Advancing fMRI Acquisition through Dissemination of EPTI- An Efficient Distortion-Free Multi-Contrast Imaging Technology

项目总结/摘要 功能性MRI（fMRI）是当今用于脑功能的非侵入性成像的主要工具， 彻底改变了我们对人脑的理解迄今为止，回波平面成像（EPI）已成为标准 功能磁共振成像采集方法，但遭受固有的限制，如静态和动态失真， 图像/对比度模糊、信号空洞、次优CNR和生理噪声。这些限制妥协了 任务和静息态功能磁共振成像的灵敏度和可靠性，并阻碍有效的空间分辨率， 对于最先进的高分辨率层状或柱状fMRI研究， 假定这些问题在更高的分辨率和场强下变得更加严重。 回波平面时间分辨成像（EPTI）最近被引入来解决这些限制。 常规的图像形成从随时间演变的k空间样本生成单个回波图像， 积累的缺陷。EPTI识别图像期间数据的强时空相关性 编码过程，并利用它来取代传统的图像形成和解决多回波图像， 完全恢复的时空数据，使收购免疫这些缺陷。这将成为一个 缺陷-MR信号随时间的演变-转化为资产。它采用以下技术实现高SNR/CNR效率： 具有最小死区的连续读出，同时提供完全不受静态和动态影响的图像 由场不均匀性引起的失真及其由于对象运动引起的变化。多回波图像 在宽范围的TE下采样（例如，~4-70 ms）不仅允许整个大脑的最佳CNR， 在具有挑战性的短T2* 区域减轻信号丢失，而且还能够有效去除不需要的 生理噪音此外，它们在精确的TE处提供纯对比度，具有最小的污染， 用于提高高分辨率层状或柱状fMRI研究的特异性。这些改进 由EPTI提供的高功率激光器已经在3 T和7 T的各种应用中得到了证明。 因此，本项目的目标是广泛传播EPTI作为下一代功能磁共振成像采集工具。 尽管EPTI已被证明具有很高的价值，但由于缺乏 可用的脉冲序列和图像重建软件以及跨供应商实施的挑战。 在这个项目中，我们将通过组装和传播整个EPTI收购来克服这些障碍， 在不同供应商平台（Siemens和GE）中开发的重建和预处理包， 开源和跨供应商框架，可供多个其他供应商访问。序列将被细化 通过结合多种功能，并与优化的重建和预处理集成， 用户友好的工作流程。我们还将利用马蒂诺斯中心的现有资源和机制， 技术传播和质量保证，多年来通过大规模的 研究（例如，HCP，ABCD），以最大限度地发挥这项技术的影响，并确保其可持续性。

PROJECT SUMMARY / ABSTRACT Functional MRI (fMRI) is today the predominant tool for noninvasive imaging of brain function, which has revolutionized our understanding of the human brain. To date, echo-planar imaging (EPI) has been the standard fMRI acquisition method, but suffers from intrinsic limitations such as static and dynamic distortion, image/contrast blurring, signal voids, suboptimal CNR and physiological noises. These limitations compromise the sensitivity and reliability of task and resting-state fMRI, and hinder the effective spatial resolution and achievable functional specificity for state-of-the-art high-resolution laminar or columnar fMRI studies, especially given that these problems become more severe at higher resolutions and field strengths. Echo-planar time-resolved imaging (EPTI) has recently been introduced to address these limitations. Conventional image formation generates a single echo image from k-space samples evolving across time with accumulated imperfections. EPTI recognizes the strong spatiotemporal correlation of the data during the image encoding process, and exploits it to replace conventional image formation and resolve multi-echo images from the fully-recovered spatiotemporal data, making the acquisition immune to these imperfections. This turns a deficit – the evolution of the MR signal with time – into an asset. It achieves high SNR/CNR efficiency using continuous readout with minimal deadtime, while providing images completely free from both static and dynamic distortions caused by field inhomogeneity and its variations due to subject motion. The multi-echo images sampled at a wide range of TEs (e.g., ~4–70 ms) not only allow for optimal CNR across the whole brain and mitigated signal dropout at challenging short T2* regions, but also enable effective removal of unwanted physiological noise. Moreover, they provide pure contrast at the exact TEs with minimal contaminations that can be used to improve the specificity for high-resolution laminar or columnar fMRI studies. These improvements provided by EPTI have been demonstrated at both 3T and 7T in a variety of applications. The goal of this project is therefore to broadly disseminate EPTI as the next-generation fMRI acquisition tool. Despite its demonstrated high value, EPTI has only been used in a small number of sites due to the lack of available pulse sequence and image reconstruction software and the challenge of cross-vendor implementation. In this project, we will overcome these barriers by assembling and disseminating the whole EPTI acquisition, reconstruction and pre-processing package developed in different vendor platforms (Siemens and GE) and in open-source and cross-vendor frameworks accessible to multiple other vendors. The sequence will be refined by incorporating multiple functionalities, and integrated with the optimized reconstruction and pre-processing in a user-friendly workflow. We will also leverage the existing resources and mechanisms at Martinos Center for technology dissemination and quality assurance, which have been refined over the years through large-scale studies (e.g., HCP, ABCD), to maximize the impact of this technology and ensure its sustainability.