Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging

Wang, Zihao; Vandersteen, Clair; Demarcy, Thomas; Gnansia, Dan; Raffaelli, Charles; Guevara, Nicolas; Delingette, Hervé

doi:10.1007/978-3-030-32226-7_14

Cited by 19 publications

(16 citation statements)

References 10 publications

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“…Recent studies have designed 3D adversarial nets for metal artifact reduction in CT images of the ear. Wang et al [37], [38] performed a large validation study that showed improved MAR performance and segmentation results. To the best of our knowledge, this study is the first to build 3D adversarial nets with a regularized loss function for metal artifact reduction derived from multiple dental metal fillings.…”

Section: Discussionmentioning

confidence: 99%

“…The application of GANs to artifact reduction is a relatively new challenge, as technical difficulties mean that various low-quality images affected by strong metal artifacts exist in clinical CT images. Recent studies have applied GANs to MAR in small regions of CT images of the ear [35]- [38]. Du et al [39] presented preliminary results from GAN-based MAR for images with dental fillings, while Liao et al [40] proposed a CycleGAN-based artifact disentanglement network and compared quantitative evaluation results against existing supervised/unsupervised MAR methods using synthesized datasets.…”

Section: Introductionmentioning

confidence: 99%

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Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images

Nakao

Imanishi²,

Imai

et al. 2020

IEEE Access

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The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it is difficult for simulated artifacts to cover the complexity of the real physical phenomena that may be observed in X-ray propagation. In this paper, we introduce metal artifact reduction methods based on an unsupervised volume-to-volume translation learned from clinical CT images. We construct three-dimensional adversarial nets with a regularized loss function designed for metal artifacts from multiple dental fillings. The results of experiments using a CT volume database of 361 patients demonstrate that the proposed framework has an outstanding capacity to reduce strong artifacts and to recover underlying missing voxels, while preserving the anatomical features of soft tissues and tooth structures from the original images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images

Nakao

Imanishi²,

Imai

et al. 2020

IEEE Access

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“…Deep Learning) is an effective way to perform image segmentation or processing in many cases. Specifically, in inner ear CT imaging analysis, many works achieved impressive results (Lv et al, 2021;Raabid et al, 2021;Heutink et al, 2020;Wang et al, 2019;Li et al, 2021;Alshazly et al, 2019;Zhang et al, 2019;Wang et al, 2020b). However, supervised learning methods have also many limitations.…”

Section: Introductionmentioning

confidence: 99%

Bayesian logistic shape model inference: Application to cochlear image segmentation

Wang

Demarcy²,

Vandersteen

et al. 2022

Medical Image Analysis

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Incorporating shape information is essential for the delineation of many organs and anatomical structures in medical images. While previous work has mainly focused on parametric spatial transformations applied on reference template shapes, in this paper, we address the Bayesian inference of parametric shape models for segmenting medical images with the objective to provide interpretable results. The proposed framework defines a likelihood appearance probability and a prior label probability based on a generic shape function through a logistic function. A reference length parameter defined in the sigmoid controls the trade-off between shape and appearance information. The inference of shape parameters is performed within an Expectation-Maximisation approach where a Gauss-Newton optimization stage allows to provide an approximation of the posterior probability of shape parameters. This framework is applied to the segmentation of cochlea structures from clinical CT images constrained by a 10 parameter shape model. It is evaluated on three different datasets, one of which includes more than 200 patient images. The results show performances comparable to supervised methods and better than previously proposed unsupervised ones. It also enables an analysis of parameter distributions and the quantification of segmentation uncertainty including the effect of the shape model.

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“…Park et al [26] used U-net [27] to repair inconsistent sinograms by removing the primary metal-induced beam hardening factors along the metal trace boundaries. In [28][29][30], generative adversarial net (GAN) and its variants were introduced to improve the in-painting performance. Partial convolution [31] was employed in [32,33] for sinogram completion.…”

Section: Introductionmentioning

confidence: 99%

Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images

Zhu

et al. 2021

Sensors

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In computed tomography (CT) images, the presence of metal artifacts leads to contaminated object structures. Theoretically, eliminating metal artifacts in the sinogram domain can correct projection deviation and provide reconstructed images that are more real. Contemporary methods that use deep networks for completing metal-damaged sinogram data are limited to discontinuity at the boundaries of traces, which, however, lead to secondary artifacts. This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of projection data at each angle, improving the accuracy of the complemented sinogram data. Masking the metal traces also ensures the stability and reliability of the unaffected data during metal artifacts reduction. The projection and reconstruction results and various evaluation metrics reveal that the proposed method can accurately repair missing data and reduce metal artifacts in reconstructed CT images.

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Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging

Cited by 19 publications

References 10 publications

Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images

Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images

Bayesian logistic shape model inference: Application to cochlear image segmentation

Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images

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