MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

Abstract

Low-dose computed tomography (LDCT) is crucial due to the risk of radiation exposure to patients. However, the high noise level in LDCT images may reduce the image quality, leading to a less accurate diagnosis. Deep learning technology, especially supervised methods, has recently been widely accepted as a powerful tool for LDCT image denoising tasks. However, supervised methods require numerous paired datasets of LDCT and high-quality pristine CT images, which are rarely available in real-world clinical scenarios. This study presents an unsupervised learning-based framework called MM-Net, consisting of two training steps for a volumetric LDCT denoising task. In the two-step training approach, we first train the initial denoising network multi-scale attention U-Net (MSAU-Net) in a self-supervised manner to predict the noise-suppressed center slice with a neighboring multi-slice input. The second training step aims to train the U-Net-based final denoiser based on the pre-trained MSAU-Net to improve the image quality by introducing new multi-patch and multi-mask matching loss. Qualitative visual inspection and quantitative measures across texturally different domains of clinical and animal data reveal that the proposed MM-Net outperformed all competing state-ofthe-art unsupervised algorithms. The unsupervised method also achieved denoising performance comparable to the representative supervised methods trained with ground truth images.

Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

“…Their model was able to improve the quality of noise-reduced CT images compared to the state-of-the-art methods. Jeon et al [40] developed the MM-Net, a novel unsupervised denoising method; consisting of two training steps. The first is to predict the noise-suppressed middle frame with neighboring multi-frame input by training the initial denoising network Multi-Scale Attention U-Net (MSAU-Net) in a self-supervised manner.…”