IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising

TL;DR

This paper introduces IPv2, an improved image purification strategy for ultra-low-dose lung CT denoising, enhancing structural preservation and noise suppression in both background and lung tissue regions.

Contribution

The paper systematically redesigns the original image purification strategy, adding modules for background removal and noise handling, to improve denoising performance in ultra-low-dose lung CT images.

Findings

IPv2 improves background suppression in lung CT images.

IPv2 enhances lung parenchyma restoration across multiple models.

Demonstrated effectiveness on real-world low-dose lung CT dataset.

Abstract

The image purification strategy constructs an intermediate distribution with aligned anatomical structures, which effectively corrects the spatial misalignment between real-world ultra-low-dose CT and normal-dose CT images and significantly enhances the structural preservation ability of denoising models. However, this strategy exhibits two inherent limitations. First, it suppresses noise only in the chest wall and bone regions while leaving the image background untreated. Second, it lacks a dedicated mechanism for denoising the lung parenchyma. To address these issues, we systematically redesign the original image purification strategy and propose an improved version termed IPv2. The proposed strategy introduces three core modules, namely Remove Background, Add noise, and Remove noise. These modules endow the model with denoising capability in both background and lung tissue regions during training data construction and provide a more reasonable evaluation protocol through refined label construction at the testing stage. Extensive experiments on our previously established real-world patient lung CT dataset acquired at 2% radiation dose demonstrate that IPv2 consistently improves background suppression and lung parenchyma restoration across multiple mainstream denoising models. The code is publicly available at https://github.com/MonkeyDadLufy/Image-Purification-Strategy-v2.