bibtext: @InProceedings{ Zha_WIALD2ND_MICCAI2024,
                   author = { Zhao, Haoyu and Gu, Yuliang and Zhao, Zhou and Du, Bo and Xu, Yongchao and Yu, Rui },
                   title = { { WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: In clinical examinations and diagnoses, low-dose computed tomography (LDCT) is crucial for minimizing health risks compared with normal-dose computed tomography (NDCT). However, reducing the radiation dose compromises the signal-to-noise ratio, leading to degraded quality of CT images. To address this, we analyze LDCT denoising task based on experimental results from the frequency perspective, and then introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. The proposed WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM). First, WIA is introduced to align NDCT with LDCT by mainly adding noise to the high-frequency components, which is the main difference between LDCT and NDCT. Second, to better capture high-frequency components and detailed information, Frequency-Aware Multi-scale Loss (FAM) is proposed by effectively utilizing multi-scale feature space. Extensive experiments on two public LDCT denoising datasets demonstrate that our WIA-LD2ND, only uses NDCT, outperforms existing several state-of-the-art weakly-supervised and self-supervised methods.
title: WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising
authors: Zhao, Haoyu Gu, Yuliang Zhao, Zhou Du, Bo Xu, Yongchao Yu, Rui