Deep Learning-Based Super Resolution Reconstruction for Quantitative Susceptibility Mapping

Motivation: Lengthy acquisitions are needed to produce high-quality quantitative susceptibility mapping (QSM) from which it is possible to segment vasculature and extract physiological parameters. Goal(s): To adapt a deep learning method for super-resolution reconstruction to enhance QSM images. Approach: We applied the 3D densely-connected super resolution network (DCSRN) to QSM data, as it has previously shown promising results in reconstructing T1w high-resolution (HR) images from low-resolution (LR) images. Results: We demonstrated an improvement in the reconstruction of the vascular network, with intravascular susceptibility values distribution close to the true distribution. Impact: Our results show the promise of DCSRN architecture in producing super resolution (SR) images from low resolution (LR) images. Furthermore, the feasibility of segmenting vessels and extracting venous OEF on SR would be beneficial for studies of brain vasculature.