Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images

• URL: http://arxiv.org/abs/2009.07469v1
• Date: Wed, 16 Sep 2020 04:43:35 GMT
• Title: Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
• Authors: Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Lei Xing
• Abstract summary: Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts. We propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques.
• Score: 29.019325663195627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this paper, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.

Related papers

• End-to-End Model-based Deep Learning for Dual-Energy Computed Tomography Material Decomposition [53.14236375171593]
  We propose a deep learning procedure called End-to-End Material Decomposition (E2E-DEcomp) for quantitative material decomposition. We show the effectiveness of the proposed direct E2E-DEcomp method on the AAPM spectral CT dataset.
  arXiv  Detail & Related papers  (2024-06-01T16:20:59Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• 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)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• 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)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• 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)
• Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning [30.977044473457]
  We present a novel deep-learning-based approach for metal artifact reduction (MAR) We train a neural network to restore the metal trace region values in the given metal-free sinogram. We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results.
  arXiv  Detail & Related papers  (2021-09-28T04:40:57Z)
• DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction [15.225899631788973]
  Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images. Several network models have been proposed for metal artifact reduction (MAR) in CT. We present a novel Dual-domain Adaptive-scaling Non-local network (DAN-Net) for MAR.
  arXiv  Detail & Related papers  (2021-02-16T08:09:16Z)
• Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography [21.5885187197634]
  Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain. We propose to address the problem by (1) retaining the metal-affected regions in sinogram and (2) replacing the binarized metal trace with the metal mask projection. Our novel network yields more precise artifact-reduced images than the state-of-the-art approaches, especially when metallic objects are large.
  arXiv  Detail & Related papers  (2020-01-02T06:39:06Z)

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.