Noise Entangled GAN For Low-Dose CT Simulation
- URL: http://arxiv.org/abs/2102.09615v1
- Date: Thu, 18 Feb 2021 21:04:32 GMT
- Title: Noise Entangled GAN For Low-Dose CT Simulation
- Authors: Chuang Niu, Ge Wang, Pingkun Yan, Juergen Hahn, Youfang Lai, Xun Jia,
Arjun Krishna, Klaus Mueller, Andreu Badal, KyleJ. Myers, Rongping Zeng
- Abstract summary: Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image.
We present two schemes to generate a clean CT image and a noise image from the high-dose CT image.
An NE-GAN is proposed to simulate different levels of low-dose CT images, where the level of generated noise can be continuously controlled by a noise factor.
- Score: 32.3869284562502
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed
tomography (CT) images from a higher dose CT image. First, we present two
schemes to generate a clean CT image and a noise image from the high-dose CT
image. Then, given these generated images, an NE-GAN is proposed to simulate
different levels of low-dose CT images, where the level of generated noise can
be continuously controlled by a noise factor. NE-GAN consists of a generator
and a set of discriminators, and the number of discriminators is determined by
the number of noise levels during training. Compared with the traditional
methods based on the projection data that are usually unavailable in real
applications, NE-GAN can directly learn from the real and/or simulated CT
images and may create low-dose CT images quickly without the need of raw data
or other proprietary CT scanner information. The experimental results show that
the proposed method has the potential to simulate realistic low-dose CT images.
Related papers
- WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising [74.14134385961775]
We introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data.
WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)
arXiv Detail & Related papers (2024-03-18T11:20:11Z) - Low-Dose CT Image Reconstruction by Fine-Tuning a UNet Pretrained for
Gaussian Denoising for the Downstream Task of Image Enhancement [3.7960472831772765]
Computed Tomography (CT) is a widely used medical imaging modality, and reconstruction from low-dose CT data is a challenging task.
In this paper, we propose a less complex two-stage method for reconstruction of LDCT images.
The proposed method achieves a shared top ranking in the LoDoPaB-CT challenge and a first position with respect to the SSIM metric.
arXiv Detail & Related papers (2024-03-06T08:51:09Z) - Rotational Augmented Noise2Inverse for Low-dose Computed Tomography
Reconstruction [83.73429628413773]
Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth.
We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN)
Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
arXiv Detail & Related papers (2023-12-19T22:40:51Z) - Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders
(DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) [68.8204255655161]
Cycle Consistent Generative Adversarial Network (GAN) is implemented to yield high-field, high resolution, high signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images.
Images were utilized to train a Denoising Autoencoder (DAE) and a Cycle-GAN, with paired and unpaired cases.
This work demonstrates the use of a generative deep learning model that can outperform classical DAEs to improve low-field MRI images and does not require image pairs.
arXiv Detail & Related papers (2023-07-12T00:01:00Z) - Deep learning network to correct axial and coronal eye motion in 3D OCT
retinal imaging [65.47834983591957]
We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan.
The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
arXiv Detail & Related papers (2023-05-27T03:55:19Z) - Self-supervised Physics-based Denoising for Computed Tomography [2.2758845733923687]
Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation.
Lowering the radiation dose reduces the health risks but leads to noisier measurements, which decreases the tissue contrast and causes artifacts in CT images.
Modern deep learning noise suppression methods alleviate the challenge but require low-noise-high-noise CT image pairs for training.
We introduce a new self-supervised approach for CT denoising Noise2NoiseTD-ANM that can be trained without the high-dose CT projection ground truth images.
arXiv Detail & Related papers (2022-11-01T20:58:50Z) - Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images.
We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem.
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
arXiv Detail & Related papers (2022-03-29T08:41:17Z) - Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A
Simulation Study [4.7849095200575045]
Radiomic features are calculated from noisy images, such as low dose computed tomography (CT) scans.
In this article, we investigate the possibility of improving the radiomic features calculated on noisy CTs by using generative models for denoising.
The results show that denoising using encoder-decoder networks (EDN) and conditional generative adversarial networks (CGANs) can improve the radiomic features calculated on noisy CTs.
arXiv Detail & Related papers (2021-04-30T15:18:57Z) - Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs [7.4103922463838785]
We propose a novel self-supervised learning-based CT denoising method.
We train pre-train CT denoising and noise models that can predict CT noise from Low-dose CT (LDCT) and Normal-dose CT (NDCT) pairs.
We evaluate our method on the 2016 AAPM Low-Dose CT Grand Challenge dataset.
arXiv Detail & Related papers (2021-04-06T07:11:46Z) - A deep network for sinogram and CT image reconstruction [28.175533839713847]
In this paper, we design a deep network for sinogram and CT image reconstruction.
The network consists of two cascaded blocks that are linked by a filter backprojection layer.
Experimental results show that the reconstructed CT images have the highest PSNR and SSIM in average compared to state of the art methods.
arXiv Detail & Related papers (2020-01-20T15:50:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.