WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising
arxiv(2024)
摘要
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.
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