Resolution- and Stimulus-Agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies

• Published in: Proceedings (International Symposium on Biomedical Imaging) Vol. 2024; pp. 1 - 5
• Main Authors: Li, Hongwei Bran, Rosen, Matthew S., Nasr, Shahin, Iglesias, Juan Eugenio
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.05.2024
• Subjects: Functional magnetic resonance imaging; Organizations; Robustness; Superresolution; Three-dimensional displays; Training data; Visualization
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI56570.2024.10635270

High-resolution fMRI provides a window into the brain's mesoscale organization. Yet, higher spatial resolution increases scan times, to compensate for the low signal and contrast-to-noise ratio. This work introduces a deep learning-based 3D super-resolution (SR) method for fMRI. By incorporating a resolution-agnostic image augmentation frame-work, our method adapts to varying voxel sizes without retraining. We apply this innovative technique to localize fine-scale motion-selective sites in the early visual areas. Detection of these sites typically requires ≤ 1mm isotropic data, whereas here, we visualize them based on lower resolution (2-3mm isotropic) fMRI data. Remarkably, the super-resolved fMRI is able to recover high-frequency detail of the interdigitated organization of these sites (relative to the color-selective sites), even with training data sourced from different subjects and experimental paradigms - including non-visual resting-state fMRI, underscoring its robustness and versatility. Quantitative and qualitative results indicate that our method has the potential to enhance the spatial resolution of fMRI, leading to a drastic reduction in acquisition time.