Abstract: Hyperpolarized 129Xe magnetic resonance imaging is an emerging medical imaging technique that plays an important role in the diagnosis and treatment of numerous lung diseases. However, the noise generated during the acquisition process affects the data quality and limits the reliability of the technique in clinical diagnosis and treatment. In this paper, we propose a multimodal feature-enhanced conditional diffusion model based on deep learning that aims to remove noise and improve image quality. The model inputs acquired 1H MRI as constraints, and a multimodal feature enhancement module is specially designed, which aims to enhance the effectiveness of the model in exploiting multimodal information and the sensitivity to changes in local details of the image. The experimental results show that the method has the best denoising performance and detail preservation compared to other methods, and demonstrate in a ventilation defect segmentation task that the method can enhance the reliability of 129Xe MRI in clinical practice.

Key words: diffusion model, hyperpolarized 129Xe MRI, multi-modal, image denoising, deep learning