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: 10266853
• 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/10266853
• 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.

项目概要/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）。该图像加权是其中一个或多个的非线性函数。 由外部扫描仪设置和定量缺陷调节的内在 MR 组织参数。 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 以绝对单位表征组织参数，这有可能改善直接比较。 跨不同机构和/或扫描仪进行扫描，也有助于了解疾病进展 尽管定量 MRI 的潜力长期以来一直受到关注。 众所周知，磁共振指纹识别 (MRF) 的使用受到较长采集时间的限制。 定量 MRI 的最新突破可以同时测量多个组织参数 在单个实验中，将每个成像切片的采集时间大幅缩短至约 15 秒，并提供 然而，这仍然会导致高分辨率、不可接受的冗长采集。 例如，MRF 进行体积全脑采集可能需要长达 20 分钟。 空间分辨率为1.2×1.2×5 mm3，该分辨率本身就达不到结构所需的分辨率 神经影像分析的主要缺陷是由于数据采集和图像重建不理想。 目前采用的方案。 在此应用中，我们将通过严格的方法优化 MRF 的数据采集和图像重建。 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 特别是，我们将利用体积采集固有的巨大灵活性/自由度。 具体来说，我们将解决图像重建问题。 采用原则统计重建方法的重建问题，该方法包含 (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） 我们将通过计算统计噪声模型来表征重建性能（例如误差条）。 我们解决了低秩张量模型下的约束 Cramer-Rao 边界（CRB）问题。 问题，通过利用约束 CRB 作为指标来优化 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，螺旋堆栈轨迹）以提高信噪比 总之，我们预计所提出的技术能够产生 2 倍更准确的 MR 组织。 参数图，实现理想的分辨率（例如，各向同性 0.8 mm3）和全脑覆盖 较短的采集时间（例如3分钟）最后，我们将系统地验证该性能。 提出的技术及其在衰老研究中的实用性，通过快速、 全脑 MRI 正在发挥着越来越重要的作用。