UFREE: Unified Fiber Orientation Distribution Resolution Enhancement

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that enables non-invasive mapping of structural brain connectivity. Fiber orientation distribution (FOD) is a crucial intermediate model used to represent complex white matter fiber configurations within a voxel and serves as a key step for subsequent brain tractography and connectome analysis. However, obtaining reliable FOD estimates is particularly challenging due to the constraints of acquisition protocols, which often feature low spatial and angular resolutions in non-research-focused clinical settings. Current deep learning approaches could address these limitations, but they typically enhance FOD quality only for either low angular or low spatial resolution inputs. Here, we present a unified and robust deep learning framework, namely UFREE, for FOD enhancement from varied resolutions. UFREE can produce high quality FOD images that align closely with ground-truth data from multimodal analysis of FOD derivatives. This framework holds the potential to bring research-level FOD quality to routine neuroimaging acquired in the clinical environment.