2024-01-23-dey24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
MTSR-MRI: Combined Modality Translation and Super-Resolution of Magnetic Resonance Images	Magnetic resonance imaging (MRI) is a common non-invasive imaging technique with high soft tissue contrast. Different MRI modalities are used for the diagnosis of various conditions including T1-weighted and T2-weighted MRI. In this paper, we introduce MTSR-MRI, a novel method that can not only upscale low-resolution scans but also translates between the T1-weighted and T2-weighted modalities. This will potentially reduce the scan time or repeat scans by taking low-resolution inputs in one modality and returning plausible high-resolution output in another modality. Due to the ambiguity that persists in image-to-image translation tasks, we consider the distribution of possible outputs in a conditional generative setting. The mapping is distilled in a low-dimensional latent distribution which can be randomly sampled at test time, thus allowing us to generate multiple plausible high-resolution outputs from a given low-resolution input. We validate the proposed method on the BraTS-18 dataset qualitatively and quantitatively using a variety of similarity measures. The implementation of this work will be available at https://github.com/AvirupJU/MTSR-MRI .	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	dey24a	0	MTSR-MRI: Combined Modality Translation and Super-Resolution of Magnetic Resonance Images	743	757	743-757	743	false	Dey, Avirup and Ebrahimi, Mehran	given family Avirup Dey given family Mehran Ebrahimi	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/dey24a/dey24a.pdf