InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

• URL: http://arxiv.org/abs/2112.12660v1
• Date: Thu, 23 Dec 2021 15:52:37 GMT
• Title: InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images
• Authors: Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Meng and Yefeng Zheng
• Abstract summary: 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.
• Score: 53.4351366246531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During the computed tomography (CT) imaging process, metallic implants within patients always cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based methods have achieved promising performance. However, most of them share two main common limitations: 1) the CT physical imaging geometry constraint is not comprehensively incorporated into deep network structures; 2) the entire framework has weak interpretability for the specific MAR task; hence, the role of every network module is difficult to be evaluated. To alleviate these issues, in the paper, we construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. Concretely, we derive a joint spatial and Radon domain reconstruction model and propose an optimization algorithm with only simple operators for solving it. By unfolding the iterative steps involved in the proposed algorithm into the corresponding network modules, we easily build the InDuDoNet+ with clear interpretability. Furthermore, 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. Comprehensive experiments on synthesized data and clinical data substantiate the superiority of the proposed methods as well as the superior generalization performance beyond the current state-of-the-art (SOTA) MAR methods. Code is available at \url{https://github.com/hongwang01/InDuDoNet_plus}.

Related papers

• ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising [23.274928463320986]
  We propose a novel Anatomy-aware Supervised CONtrastive learning framework, termed ASCON, to explore the anatomical semantics for low-dose CT denoising. Our ASCON provides anatomical interpretability for low-dose CT denoising for the first time.
  arXiv  Detail & Related papers  (2023-07-23T04:36:05Z)
• MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images [29.458632068296854]
  We propose a model-driven equivariant proximal network, called MEPNet. MEPNet is optimization-inspired and has a clear working mechanism. We will release the code at urlhttps://github.com/hongwang01/MEPNet.
  arXiv  Detail & Related papers  (2023-06-25T15:50:11Z)
• TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction [7.959841510571622]
  We propose a novel triple domain model-driven CTMAR network, termed as TriDoNet. We encode non-local repetitive streaking patterns of metal artifacts as an explicit tight frame sparse representation model with adaptive thresholds. Experimental results show that our TriDoNet can generate superior artifact-reduced CT images.
  arXiv  Detail & Related papers  (2022-11-14T08:28:57Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction [62.691996239590125]
  We propose an adaptive convolutional dictionary network (ACDNet) for metal artifact reduction. Our ACDNet can automatically learn the prior for artifact-free CT images via training data and adaptively adjust the representation kernels for each input CT image. Our method inherits the clear interpretability of model-based methods and maintains the powerful representation ability of learning-based methods.
  arXiv  Detail & Related papers  (2022-05-16T06:49:36Z)
• Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism [4.998352078907441]
  Low radiation dose may result in increased noise and artifacts, which greatly affected the clinical diagnosis. To obtain high-quality Total-body Low-dose CT (LDCT) images, previous deep-learning-based research work has introduced various network architectures. In this paper, we propose a novel intra-task knowledge transfer method that leverages the distilled knowledge from NDCT images.
  arXiv  Detail & Related papers  (2021-12-01T06:46:38Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• 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)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)

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.