High SNR Functional Brain Imaging using Oscillating Steady State MRI

项目总结：使用振荡稳态MRI的高SNR脑功能成像 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全脑fMRI采集方法， 与当前领先的方法相比，SNR提高了2至3倍。这样的提升大致相当于 从3 T到7 T的SNR增益，但没有额外的成本。我们的目标是提供 快速、高SNR、亚毫米分辨率图像，具有非常好的时间分辨率。我们的做法是 根本不同的是，几乎所有的标准功能磁共振成像方法都使用了新发现的来源， 基于重复使用磁化的振荡稳态方法的用于fMRI的信号， 提高了信号强度。该信号显示具有与标准fMRI相似的对比度加权 方法.振荡是非常可再现的，这将允许使用基于模型的重建， 示例低秩（LR）方法。一种新的基于黄金角的LR张量及其获取方法 提出了旋转变密度采集，在初步数据中，显示出17倍的速度提高， 错误率总之，这些方法有望大大提高功能磁共振成像的信噪比（SNR 并允许更高的空间分辨率。 该项目有四个主要目标：（1）分析和模拟OSS信号的自旋物理， 该信号的性质，并获得最佳灵敏和鲁棒的采集参数，（2）开发最佳的 用于OSS fMRI采集图像采集和重建方法。收购和重建 战略与开放源码软件方法有着必然的联系，并且是唯一的;（3）开发和评估方法， 解决了几个公认的问题与功能磁共振成像采集，特别是生理噪声和头部 运动，以及（4）评价OSS功能磁共振成像方法与最先进的同步多层成像的比较 (SMS)采集方法在幻影和人类受试者使用任务和静息状态fMRI。 所提出的技术将大大提高信噪比和空间分辨率为一组给定的硬件（主 磁场强度、RF线圈阵列）。更高的SNR将允许个体受试者中更稳健的fMRI， 虽然空间分辨率是重要的，因为大脑的功能单元（皮质柱）是1- 2 mm， 类似地，功能上不同的层是亚毫米级的，其中距输入层和输出层的距离约为 1毫米。由于该方法不依赖于任何特定的硬件，因此该方法可以广泛而快速地应用于各种场合。 传播到神经影像学界。

Project Summary: High SNR Functional Brain Imaging using Oscillating Steady State MRI 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 whole-brain fMRI acquisition approach that improves the SNR by 2- to 3-fold in comparison to the current leading methods. Such a boost is roughly equivalent to the SNR gain one achieves in going from 3T to 7T, but without the additional costs. Our goal is to provide rapid, high SNR, sub-millimeter resolution images with very good temporal resolution. Our approach is fundamentally different that nearly all standard fMRI methods in that is uses a newly discovered source of signal for fMRI that is based on an oscillating steady state approach which reuses magnetization and thus, improves the signal strength. This signal is shown to have contrast weighting that is similar to standard fMRI methods. The oscillations are very reproducible, which will allow the use of model based reconstructions, for example low-rank (LR) methods. A novel LR tensor and acquisition approach based on with a golden-angle rotated variable density acquisition is proposed that, in preliminary data, show a 17-fold speed-up with very low error rates. Together, these methods promise to dramatically improve the signal-to-noise ratio (SNR) of fMRI and allow for higher spatial resolution. The project has four main aims: (1) Analyze and simulate the spin physics of the OSS signal to elucidate the nature of this signal and obtain optimally sensitive and robust acquisition parameters, (2) Develop optimal image acquisition and reconstruction methods for OSS fMRI acquisition. The acquisition and reconstruction strategies are necessarily linked and are unique to the OSS approach, (3) Develop and evaluate methods to address several well-recognized issues associated with fMRI acquisition, notably physiological noise and head motion, and (4) Evaluate the OSS fMRI approach in comparison to state-of-the-art simultaneous multislice (SMS) acquisition methods in phantoms and in human subjects using both task and resting state fMRI. The proposed technology will greatly improve the SNR and spatial resolution for a given set of hardware (main magnetic field strength, RF coils arrays). Higher SNR will allow for more robust fMRI in individual subject, while spatial resolution is important as the functional units (cortical columns) of the brain are 1-2mm and similarly, functionally distinct layers are sub-mm with the distances from input and output layers being about 1mm. Since the methods do not relay on any unique hardware, the method can be widely and quickly disseminated to the neuroimaging community.