Incremental Cross-View Mutual Distillation for Self-Supervised Medical CT Synthesis

Abstract

Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low within-slice resolution. Improving the inter-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds a novel medical slice synthesis to increase the inter-slice resolution.

Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy to accomplish this task in the self-supervised learning manner.

Methodology

Specifically, we model this problem from three different views:

1. Slice-wise interpolation from axial view
2. Pixel-wise interpolation from coronal view
3. Pixel-wise interpolation from sagittal view

Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other. We can repeat this process to make the models synthesize intermediate slice data with increasing between-slice resolution.

Experimental Results

To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on a large-scale CT dataset. Quantitative and qualitative comparison results show that our method outperforms state-of-the-art algorithms by clear margins.

The incremental cross-view mutual distillation strategy successfully enables self-supervised learning for medical CT synthesis, providing a practical solution for improving inter-slice resolution without requiring ground-truth intermediate slices.