Self-Supervised Denoising with Noise Propagation Model: Improving Material Decomposition in Photon-Counting CT.

The inherent spectral properties of photon-counting computed tomography (PCCT) allow detailed material identification through decomposition techniques, but these methods often amplify image noise and artifacts. Current denoising approaches mainly focus on improving already degraded images, ignoring the fundamental noise caused by random variations in photon detection. To tackle these issues, we combine a physics-based noise analysis with deep learning to control noise during the material decomposition process. Our work has three key parts: (1) A noise analysis model that explains how random photon-count variations in the detector affect the noise levels in different materials after decomposition. This model connects the Poisson-distributed detector noise to material-specific noise patterns. (2) A self-supervised training method that combines the noise model with neural networks using probability-based optimization, allowing the system to learn from limited training data without needing high-quality data. (3) A flexible image improvement system that adapts to different body structures and noise conditions, ensuring reliable results across various scanning scenarios. Tests using real patient scan data show our method better preserves material accuracy and produces cleaner virtual monochromatic images compared to traditional approaches. Importantly, our solution works effectively with small training datasets and can be practically used in hospital settings without slowing down workflows. This research bridges the gap between theoretical noise analysis and clinical medical imaging needs, offering a balanced approach to improving PCCT technology.