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 功能磁共振成像（fMRI）是一种用于映射脑活动的非侵入性技术，已经是 广泛用于认知神经科学和患者护理中。磁场不均匀性（B）周围 界面可以诱导fMRI图像中特定大脑区域的严重几何变形。图像扭曲 导致fMRI和高分辨率解剖学MRI图像之间的注册错误，从而减少 通过fMRI检测大脑活性的空间精度和灵敏度。在目前的fMRI研究中，B诱导 使用基于图像的方法，在重建的幅度图像中通常是正确的失真 注册，假设平稳变化。但是，基于注册的校正（Reg-Corr）可以 导致图像伪影和模糊，因为它的假设在大脑区域发生了故障 幅度级图像中相位信息的迅速省略会加剧计算误差。 该项目的总体目标是开发一种基于深度学习（DL）的新颖方法，以准确纠正 用于通过图像重建的几何变形。通过将B效应的物理模型整合到一个 展开的DL网络，无失真的fMRI图像将直接从K-中的复杂MR信号中重建 空间，没有关于b的平滑度的假设。拟议的基于重建的校正 （侦察）算法将接受4050 fMRI扫描的原始K空间数据训练和测试， 慢性疼痛签名（A2CPS）财团，其中伊利诺伊大学的芝加哥大学是主要的 表演站点。该项目有两个具体的目的：（1）开发一种物理引导的DL算法 简单的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。通过开发侦察方法和 利用大型A2CPS fMRI K-Space数据库，该项目将证明fMRI的准确方法 可以提供功能和解剖学MRI图像的更好的注册准确性的失真校正。 成功完成该项目将解决fMRI中的长期存在的重要问题（即图像 失真），为神经科学，患者护理和其他研究领域的功能磁共振成像应用做出贡献。