Acquisition technology for in vivo functional and structural MR imaging at the mesoscopic scale.

在动物模型和离体样品的显微脑成像方面取得了重大进展， 在光学显微镜和新的工具，用于操纵神经回路和有针对性的刺激的进展; 使我们能够在这个精细的尺度上获得对神经细胞和电路功能的新见解。与此同时 随着技术的发展，体内非侵入性人脑成像，特别是通过MRI，也经历了 重大进步。这使它能够在宏观尺度上收集丰富的功能和结构信息 快速，也有助于推动更高的空间分辨率，其中在介观尺度上的成像是 开始变得可行。尽管如此，在实现足够的特异性和敏感性方面仍然存在关键障碍 在这种规模下。MRI在结构和功能上更精确地在中尺度成像的能力将 发挥关键作用，弥合差距，并将我们在微观尺度上的理解与动物和 从体外研究到在大规模研究和临床环境中进行的大规模人体成像。 该项目将为MR技术开发创建一个程序，以克服MRI中当前的“编码限制 在介观尺度上实现体内成像：人体的扩散、功能和结构成像 在400-600 µm的各向同性体素尺寸下，具有高灵敏度和高空间精度。这个会推进去 活体MRI从大脑皮质柱和层以及皮质下的宏观尺度向中观尺度 核转移新的见解从侵入性动物和死后微观尺度成像到非侵入性 人体成像因为大脑组织的基本模块可以在中观尺度上观察到 该项目将允许在相关空间尺度上进行足够覆盖的体内研究。 我们将进行一个协同的“从地面上”的发展，结合新的编码， 重建策略与新的可用仪器，以实现高成像保真度和灵敏度， 目标分辨率。SNR高效的容积和连续采集沿着高加速 将开发时空受控混叠编码。图像编码的新方法将是 利用最近推出的组合RF和B 0匀场阵列技术，不仅用于其原有的 预期目的是减少B 0不均匀性，但也是为了补充常规编码方案， 提高加速性能、改进鲁棒性并实现大伪影减轻，特别是对于 多针肾上腺素协同重建计划也将使用新兴的概念，在低级别的发展 多维子空间建模与强大的机器学习（ML）算法相结合。的 提出的功能和结构数据的时间分辨重建将提供一种新的、丰富的成像方法， 一个数据集包含来自单次扫描的数百个TE和TI。通过这种方法， 从松弛效应和失真从B 0不均匀性，也将被删除，以创建尖锐，高保真 数据集。

Significant strides have been made in microscopic brain imaging of animal models and ex vivo samples, led by advances in optical microscopy and new tools for manipulation of neural circuitry and targeted stimulation; enabling us to gain new insights into neuronal cells and circuits functions at this fine scale. Concurrent to these developments, in vivo non-invasive human brain imaging, particularly through MRI, has also undergone significant advancement. This has allowed it to collect rich functional and structural information at the macroscale quickly, and also aid in its push towards higher spatial resolution, where imaging at the mesoscopic scale is starting to become feasible. Nonetheless, critical barriers remain in achieving adequate specificity and sensitivity at this scale. The ability to image more precisely at the mesoscale both structurally and functionally with MRI will play a critical role to bridge the gap and transfer our improved understanding at the microscale with animal and ex vivo studies to macroscale human imaging that are performed in large scale studies and in clinical settings. This project will create a program for MR technology development to overcome current “encoding limits” in MRI to achieve in vivo imaging at the mesoscopic scale: diffusion, functional, and structural imaging of the human brain at the 400–600 µm isotropic voxel size with high sensitivity and high spatial accuracy. This will push in vivo MRI from the macro-scale toward the meso-scale of cerebral cortical columns and layers and subcortical nuclei to transfer new insights from invasive animal and post mortem micro-scale imaging to non-invasive human imaging. Because fundamental modules of brain organization can be observed in the meso-scale architecture, this project will allow for in vivo investigation at relevant spatial scales with sufficient coverage. We will undertake a synergistic ‘from-the-ground-up’ development that combines novel encoding and reconstruction strategies with newly-available instrumentation to achieve high imaging fidelity and sensitivity at the target resolution. SNR-efficient volumetric and continuous acquisitions along with highly-accelerated spatio-temporal controlled-aliasing encoding will be developed. New approaches to image encoding will be created that utilize a recently-introduced combined RF and B0 shim-array technology, not only for its original intended purpose of reducing B0 inhomogeneity, but also to complement conventional encoding schemes to increase acceleration performance, improve robustness, and achieve large artifacts mitigation, particularly for multi-shot EPI. Synergistic reconstruction schemes will also be developed using emerging concepts in low-rank and multi-dimensional sub-space modeling combined with powerful Machine Learning (ML) algorithms. The proposed time-resolved reconstruction of both functional and structural data will provide a new, rich imaging dataset with hundreds of TEs and TIs from a single scan. With this approach, the detrimental image blurring from relaxation effects and distortion from B0 inhomogeneity, will also be removed to create sharp, high-fidelity datasets.