@inproceedings{9a50023385754365ae60d1aab0f451f0,
title = "Multi-scale Super-Resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness",
abstract = "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 a1H-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. Our code is available at https://github.com/dsy199610/Multiscale-SR-MRSI-adjustable-sharpness.",
keywords = "Brain MRSI, Network conditioning, Super-resolution",
author = "Siyuan Dong and Gilbert Hangel and Wolfgang Bogner and Georg Widhalm and Karl R{\"o}ssler and Siegfried Trattnig and Chenyu You and \{de Graaf\}, Robin and Onofrey, \{John A.\} and Duncan, \{James S.\}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2022",
doi = "10.1007/978-3-031-16446-0\_39",
language = "English",
isbn = "9783031164453",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "410--420",
editor = "Linwei Wang and Qi Dou and Fletcher, \{P. Thomas\} and Stefanie Speidel and Shuo Li",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings",
}