  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.
