基染料进行染色。 在嵌入树脂之前。我们将优化这条MicroCT-EM验证管道，以解决Pre VIEW基于组织学的研究，并在整个小鼠大脑中使用 微米到纳米尺度的神经学信息。 该方案的具体目标是：(1)模型相位对比以优化MicroCT数据采集；(2)VALI-fi-VALI-VALI-VALI。 使用地面真实MicroCT的DTI ODF重建方法(3)使用以下参数表征DTI性能 来自EM的组织微结构信息。完成后，目标1将产生一个新的理论模型 以及在强吸收生物样品中利用MicroCT相位对比度的采集策略。目标2将 生成整个小鼠大脑的ODF的地面真实数据集，该数据集将用于计算算法- SPECIfic空间地图的DTI性能。在AIM 3中，将选择大约20个ROI进行纳米级成像 EM，DTI的表现将通过基础神经结构的量化特征来表征。 这些结果将提供对DTI信号的前所未有的微观结构驱动的理解，从而使我们能够更深入地了解DTI信号。 fi研究以开发更高级的DTI模型和收购策略，以更好地利用TERE BER定位 以及神经疾病治疗中的连接信息。

Project Summary In this project, we propose to validate and characterize fiber orientation estimation from diffusion tensor imag- ing (DTI) through the optimization of a multi-modality, multi-scale imaging pipeline for whole mouse brains. DTI is a powerful tool used to noninvasively report 3D microstructural properties of nervous tissue on a macroscopic scale, and has played an important role in the understanding and diagnosis of a number of neurological disease processes. Modern acquisitions of DTI data can be processed to generate a 3D diffusion profile known as an orientation diffusion function (ODF) at each voxel. The ODF is used to infer the orientation of local axon fiber pop- ulations. Previous efforts to validate these orientation estimates have primarily relied on serial optical histology as a ground truth dataset. Histology-based pipelines involve the labor intensive task of physically sectioning the tissue into thin slices, leading to physical destruction of the sample and anisotropic resolution. These limitations potentially confound the accuracy of 3D orientation estimation, complicate the process of spatially registering the ground-truth and DTI datasets, and limit quantitative comparisons to select regions of interest (ROI) across the brain sample. In recent years, synchrotron x-ray microcomputed tomography (microCT) has emerged as a powerful tool for high-resolution tissue imaging. With a mosaic projection-stitching method, a whole mouse brain can be imaged at an isotropic, 3D resolution of 1.2 microns after prior imaging with DTI. To enhance microCT contrast, the tis- sue specimens are fixed and stained with the same kind of metal-based stains used in electron microscopy (EM) prior to embedding in resin. We will optimize this microCT-EM validation pipeline to address the limitations of pre- vious histology-based studies, and characterize DTI algorithm performance across a whole mouse brain using micron- to nano-scale neurological information. The specific aims of the proposal are: (1) model phase contrast to optimize microCT data acquisition, (2) vali- date DTI ODF reconstruction methods using ground-truth microCT (3) characterize DTI performance using under- lying tissue microstructure information from EM. Upon completion, aim 1 will generate a novel theoretical model and acquisition strategy to exploit microCT phase contrast in strongly absorbing biological samples. Aim 2 will generate a ground-truth dataset of ODFs across a whole mouse brain, which will be used to calculate algorithm- specific spatial maps of DTI performance. In Aim 3, around 20 ROI will be selected for nano-scale imaging with EM, and DTI performance will be characterized by quantitative features of the underlying neural architecture. These results will provide an unprecedented microstructure-driven understanding of the DTI signal, allowing fu- ture studies to develop more advanced DTI models and acquisition strategies to better leverage fiber orientation and connectivity information in the treatment of neurological disease.