Diffusion Probabilistic Models-based Noise Reduction for Enhancing the Quality of Medical Images

The quality of medical images is critical for Computer-aided diagnosis (CAD) and Image-guided robotic interventions because accurate and high-quality images are required to perform each task. High resolution and Signal-to-Noise Ratio (SNR) images are required to analyze and navigate the robotic instruments to the accurate localization inside the body. However, medical images are often of substantially lower quality than clean photographic images due to various factors. In this study we focus on a post-processing based strategy for reducing the amount of noise in MRI images. We propose a method based on Denoising Diffusion Probablistic Models (DDPM), also known as diffusion models the reduce the amount of noise in the image. Specifically, a two-stage DDPM method is proposed – estimating the amount of noise and designating to the correct stage in the Marchov Chain in the reverse diffusion operation, and iteratively and gradually reducing noise by reversing the process. Our experiment was performed on an actually scanned images on a clinical MR scanner, with the reference image that were averaged to match the SNR. Our quantitative and qualitative comparison shows that our method outperforms previous methods including supervised training based on two different metrics (SSIM, PSNR). It demonstrates the effectiveness of the DDPM-based method in reducing noise in the image. Moreover, the resulting image quality achieved with the proposed approach shows that tissue sub-structures are clearer. The noise reduction performance of the proposed method for multiple adjacent slices and various contrasts was tested to show the modelś ability to reduce noise across a diverse set of imaging conditions, which is essential in real-world scenarios.