Work

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

Year: 2024

Type: conference-paper

Abstract: 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.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi56570.2024.10635270

Authors: Hongwei Bran Li, Matthew S. Rosen, Shahin Nasr, Juan Eugenio Iglesias

Institutions: Harvard University, Athinoula A. Martinos Center for Biomedical Imaging

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green