Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction

• URL: http://arxiv.org/abs/2307.12717v2
• Date: Fri, 28 Jul 2023 12:54:24 GMT
• Title: Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction
• Authors: Wangduo Xie, Matthew B.Blaschko
• Abstract summary: We propose a novel Dense Transformer based Enhanced Coding Network (DTEC-Net) for unsupervised metal artifact reduction. Specifically, we introduce a Hierarchical Disentangling, supported by the high-order dense process, and transformer to obtain densely encoded sequences. Experiments and model discussions illustrate DTEC-Net's effectiveness, which outperforms the previous state-of-the-art methods on a benchmark dataset.
• Score: 27.800525536048944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: CT images corrupted by metal artifacts have serious negative effects on clinical diagnosis. Considering the difficulty of collecting paired data with ground truth in clinical settings, unsupervised methods for metal artifact reduction are of high interest. However, it is difficult for previous unsupervised methods to retain structural information from CT images while handling the non-local characteristics of metal artifacts. To address these challenges, we proposed a novel Dense Transformer based Enhanced Coding Network (DTEC-Net) for unsupervised metal artifact reduction. Specifically, we introduce a Hierarchical Disentangling Encoder, supported by the high-order dense process, and transformer to obtain densely encoded sequences with long-range correspondence. Then, we present a second-order disentanglement method to improve the dense sequence's decoding process. Extensive experiments and model discussions illustrate DTEC-Net's effectiveness, which outperforms the previous state-of-the-art methods on a benchmark dataset, and greatly reduces metal artifacts while restoring richer texture details.

Related papers

• MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images [53.62335292022492]
  Metal teeth implants could bring annoying metal artifacts during the CBCT imaging process. We develop an efficient Transformer to perform metal artifacts reduction (MAR) from dental CBCT images. A Patch-wise Perceptive Feed Forward Network (P2FFN) is also proposed to perceive local image information for fine-grained restoration.
  arXiv  Detail & Related papers  (2023-11-16T06:02:03Z)
• Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction [48.1445005916672]
  We present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body. Our Polyner achieves comparable or better performance than supervised methods on in-domain datasets.
  arXiv  Detail & Related papers  (2023-06-27T04:50:58Z)
• RetinexFlow for CT metal artifact reduction [10.612456151972113]
  Metal artifacts is a major challenge in computed tomography (CT) imaging. In this work, we formulate metal artifacts reduction problem as a combination of decomposition and completion tasks. We propose RetinexFlow, which is a novel end-to-end image domain model based on Retinex theory and conditional normalizing flow.
  arXiv  Detail & Related papers  (2023-06-18T10:53:52Z)
• Continual Detection Transformer for Incremental Object Detection [154.8345288298059]
  Incremental object detection (IOD) aims to train an object detector in phases, each with annotations for new object categories. As other incremental settings, IOD is subject to catastrophic forgetting, which is often addressed by techniques such as knowledge distillation (KD) and exemplar replay (ER) We propose a new method for transformer-based IOD which enables effective usage of KD and ER in this context.
  arXiv  Detail & Related papers  (2023-04-06T14:38:40Z)
• 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)
• 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)
• 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)
• M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection [74.19291916812921]
  forged images generated by Deepfake techniques pose a serious threat to the trustworthiness of digital information. In this paper, we aim to capture the subtle manipulation artifacts at different scales for Deepfake detection. We introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods.
  arXiv  Detail & Related papers  (2021-04-20T05:43:44Z)
• Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction [17.01644053979103]
  An artifact disentanglement network (ADN) was proposed with unpaired clinical images directly, producing promising results on clinical datasets. We propose a low-dimensional manifold (LDM) constrained disentanglement network (DN), leveraging the image characteristics that the patch manifold is generally low-dimensional. We show that the proposed LDM-DN approach can consistently improve the MAR performance in paired and/or unpaired learning settings.
  arXiv  Detail & Related papers  (2020-07-08T03:47:34Z)
• 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.