Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness
This work addresses the limitation of low spatial resolution in MRSI for medical imaging applications, offering a more efficient and flexible approach, though it is incremental as it builds on existing deep learning methods.
The authors tackled the problem of low spatial resolution in Magnetic Resonance Spectroscopic Imaging (MRSI) by developing a multi-scale super-resolution method using a Filter Scaling strategy and a Multi-Conditional Module, achieving the best performance among several methods and enabling adjustable sharpness in super-resolved metabolic maps.
Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial resolutions. The existing deep learning-based MRSI super-resolution methods require training a separate network for each upscaling factor, which is time-consuming and memory inefficient. We tackle this multi-scale super-resolution problem using a Filter Scaling strategy that modulates the convolution filters based on the upscaling factor, such that a single network can be used for various upscaling factors. Observing that each metabolite has distinct spatial characteristics, we also modulate the network based on the specific metabolite. Furthermore, our network is conditioned on the weight of adversarial loss so that the perceptual sharpness of the super-resolved metabolic maps can be adjusted within a single network. We incorporate these network conditionings using a novel Multi-Conditional Module. The experiments were carried out on a 1H-MRSI dataset from 15 high-grade glioma patients. Results indicate that the proposed network achieves the best performance among several multi-scale super-resolution methods and can provide super-resolved metabolic maps with adjustable sharpness.