S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement

TL;DR

This paper introduces S2MS, a self-supervised learning framework for multi-spectral CT image enhancement that effectively reduces noise by leveraging spectral data without requiring clean training labels.

Contribution

The novel S2MS framework utilizes self-supervised learning with noisy spectral CT images, eliminating the need for clean datasets and improving noise suppression and detail preservation.

Findings

S2MS outperforms traditional DL models in noise reduction.

The method effectively preserves image details.

Simulation results demonstrate potential clinical benefits.

Abstract

Photon counting spectral CT (PCCT) can produce reconstructed attenuation maps in different energy channels, reflecting energy properties of the scanned object. Due to the limited photon numbers and the non-ideal detector response of each energy channel, the reconstructed images usually contain much noise. With the development of Deep Learning (DL) technique, different kinds of DL-based models have been proposed for noise reduction. However, most of the models require clean data set as the training labels, which are not always available in medical imaging field. Inspiring by the similarities of each channel's reconstructed image, we proposed a self-supervised learning based PCCT image enhancement framework via multi-spectral channels (S2MS). In S2MS framework, both the input and output labels are noisy images. Specifically, one single channel image was used as output while images of other single channels and channel-sum image were used as input to train the network, which can fully use the spectral data information without extra cost. The simulation results based on the AAPM Low-dose CT Challenge database showed that the proposed S2MS model can suppress the noise and preserve details more effectively in comparison with the traditional DL models, which has potential to improve the image quality of PCCT in clinical applications.