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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 .
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