SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap

TL;DR

SR4ZCT is a novel self-supervised approach that enhances through-plane resolution in CT images with arbitrary resolution and overlap, improving diagnostic accuracy without requiring labeled data.

Contribution

It introduces a flexible self-supervised method capable of handling any resolution and overlap combinations, explicitly modeling relationships between resolutions for accurate training.

Findings

Effective in real-world CT datasets

Handles arbitrary resolution and overlap configurations

Improves through-plane resolution for better diagnosis

Abstract

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.