Fast Functional MRI with Sparse Sampling and Model-Based Reconstruction

具有稀疏采样和基于模型的重建的快速功能 MRI

• 批准号: 9228804
• 负责人: JEFFREY A FESSLER
• 金额: $ 32.63万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9228804
• 关键词: Acceleration; Address; Aging; Alzheimer' s Disease; Biological Markers; Brain; Brain imaging; Brain region; Cardiac; Complex; Cortical Column; Data; Development; Dimensions; Frequencies; Functional Magnetic Resonance Imaging; Future; Goals; Head; Image; Imaging Techniques; Information Networks; Language; Lead; Magnetic Resonance Imaging; Measures; Mental disorders; Methods; Modeling; Morphologic artifacts; Motion; Multiple Sclerosis; Neurosciences Research; Noise; Output; Pathway Analysis; Patients; Pattern; Physiological; Population Study; Predisposition; Process; RF coil; Resolution; Respiration; Rest; Sampling; Signal Transduction; Slice; Socioeconomic Status; Speed; Substance abuse problem; Surface; Techniques; Technology; Testing; Time; Variant; Work; base; cognitive neuroscience; cognitive process; heart rhythm; hemodynamics; image reconstruction; improved; magnetic field; nervous system disorder; neuroimaging; novel; physiologic model; public health relevance; reconstruction; relating to nervous system; respiratory; response; social; spatiotemporal; temporal measurement; tool; trend

Project Summary: Fast Functional MRI with Sparse Sampling and Model-Based Reconstruction Functional brain imaging using MRI (functional MRI or fMRI) has grown rapidly over the past 25 years and is widely used for basic cognitive neuroscience research and for presurgical planning. It is increasingly being used for developing biomarkers for neurological and psychiatric disorders and for population based studies of, for example, normal and abnormal development and aging. There have also been developments in imaging hardware and methods as well as processing methods to correct for artifacts and analyze functional activity. The overarching goal of this project is to develop a novel ultra-fast whole-brain fMRI acquisition approach that expands the spatiotemporal resolution envelope by roughly 3-fold. For example, new methods will allow 2mm isotropic resolution image with 250ms temporal resolution or 1.5mm isotropic resolution images with 500ms temporal resolution. Current state-of-the-art acquisition approaches for fMRI use the simultaneous multislice (SMS, and also known as multiband) method; these single-shot acquisitions use parallel imaging concepts and array coils to provide acceleration in the slice direction and possibly, the in-plane direction as well. Our approach is fundamentally different and uniquely powerful because: 1) it uses parallel imaging concepts for the slice and in-plane directions similar to multiband methods, while 2) also exploiting the temporal dimension that has a substantial data redundancy, and 3) incorporating novel image reconstruction methods based on low- rank (LR) spatiotemporal representations and “sparse” sampling patterns that extend farther out in k-space to improve spatial resolution. Together, these methods promise to enable new faster and more robust fMRI acquisition technology than is currently possible, while also improving spatial resolution. The project has three main aims: (1) Develop new low-rank and sparse (L+S) acquisition and reconstruction methods that model temporal basis functions using multi-coil array data, and account for magnetic field inhomogeneity; (2) Develop and evaluate methods to address several well-recognized issues associated with fMRI acquisition, notably physiological noise, head motion, and susceptibility-induced signal losses; and (3) Evaluate the low-rank and sparse acquisition approach and compare to state-of-the-art SMS (multiband) acquisition methods for task and resting state fMRI. The proposed technology will greatly improve spatiotemporal resolution for a given set of hardware (gradient and RF coils). Faster fMRI will allow improved physiological noise correction, improved statistical power and sensitivity for network analysis, and discovery of temporally ordered network processes. Higher spatial resolution will lead to less partial volume and susceptibility artifacts, improved surface-based analyses, and potentially layer-specific BOLD dynamics. These methods also may lead to fMRI that is more robust to head motion making it more useful for patient studies and studies of language.

项目摘要：具有稀疏采样和基于模型的重建的快速功能性MRI 使用MRI（功能性MRI或fMRI）的功能性脑成像在过去25年中迅速发展， 广泛用于基础认知神经科学研究和术前规划。人们越来越 用于开发神经和精神疾病的生物标志物， 例如正常和异常的发育和衰老。在成像方面也有进展 硬件和方法以及处理方法来校正伪像和分析功能活动。 该项目的总体目标是开发一种新的超快速全脑fMRI采集方法， 将时空分辨率包络扩展了大约3倍。例如，新方法将允许2 mm 时间分辨率为250 ms的各向同性分辨率图像或时间分辨率为500 ms的1.5 mm各向同性分辨率图像 时间分辨率目前最先进的fMRI采集方法使用同步多层 (SMS，也称为多波段）方法;这些单次激发采集使用并行成像概念， 阵列线圈，以提供切片方向上的加速度，也可能提供平面内方向上的加速度。我们 这种方法是根本不同的和独特的强大的，因为：1）它使用并行成像概念， 切片和平面内方向类似于多波段方法，同时2）还利用时间维度， 具有大量的数据冗余，以及3）结合基于低- 排序（LR）时空表示和“稀疏”采样模式，在k空间中延伸得更远， 提高空间分辨率。总之，这些方法有望使新的更快，更强大的功能磁共振成像 这是一种比目前可能的采集技术，同时还提高了空间分辨率。 该项目有三个主要目标：（1）开发新的低秩和稀疏（L+S）采集和重建 使用多线圈阵列数据对时间基函数进行建模的方法， （2）制定和评估方法，以解决与以下方面有关的几个公认的问题： fMRI采集，特别是生理噪声、头部运动和易失性引起的信号损失;以及（3） 评估低秩和稀疏采集方法，并与最先进的SMS（多频段）进行比较 任务和静息状态fMRI的采集方法。 所提出的技术将极大地提高给定硬件集（梯度）的时空分辨率 和RF线圈）。更快的功能磁共振成像将允许改善生理噪声校正，提高统计能力， 网络分析的灵敏度，以及时间有序网络过程的发现。较高空间 分辨率将减少部分体积和磁化率伪影，改善基于表面的分析， 潜在的特定于层的BOLD动态。这些方法也可能导致功能磁共振成像，更强大的头部 运动使其对患者研究和语言研究更有用。