# Distortion Correction in Functional MRI with Deep Learning

> **NIH NIH R03** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2023 · $79,950

## Abstract

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.

## Key facts

- **NIH application ID:** 10647991
- **Project number:** 1R03EB034480-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Qingfei Luo
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $79,950
- **Award type:** 1
- **Project period:** 2023-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10647991

## Citation

> US National Institutes of Health, RePORTER application 10647991, Distortion Correction in Functional MRI with Deep Learning (1R03EB034480-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10647991. Licensed CC0.

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