Training a low-dose CT denoising network with only low-dose CT dataset: Comparison of DDLN and Noise2Void

摘要

The radiation risk of X-ray CT gained increasing concern in the past decades. Lowering CT scan dose leads to noisy raw data as well as streak artifacts after reconstruction. Extensive studies have been conducted to reduce noise and artifacts for low-dose CT (LDCT). As deep learning has achieved great success in computer vision tasks, it also become a powerful tool in LDCT denoising. Commonly used deep learning methods such as supervised learning and generative adversarial learning have a strong dependence on large normal-dose CT (NDCT) dataset. While in real cases, the NDCT dataset is often expensive or not accessible, which limits the implementation of deep learning. In recent studies, multiple deep learning methods have been proposed for LDCT denoising without NDCT data. Among them, a popular type of methods is noisy label training (NLT) which use LDCT data as labels for network supervised training. Noise2Void is an easily implementable and representative method of NLT and has achieved great results in pixel-independent noise denoising. Another type is distribution learning methods which reduce LDCT noise-level by learning NDCT distribution. Deep distribution learning from noisy samples (DDLN) learns the NDCT distribution from LDCT data only and adopts MAP estimation for LDCT denoising with the learned NDCT distribution prior. It is effective for LDCT projection data denoising. In this work, the two representative methods are compared for LDCT projection data denoising under different noise-levels to seek for their suitable application scenarios.