Distortion Correction in Functional MRI with Deep Learning

利用深度学习进行功能 MRI 畸变校正

• 批准号: 10647991
• 负责人: Qingfei Luo
• 金额: $ 8万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10647991
• 关键词: Acute; Adopted; Algorithms; Anatomy; Architecture; Area; Brain; Brain Mapping; Brain region; Chicago; Compensation; Complex; Data; Data Collection; Databases; Exhibits; Functional Magnetic Resonance Imaging; Funding; Goals; Illinois; Image; Individual; Lead; Learning; Magnetic Resonance; Magnetic Resonance Imaging; Measures; Methods; Modeling; Morphologic artifacts; Neurosciences; Patient Care; Performance; Phase; Physics; Random Allocation; Research; Residual state; Resolution; Scanning; Signal Transduction; Site; Techniques; Testing; Time; Tissues; Training; United States National Institutes of Health; Universities; analytical method; anatomic imaging; chronic pain; clinical application; cognitive neuroscience; convolutional neural network; data space; deep learning; deep learning algorithm; detection sensitivity; executive function; frontal lobe; functional MRI scan; image reconstruction; image registration; learning network; loss of function; magnetic field; mind control; novel; novel strategies; physical model; reconstruction; tool

Project Abstract Functional magnetic resonance imaging (fMRI), a non-invasive technique for mapping brain activity, has been widely used in cognitive neuroscience and patient care. Magnetic field inhomogeneities (B) around tissue interfaces can induce severe geometric distortions in specific brain regions in fMRI images. The image distortions lead to errors in the registration between fMRI and high-resolution anatomical MRI images, and thus decrease spatial accuracy and sensitivity of detecting brain activity with fMRI. In present fMRI studies, B-induced distortions are typically corrected in the reconstructed magnitude images using methods based on image registration, which assume a smoothly varying B. However, the registration-based correction (Reg-Corr) can cause image artifacts and blurring because its assumption breaks down in brain regions where B changes rapidly and omission of phase information in the magnitude images can exacerbate calculation errors. The overarching goal of this project is to develop a novel approach based on deep learning (DL) to accurately correct for geometric distortions through image reconstruction. By integrating the physical model of B effects into an unrolling DL network, distortion-free fMRI images will be directly reconstructed from the complex MR signal in k- space, without the assumption about the smoothness of B. The proposed reconstruction-based correction (Recon-Corr) algorithm will be trained and tested with raw k-space data from 4050 fMRI scans, in the Acute to Chronic Pain Signatures (A2CPS) consortium, in which the University of Illinois at Chicago is a primary performing site. The project has two specific aims: (1) To develop a physics-guided DL algorithm for simultaneous fMRI image reconstruction and distortion correction; (2) To systematically compare the performance of Recon-Corr and traditional Reg-Corr methods. By developing the Recon-Corr method and leveraging the large A2CPS fMRI k-space database, this project will demonstrate an accurate method for fMRI distortion correction that can offer better registration accuracy of functional and anatomical MRI images. Successful completion of the project will resolve a long-standing and important problem in fMRI (i.e., image distortion), contributing to fMRI applications in neuroscience, patient care, and other research areas.

项目摘要 功能性磁共振成像（fMRI）是一种非侵入性的脑活动成像技术， 广泛应用于认知神经科学和病人护理。组织周围的磁场不均匀性（磁场B） 界面会在fMRI图像中的特定大脑区域引起严重的几何失真。图像失真 导致fMRI和高分辨率解剖MRI图像之间的配准误差， 功能磁共振成像检测脑活动的空间准确性和灵敏度。在目前的fMRI研究中， 通常使用基于图像的方法来校正重构幅度图像中的失真 配准，其假设平滑变化的Δ B。然而，基于配准的校正（Reg-Corr）可以 造成图像伪影和模糊，因为它的假设在大脑区域发生变化，B 幅度图像中的相位信息的快速和遗漏会加剧计算误差。的 该项目的总体目标是开发一种基于深度学习（DL）的新方法， 通过图像重建来进行几何失真。通过将电子束B效应的物理模型整合到一个 展开DL网络，无失真的fMRI图像将直接从k中的复MR信号重建。 空间，而不需要关于图B的光滑性的假设。建议的基于重建的校正 （Recon-Corr）算法将使用来自4050次fMRI扫描的原始k空间数据进行训练和测试， 慢性疼痛特征（A2 CPS）联盟，其中伊利诺伊大学芝加哥分校是主要的 表演现场。该项目有两个具体目标：（1）开发一个物理引导的DL算法， 同时进行fMRI图像重建和失真校正;（2）系统地比较 Recon-Corr和传统Reg-Corr方法的性能。通过发展Recon-Corr方法， 利用大型A2 CPS fMRI k空间数据库，该项目将展示一种精确的fMRI方法， 失真校正，可以提供更好的功能和解剖MRI图像的配准精度。 该项目的成功完成将解决功能磁共振成像中一个长期存在的重要问题（即，图像 失真），有助于功能磁共振成像在神经科学，病人护理和其他研究领域的应用。