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.

翻译：高分辨率功能磁共振成像（fMRI）为解析大脑介观尺度组织提供了窗口。然而，更高的空间分辨率会增加扫描时间，以补偿低信号和对比度噪声比。本研究提出了一种基于深度学习的三维fMRI超分辨率（SR）方法。通过融入分辨率无关的图像增强框架，我们的方法无需重新训练即可适应不同体素尺寸。我们将这一创新技术应用于早期视觉皮层中精细尺度运动选择性位点的定位。通常，检测这些位点需要高于1mm各向同性的分辨率，而本研究基于较低分辨率（2-3mm各向同性）的fMRI数据实现了其可视化。值得注意的是，超分辨率fMRI能够恢复这些位点（相较于颜色选择性位点）交错组织的高频细节，即使训练数据源自不同被试和实验范式——包括非视觉静息态fMRI，这充分体现了其鲁棒性和通用性。定量与定性结果表明，本方法具有提升fMRI空间分辨率的潜力，可显著缩短采集时间。