Computed tomography (CT) is a widely used non-invasive medical imaging technique for disease diagnosis. The diagnostic accuracy is often affected by image resolution, which can be insufficient in practice. For medical CT images, the through-plane resolution is often worse than the in-plane resolution and there can be overlap between slices, causing difficulties in diagnoses. Self-supervised methods for through-plane resolution enhancement, which train on in-plane images and infer on through-plane images, have shown promise for both CT and MRI imaging. However, existing self-supervised methods either neglect overlap or can only handle specific cases with fixed combinations of resolution and overlap. To address these limitations, we propose a self-supervised method called SR4ZCT. It employs the same off-axis training approach while being capable of handling arbitrary combinations of resolution and overlap. Our method explicitly models the relationship between resolutions and voxel spacings of different planes to accurately simulate training images that match the original through-plane images. We highlight the significance of accurate modeling in self-supervised off-axis training and demonstrate the effectiveness of SR4ZCT using a real-world dataset.

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Lecture Notes in Computer Science
14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023
Computational Imaging

Shi, J., Pelt, D., & Batenburg, J. (2023). SR4ZCT: Self-supervised through-plane resolution enhancement for CT images with arbitrary resolution and overlap. In Machine Learning in Medical Imaging (pp. 52–61). doi:10.1007/978-3-031-45673-2_6