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.