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Existing medical image super-resolution methods rely on pairs of low- andhigh- resolution images to learn a mapping in a fully supervised manner.However, such image pairs are often not available in clinical practice. In thispaper, we address super-resolution problem in a real-world scenario usingunpaired data and synthesize linearly \textbf{eight times} higher resolvedMicro-CT images of temporal bone structure, which is embedded in the inner ear.We explore cycle-consistency generative adversarial networks forsuper-resolution task and equip the translation approach with Bayesianinference. We further introduce \emph{Hu Moment distance} the evaluation metricto quantify the shape of the temporal bone. We evaluate our method on a publicinner ear CT dataset and have seen both visual and quantitative improvementover state-of-the-art deep-learning-based methods. In addition, we perform amulti-rater visual evaluation experiment and find that trained expertsconsistently rate the proposed method the highest quality scores among allmethods. Furthermore, we are able to quantify uncertainty in the unpairedtranslation task and the uncertainty map can provide structural information ofthe temporal bone.

Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference