Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction

• URL: http://arxiv.org/abs/2406.12186v2
• Date: Thu, 20 Jun 2024 09:09:43 GMT
• Title: Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction
• Authors: Xinquan Yang, Guanqun Zhou, Wei Sun, Youjian Zhang, Zhongya Wang, Jiahui He, Zhicheng Zhang,
• Abstract summary: In computed tomography (CT), the presence of metallic implants in patients often leads to disruptive artifacts in the reconstructed images. In this paper, we have discovered that the uncertainty image computed from the restoration result of initial training weights can effectively highlight high-frequency regions. We propose an uncertainty constraint (UC) loss that utilizes the uncertainty image as an adaptive weight to guide the MAR network to focus on the metal artifact region.
• Score: 7.4965536794782945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In computed tomography (CT), the presence of metallic implants in patients often leads to disruptive artifacts in the reconstructed images, hindering accurate diagnosis. Recently, a large amount of supervised deep learning-based approaches have been proposed for metal artifact reduction (MAR). However, these methods neglect the influence of initial training weights. In this paper, we have discovered that the uncertainty image computed from the restoration result of initial training weights can effectively highlight high-frequency regions, including metal artifacts. This observation can be leveraged to assist the MAR network in removing metal artifacts. Therefore, we propose an uncertainty constraint (UC) loss that utilizes the uncertainty image as an adaptive weight to guide the MAR network to focus on the metal artifact region, leading to improved restoration. The proposed UC loss is designed to be a plug-and-play method, compatible with any MAR framework, and easily adoptable. To validate the effectiveness of the UC loss, we conduct extensive experiments on the public available Deeplesion and CLINIC-metal dataset. Experimental results demonstrate that the UC loss further optimizes the network training process and significantly improves the removal of metal artifacts.

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 CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains [8.40564813751161]
  metallic implants often cause disruptive artifacts in computed tomography (CT) images, impeding accurate diagnosis. Several supervised deep learning-based approaches have been proposed for reducing metal artifacts (MAR) We propose an unsupervised MAR method based on the diffusion model, a generative model with a high capacity to represent data distributions.
  arXiv  Detail & Related papers  (2023-08-31T14:00:47Z)
• 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)
• 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)
• 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)
• 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)
• High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm [70.50499513259322]
  Current artifact detection pipelines are resource-hungry and rely heavily on hand-crafted features. We propose E4G, a deep learning framework for high frequency EEG artifact detection. Our framework exploits the early exit paradigm, building an implicit ensemble of models capable of capturing uncertainty.
  arXiv  Detail & Related papers  (2021-07-21T07:05:42Z)
• Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN [36.1921415839058]
  Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT) Here we propose a much simpler and much effective unsupervised MAR method for CT.
  arXiv  Detail & Related papers  (2020-07-07T14:11:47Z)
• Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework [1.1374919153601266]
  Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In MRI, no method has yet been introduced to correct the susceptibility artefact. We propose an unsupervised deep learning method for multimodal MAR.
  arXiv  Detail & Related papers  (2020-04-20T14:12:00Z)
• 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.