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 framework, 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 a resolution higher than 1
mm isotropic, 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.

这项研究介绍了一种基于深度学习的三维超分辨率 (fMRI) 方法，通过采用一个分辨率无关的图像增强框架，使我们的方法能适应不同的体素尺寸而无需重新训练。我们应用了这一创新技术来定位早期视觉区域中细粒度的运动选择性区域，并且证明这种方法潜在地能增强 fMRI 的空间分辨率，并大幅降低采集时间。