Optimized MR Fingerprinting for Rapid Volumetric Quantitative Neuroimaging

用于快速体积定量神经成像的优化 MR 指纹识别

• 批准号: 10260805
• 负责人: Bo Zhao
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-21 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10260805
• 关键词: 3-Dimensional; Address; Age; Aging; Alzheimer' s Disease; Brain; Calibration; Complex; Data; Detection; Disease; Disease Progression; Fingerprint; Freedom; Goals; Hospitals; Human; Image; Imaging Techniques; Institution; Longevity; MRI Scans; Magnetic Resonance; Magnetic Resonance Imaging; Maps; Measurement; Modeling; Noise; Patients; Pattern; Performance; Play; Process; Property; Protons; Published Comment; Resolution; Role; Scanning; Schedule; Scheme; Series; Slice; Speed; Structure; Techniques; Thick; Three-Dimensional Imaging; Time; Tissues; Weight; White Matter Hyperintensity; base; data acquisition; data modeling; data space; density; experimental study; falls; flexibility; heuristics; image reconstruction; imaging biomarker; improved; in vivo; magnetic field; neuroimaging; novel; quantitative imaging; reconstruction; signal processing; simulation

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.

项目总结/摘要 MRI扫描主要使用对比加权图像（例如， 具有T1、T2或质子密度加权）。该图像加权是这些中的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。以定量 MR组织参数的映射，我们试图解开这个复杂的组合，以提供直接的 以绝对单位表示的组织参数的表征。这有可能改善直接比较， 在不同机构和/或扫描仪之间进行扫描，还有助于了解疾病进展 和治疗的时间跨度。尽管定量MRI的潜力长期以来一直受到质疑， 虽然认识到这一点，但其使用受到购置时间过长的限制。磁共振指纹（MRF）是一种 定量MRI的最新突破，可同时测量多个组织参数 在单个实验中，将采集时间显著缩短至每个成像切片约15秒， 内在的映射。然而，这仍然可能导致无法接受的高分辨率， 容积定量成像例如，MRF可能需要长达20分钟的体积全脑采集 其空间分辨率为1.2×1.2×5 mm 3，该分辨率本身福尔斯达不到结构化所需的分辨率。 神经影像学分析主要的不足是由于次优的数据采集和图像重建 目前采用的方案。 在这个应用中，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准确性和速度 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 以及图像重建以提高精度和效率。具体来说，我们将解决图像 重建问题与原则的统计重建方法，包括（1）数据模型 对于多通道采集，（2）用于体积时间序列图像的低秩张量图像模型，以及（3） 统计噪声模型我们将表征重建性能（例如，误差条）， 低秩张量模型下的约束Cramer-Rao边界（CRB）。我们解决数据采集 问题，通过利用受约束的CRB作为度量来优化MRF数据采集参数（例如，翻转 角度和重复时间表）和k空间轨迹（例如，螺旋轨迹堆叠），以提高SNR 效率总之，我们预计所提出的技术可以产生2倍更准确的MR组织 参数图，实现期望的分辨率（例如，各向同性0.8 mm 3）和全脑覆盖， 短的获取时间（例如，3分钟）。最后，我们将系统地验证 所提出的技术及其用于衰老研究的效用，其中通过快速， 全脑MRI正在发挥越来越重要的作用。