Real-World Low-Dose CT Image Denoising by Patch Similarity Purification

Abstract

Reducing the radiation dose in CT scanning is important to alleviate the damage to the human health in clinical scenes. A promising way is to replace the normal-dose CT (NDCT) imaging by low-dose CT (LDCT) imaging with lower tube voltage and tube current. This often brings severe noise to the LDCT images, which adversely affects the diagnosis accuracy. Most of existing LDCT image denoising networks are trained either with synthetic LDCT images or real-world LDCT and NDCT image pairs with huge spatial misalignment. However, the synthetic noise is very different from the complex noise in real-world LDCT images, while the huge spatial misalignment brings inaccurate predictions of tissue structures in the denoised LDCT images. To well utilize real-world LDCT and NDCT image pairs for LDCT image denoising, in this paper, we introduce a new Patch Similarity Purification (PSP) strategy to construct high-quality training dataset for network training. Specifically, our PSP strategy first perform binarization for each pair of image patches cropped from the corresponding LDCT and NDCT image pairs. For each pair of binary masks, it then computes their similarity ratio by common mask calculation, and the patch pair can be selected as a training sample if their mask similarity ratio is higher than a threshold. By using our PSP strategy, each training set of our Rabbit and Patient datasets contain hundreds of thousands of real-world LDCT and NDCT image patch pairs with negligible misalignment. Extensive experiments demonstrate the usefulness of our PSP strategy on purifying the training data and the effectiveness of training LDCT image denoising networks on our datasets. The code and dataset are provided at https://github.com/TuTusong/PSP.