SUPER-RESOLUTION OF DIFFUSION MRI VIA JOINT CONSIDERATION OF SPATIAL AND ANGULAR SPACES USING GRAPH CNN

Abstract

Diffusion magnetic resonance imaging (dMRI) is an essential technique for studying brain white matter. However, its application suffers from the tradeoff between resolution, signal-to-noise ratio (SNR), and scanning time. Under a given setting, post-acquisition super-resolution (SR) has displayed the exciting performance of increasing the spatial or angular resolution of dMRI without prolonging the acquisition time. dMRI is acquired from a heterogeneous space composed of the spatial domain (x-space) and the diffusion wave vector domain (q-space). We introduced a graph convolution neural network (GCNN) to fit the mapping from low-resolution (LR) dMRI to high-resolution (HR) counterpart by jointly considering x-space and q-space to make an effective learning in this non-Euclidean space to increase the spatial resolution. Real brain dMRI data were downloaded from the Human Connectome Project (HCP). Comparisons were made between different spaces, including x-space, q-space, and xq-space. The results indicated that the best performance was obtained when two spaces were considered jointly using GCNN. Compared with the traditional methods, our network fully used spatio-angular information to improve the quality of dMRI SR. Furthermore, some feature metrics, including fractional anisotropic mapping, fiber orientation distribution (FOD), and fiber tractography, were presented to support the improvement. The cross-phantom test on synthetic data generated by the Phantomas tool was performed to further evaluate our model. Extended experiments revealed that GCNN might serve as a new way of enhancing dMRI.