Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework

• URL: http://arxiv.org/abs/2004.09321v1
• Date: Mon, 20 Apr 2020 14:12:00 GMT
• Title: Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
• Authors: Marta B.M. Ranzini, Irme Groothuis, Kerstin Kl\"aser, M. Jorge Cardoso, Johann Henckel, S\'ebastien Ourselin, Alister Hart, Marc Modat
• Abstract summary: 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.
• Score: 1.1374919153601266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown effective but limited in generalisability, as they mostly rely on synthetic data. In Magnetic Resonance Imaging (MRI) instead, no method has yet been introduced to correct the susceptibility artefact, still present even in MAR-specific acquisitions. In this work, we hypothesise that a multimodal approach to MAR would improve both CT and MRI. Given their different artefact appearance, their complementary information can compensate for the corrupted signal in either modality. We thus propose an unsupervised deep learning method for multimodal MAR. We introduce the use of Locally Normalised Cross Correlation as a loss term to encourage the fusion of multimodal information. Experiments show that our approach favours a smoother correction in the CT, while promoting signal recovery in the MRI.

Related papers

• Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model [58.694932010573346]
  Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. We propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images.
  arXiv  Detail & Related papers  (2024-12-10T15:25:18Z)
• Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction [7.4965536794782945]
  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.
  arXiv  Detail & Related papers  (2024-06-18T01:36:58Z)
• Solving Energy-Independent Density for CT Metal Artifact Reduction via Neural Representation [46.57879724994237]
  Reconstructing CT images from metal-corrupted measurements becomes a challenging nonlinear inverse problem. Existing state-of-the-art (SOTA) metal artifact reduction (MAR) algorithms rely on supervised learning with numerous paired CT samples. In this work, we propose Density neural representation (Diner), a novel unsupervised MAR method.
  arXiv  Detail & Related papers  (2024-05-11T16:30:39Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• 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)
• 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)
• Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging [41.587861167935785]
  Motion artifacts compromise the quality of magnetic resonance imaging (MRI) Deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR) One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. We propose a novel UN Abnormality Extraction Network (UNAEN) to alleviate this problem.
  arXiv  Detail & Related papers  (2023-01-04T18:02:59Z)
• 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)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• 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)

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.