Optimized MR Fingerprinting for Rapid Volumetric Quantitative Neuroimaging

项目摘要/摘要 主要使用对比度加权图像以定性的方式执行和评估MRI扫描(例如， 具有T1、T2或质子密度加权)。该图像权重是其中一个或多个的非线性函数 由外部扫描仪设置和缺陷调制的固有MR组织参数。在数量上 磁共振组织参数的测绘，我们试图解开这种复杂的组合，以提供直接的 以绝对单位表征组织参数。这有可能改善对 跨不同机构和/或扫描仪进行扫描，还有助于了解疾病进展情况 以及对一名患者的治疗。尽管定量核磁共振的潜力长期以来一直是 尽管已经得到认可，但由于收购时间较长，它的使用一直受到限制。磁共振指纹识别(MRF)是一种 定量磁共振成像的最新突破，可以同时测量多个组织参数 在单个实验中，将采集时间大幅缩短至每个成像切片约15秒，并提供 登记在册的地图。然而，这仍然可能导致对高分辨率的长得令人无法接受的获取， 体积定量成像。例如，磁共振成像可能需要长达20分钟的全脑体积采集 空间分辨率为1.2×1.2×5 mm~3，这一分辨率本身就低于结构分析所需的分辨率 神经影像分析。主要的不足之处在于数据采集和图像重建不够理想 目前正在实施的计划。 在这一应用中，我们将通过严格的 统计信号处理框架，总体目标是提高体积测量的精度和速度 神经成像。特别是，我们将利用体积采集固有的巨大灵活性/自由度 和图像重建，以提高精度和效率。具体地说，我们将解决图像 结合(1)数据模型的原则性统计重建方法的重建问题 对于多通道采集，(2)体积时间序列图像的低阶张量图像模型，以及(3) 统计噪声模型。我们将通过计算以下参数来表征重建性能(例如误差条 低阶张量模型下的约束Cramer-Rao界(CRB)。我们解决了数据采集问题 问题，通过使用受限CRB作为度量来优化MRF数据采集参数(例如，翻转 角度和补充时间安排)和k空间轨迹(例如，螺旋堆叠轨迹)以改善SNR 效率。总而言之，我们期望所提出的技术产生的磁共振组织的精确度提高2倍 参数图，实现所需的分辨率(例如，各向同性0.8mm3)和全脑覆盖 获取时间短(例如，3分钟)。最后，我们将系统地验证该算法的性能 拟议的技术及其在衰老研究中的效用，对于该技术，通过快速、 全脑核磁共振发挥着越来越重要的作用。

PROJECT SUMMARY/ABSTRACT MRI scans are primarily performed and evaluated in a qualitative way using contrast-weighted images (e.g., with T1, T2 or proton-density weighting). This image weighting is a nonlinear function of one or more of these intrinsic MR tissue parameters as modulated by external scanner settings and imperfections. In quantitative mapping of MR tissue parameters, we attempt to unravel this complex combination to provide a direct characterization of the tissue parameter in absolute units. This has potential to improve direct comparisons of scans across different institutions and/or scanners, and also facilitates the understanding of disease progression and treatment for a single patient across time. Although the potential of quantitative MRI has long been recognized, its use has been limited by lengthy acquisition times. Magnetic resonance fingerprinting (MRF) is a recent breakthrough in quantitative MRI that enables simultaneous measurements of multiple tissue parameters in a single experiment, dramatically shortening acquisition time to ~15 sec per imaging slice and providing intrinsically registered maps. However, this can still result in unacceptably lengthy acquisitions for high-resolution, volumetric quantitative imaging. For example, MRF can take up to 20 min for a volumetric whole-brain acquisition with a spatial resolution of 1.2×1.2×5 mm3, a resolution which, itself, falls short of that needed for structural neuroimaging analysis. The major deficiency is due to the sub-optimal data acquisition and image reconstruction schemes currently employed. In this application, we will optimize the data acquisition and image reconstruction for MRF by a rigorous statistical signal processing framework, with an overall goal of improving the accuracy and speed of for volumetric neuroimaging. In particular, we will exploit the tremendous flexibility/freedom inherent to volumetric acquisition and image reconstruction to improve accuracy and efficiency. Specifically, we will address the image reconstruction problem with a principled statistical reconstruction approach that incorporates (1) a data model for multi-channel acquisitions, (2) a low-rank tensor image model for volumetric time-series images, and (3) a statistical noise model. We will characterize the reconstruction performance (e.g., error bars) by calculating the constrained Cramer-Rao bounds (CRB) under low-rank tensor models. We address the data acquisition problem, by utilizing the constrained CRB as metrics to optimize MRF data acquisition parameters (e.g., flip angle and repletion time schedule) and k-space trajectories (e.g., stack-of-spiral trajectories) for improved SNR efficiency. Together, we expect that the proposed technique produces 2x more accurate MR tissue parameter maps, enabling a desirable resolution (e.g., isotropic 0.8 mm3) and a whole-brain coverage in a short acquisition time (e.g., 3 minutes). Finally, we will systematically validate the performance of the proposed technique and its utility for ageing studies, for which quantitative imaging biomarkers enabled by rapid, whole-brain MRI are playing an increasingly important role.