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.
- Linux
- Python 3.7
- CPU or NVIDIA GPU + CUDA CuDNN
- Clone this repo:
git clone https://github.com/zhaohaoyu376/morestyle
cd wiaThe 2016 AAPM-Mayo dataset can be downloaded from: CT Clinical Innovation Center
The 2020 AAPM-Mayo dataset can be downloaded from: cancer imaging archive
- train the model:
python train_wavelet.py - test the model:
python test.pyIf you use this code for your research, please cite our papers.
@article{zhao2024wia,
title={WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising},
author={Zhao, Haoyu and Liang, Guyu and Zhao, Zhou and Du, Bo and Xu, Yongchao and Yu, Rui},
journal={arXiv preprint arXiv:2403.11672},
year={2024}
}
Our code is inspired by pytorch-CycleGAN-and-pix2pix.