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.

项目摘要/摘要 MRI扫描主要是使用对比度加权图像以定性方式进行和评估的（例如， 使用T1，T2或质子密度加权）。此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瑕疵调节的固有MR组织参数。定量 MR组织参数的映射，我们试图解开这种复合物的组合以提供直接 绝对单位中组织参数的表征。这有可能提高直接比较的 扫描不同机构和/或扫描仪，也有助于理解疾病进展 和单个患者的治疗。尽管长期以来定量MRI的潜力一直是 认识到，其使用受到冗长的收购时间的限制。磁共振指纹（MRF）是 定量MRI的最新突破，可以同时测量多个组织参数 在一个实验中，每个成像切片中的缩短获取时间急剧缩短到约15秒，并提供 本质上注册的地图。但是，这仍然可能导致高分辨率的冗长收购， 体积定量成像。例如，MRF最多可以花费20分钟进行全脑习得 空间分辨率为1.2×1.2×5 mm3，该分辨率本身就降低了结构的所需的分辨率 神经影像分析。主要缺陷是由于亚最佳数据采集和图像重建 目前雇用的计划。 在此应用程序中，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的准确性和速度 神经影像学。特别是，我们将利用巨大的灵活性/自由来实现批量采集 和图像重建以提高准确性和效率。具体来说，我们将解决图像 重建问题采用主要统计重建方法，该方法包含（1）数据模型 对于多通道采集，（2）体积时间序列图像的低级张量图像模型和（3）A 统计噪声模型。我们将通过计算重建性能（例如，错误栏）来表征重建性能 在低级张量模型下，受约束的Cramer-Rao边界（CRB）。我们解决数据采集 问题，通过使用受约束的CRB作为指标来优化MRF数据采集参数（例如，翻转 角度和补充时间时间表）和K空间轨迹（例如，螺旋形轨迹）用于改进SNR 效率。总之，我们期望提出的技术产生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）和全脑覆盖范围 简短的收购时间（例如，3分钟）。最后，我们将系统地验证 拟议的技术及其用于衰老研究的实用性，以快速的， 全脑MRI扮演着越来越重要的角色。