Interior photon-counting CT data denoising via multi-agent reinforcement learning

Interior photon-counting computed tomography (PCCT) scans are essential for obtaining high-resolution images at minimal radiation dose by focusing only on a region of interest. However, designing a deep learning model for denoising a PCCT interior scan is rather challenging. Recently, several studies explored deep reinforcement learning (RL)-based models with far fewer parameters than those typical for supervised and self-learning models. Such an RL model can be effectively trained on a small dataset, and yet be generalizable and interpretable. In this work, we design an RL model to perform multichannel PCCT scan denoising. Because a reliable reward function is crucial for optimizing the RL model, we focus on designing a small denoising autoencoder-based reward network to learn the latent representation of full-dose simulated PCCT data and use the reconstruction error to quantify the reward. We also use domain-specific batch normalization for unsupervised domain adaptation with a limited amount of multichannel PCCT data. Our results show that the proposed model achieves excellent denoising results, with a significant potential for clinical and preclinical PCCT denoising.