Low-Dose CT Denoising via Sinogram Inner-Structure Transformer
- URL: http://arxiv.org/abs/2204.03163v1
- Date: Thu, 7 Apr 2022 02:18:23 GMT
- Title: Low-Dose CT Denoising via Sinogram Inner-Structure Transformer
- Authors: Liutao Yang and Zhongnian, Li and Rongjun, Ge and Junyong, Zhao and
Haipeng, Si and Daoqiang Zhang
- Abstract summary: Low-Dose Computed Tomography (LDCT) technique, which reduces the radiation harm to human bodies, is attracting increasing interest in the medical imaging field.
As the image quality is degraded by low dose radiation, LDCT exams require specialized reconstruction methods or denoising algorithms.
We propose an LDCT denoising network namely Sinogram Inner-Structure Transformer (SIST) to reduce the noise by utilizing the inner-structure in the sinogram domain.
- Score: 13.65180174091348
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Low-Dose Computed Tomography (LDCT) technique, which reduces the radiation
harm to human bodies, is now attracting increasing interest in the medical
imaging field. As the image quality is degraded by low dose radiation, LDCT
exams require specialized reconstruction methods or denoising algorithms.
However, most of the recent effective methods overlook the inner-structure of
the original projection data (sinogram) which limits their denoising ability.
The inner-structure of the sinogram represents special characteristics of the
data in the sinogram domain. By maintaining this structure while denoising, the
noise can be obviously restrained. Therefore, we propose an LDCT denoising
network namely Sinogram Inner-Structure Transformer (SIST) to reduce the noise
by utilizing the inner-structure in the sinogram domain. Specifically, we study
the CT imaging mechanism and statistical characteristics of sinogram to design
the sinogram inner-structure loss including the global and local
inner-structure for restoring high-quality CT images. Besides, we propose a
sinogram transformer module to better extract sinogram features. The
transformer architecture using a self-attention mechanism can exploit
interrelations between projections of different view angles, which achieves an
outstanding performance in sinogram denoising. Furthermore, in order to improve
the performance in the image domain, we propose the image reconstruction module
to complementarily denoise both in the sinogram and image domain.
Related papers
- CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images.
The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism.
We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
arXiv Detail & Related papers (2024-03-21T15:13:36Z) - 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) - APRF: Anti-Aliasing Projection Representation Field for Inverse Problem
in Imaging [74.9262846410559]
Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse problem in imaging.
Recent works use Implicit Neural Representations (INRs) to build the coordinate-based mapping between sinograms and CT images.
We propose a self-supervised SVCT reconstruction method -- Anti-Aliasing Projection Representation Field (APRF)
APRF can build the continuous representation between adjacent projection views via the spatial constraints.
arXiv Detail & Related papers (2023-07-11T14:04:12Z) - CTformer: Convolution-free Token2Token Dilated Vision Transformer for
Low-dose CT Denoising [11.67382017798666]
Low-dose computed tomography (LDCT) denoising is an important problem in CT research.
vision transformers have shown superior feature representation ability over convolutional neural networks (CNNs)
We propose a Convolution-free Token2Token Dilated Vision Transformer for low-dose CT denoising.
arXiv Detail & Related papers (2022-02-28T02:58:16Z) - Self-Attention Generative Adversarial Network for Iterative
Reconstruction of CT Images [0.9208007322096533]
The aim of this study is to train a single neural network to reconstruct high-quality CT images from noisy or incomplete data.
The network includes a self-attention block to model long-range dependencies in the data.
Our approach is shown to have comparable overall performance to CIRCLE GAN, while outperforming the other two approaches.
arXiv Detail & Related papers (2021-12-23T19:20:38Z) - DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram
Restoration in Sparse-View CT Reconstruction [13.358197688568463]
iodine radiation in the imaging process induces irreversible injury.
Iterative models are proposed to alleviate the appeared artifacts in sparse-view CT images, but the cost is too expensive.
We propose textbfDual-textbfDomain textbfDuDoTrans to reconstruct CT image with both the enhanced and raw sinograms.
arXiv Detail & Related papers (2021-11-21T10:41:07Z) - Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image
Synthesis [65.47507533905188]
Conditional generative adversarial networks have been applied to generate synthetic histopathology images.
We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
arXiv Detail & Related papers (2021-10-27T18:54:25Z) - Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction
Network [10.09577595969254]
CNN-based approaches treat all regions of the CT image equally and can be inefficient when fine-grained structures coexist with non-uniformly distributed noises.
We propose a Structure-preserving Kernel Prediction Network (StructKPN) that combines the kernel prediction network with a structure-aware loss function.
Our approach achieved superior performance on both synthetic and non-synthetic datasets, and better preserves structures that are highly desired in clinical screening and low-dose protocol optimization.
arXiv Detail & Related papers (2021-05-31T07:42:21Z) - Regularization by Denoising Sub-sampled Newton Method for Spectral CT
Multi-Material Decomposition [78.37855832568569]
We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT.
In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function.
We show numerical and experimental results for spectral CT materials decomposition.
arXiv Detail & Related papers (2021-03-25T15:20:10Z) - Interpolation of CT Projections by Exploiting Their Self-Similarity and
Smoothness [6.891238879512674]
The proposed algorithm exploits the self-similarity and smoothness of the sinogram.
Experiments with simulated and real CT data show that sinogram with the proposed algorithm leads to a substantial improvement in the quality of the reconstructed image.
arXiv Detail & Related papers (2021-03-05T22:41:25Z) - Limited-angle tomographic reconstruction of dense layered objects by
dynamical machine learning [68.9515120904028]
Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem.
Regularizing priors are necessary to reduce artifacts by improving the condition of such problems.
We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
arXiv Detail & Related papers (2020-07-21T11:48:22Z)
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.