Deep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images

Du, Muge; Liang, Kaichao; Zhang, Li; Gao, Hewei; Liu, Yinong; Xing, Yuxiang

doi:10.1109/tmi.2023.3244252

Cited by 6 publications

(4 citation statements)

References 47 publications

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“…In addition to the already mentioned sinograminterpolation, several papers have been published on deep learning-based MAR for medical applications. Most of them can be categorized in the following two major groups: Image-based approaches [18][19][20] aim to correct artifacts solely in image space. In contrast, sinogram-based approaches [21][22][23] try to correct the information within the metal trace of the sinogram instead of applying linear interpolation.…”

Section: Introductionmentioning

confidence: 99%

Nonlinearly scaled prior image‐controlled frequency split for high‐frequency metal artifact reduction in computed tomography

et al. 2022

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Purpose This paper introduces a new approach for the dedicated reduction of high‐frequency metal artifacts, which applies a nonlinear scaling (NLS) transfer function on the high‐frequency projection domain to reduce artifacts, while preserving edge information and anatomic detail by incorporating prior image information. Methods An NLS function is applied to suppress high‐frequency streak artifacts, but to restrict the correction to metal projections only, scaling is performed in the sinogram domain. Anatomic information should be preserved and is excluded from scaling by incorporating a prior image from tissue classification. The corrected high‐frequency sinogram is reconstructed and combined with the low‐frequency component of a normalized metal artifact reduction (NMAR) image. Scans of different anthropomorphic phantoms were acquired (unilateral hip, bilateral hip, dental implants, and embolization coil). Multiple regions of interest (ROIs) were drawn around the metal implants and hounsfield unit (HU) deviations were analyzed. Clinical data sets including single image slices of dental fillings, a bilateral hip implant, spinal fixation screws, and an aneurysm coil were reconstructed and assessed. Results The prior image‐controlled NLS can remove streak artifacts while preserving anatomic detail within the bone and soft tissue. The qualitative analysis of clinical cases showed a tremendous enhancement within dental fillings and neuro coils, and a significant enhancement within spinal screws or hip implants. The phantom scan measurements support this observation. In all phantom setups, the NLS‐corrected result showed lowest HU derivation and the best visualization of the data. Conclusions The prior image‐controlled NLS provides a method to reduce high‐frequency streaks in metal‐corrupted computed tomography (CT) data.

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Section: Introductionmentioning

confidence: 99%

Nonlinearly scaled prior image‐controlled frequency split for high‐frequency metal artifact reduction in computed tomography

et al. 2022

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“…In the case of medical images, these kind of data are hard to create. Because of that, the majority of learning‐based methods are forced to use simulated data, although they oftentimes cannot completely reproduce the complex underlying physical image formation process (e.g., DeepDRR 26 ), thus ending up with a domain gap as described by Du et al 27 . Alternatively, physically correct simulations require extensive computational effort in the case of, for example, Monte Carlo simulations (e.g., MC‐GPU 28 ).…”

Section: Discussionmentioning

confidence: 99%

DL‐based inpainting for metal artifact reduction for cone beam CT using metal path length information

et al. 2022

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Background Metallic implants, which are inserted into the patient's body during trauma interventions, are the main cause of heavy artifacts in 3D X‐ray acquisitions. These artifacts then hinder the evaluation of the correct implant's positioning, thus leading to a disturbed patient's healing process and increased revision rates. Purpose This problem is tackled by so‐called metal artifact reduction (MAR) methods. This paper examines possible advances in the inpainting process of such MAR methods to decrease disruptive artifacts while simultaneously preserving important anatomical structures adjacent to the inserted implants. Methods In this paper, a learning‐based inpainting method for cone‐beam computed tomography is proposed that couples a convolutional neural network (CNN) with an estimated metal path length as prior knowledge. Further, the proposed method is solely trained and evaluated on real measured data. Results The proposed inpainting approach shows advantages over the inpainting method used by the currently clinically approved frequency split metal artifact reduction (fsMAR) method as well as the learning‐based state‐of‐the‐art (SOTA) method PConv‐Net. The major improvement of the proposed inpainting method lies in the ability to correctly preserve important anatomical structures in those regions adjacent to the metal implants. Especially these regions are highly important for a correct implant's positioning in an intraoperative setup. Using the proposed inpainting, the corresponding MAR volumes reach a mean structural similarity index measure (SSIM) score of 0.9974 and outperform the other methods by up to 6 dB on single slices regarding the peak signal‐to‐noise ratio (PSNR) score. Furthermore, it can be shown that the proposed method can generalize to clinical cases at hand. Conclusions In this paper, a learning‐based inpainting network is proposed that leverages prior knowledge about the metal path length of the inserted implant. Evaluations on real measured data reveal an increased overall MAR performance, especially regarding the preservation of anatomical structures adjacent to the inserted implants. Further evaluations suggest the ability of the proposed approach to generalize to clinical cases.

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Section: Introductionmentioning

confidence: 99%

“…Moreover, the semi-supervised MAR methods usually train the network model on paired data and use real unpaired data to refine and adjust them for improving the generalization ability of the model (Niu et al 2021, Shi et al 2022, Du et al 2023. For example, Du et al developed an unsupervised domain adaptation (UDAMAR) method that utilizes domain classifiers to align the features extracted by the encoder for decreasing the domain gap between simulated and real data (Du et al 2023). Shi et al proposed a semi-supervised learning method of latent features based on convolutional neural network (SLF-CNN), which used the Gaussian process to model the latent features of clinic artifact CT images as the weighted sum of the latent features of simulated artifact images to obtain the feature level pseudo-ground truths (Shi et al 2022).…”

Section: Introductionmentioning

confidence: 99%

b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT

Song,

Yao,

Peng

et al. 2024

Phys. Med. Biol.

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Objective. Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance.Approach. we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations.Main results. To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests,which demonstrate b-MAR improvements of > 1.4131 dB in PSNR, > 0.3473 HU decrements in RMSE, and > 0.0025 promotion in SSIM over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively.Significance. The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model's artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.

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Deep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images

Cited by 6 publications

References 47 publications

Nonlinearly scaled prior image‐controlled frequency split for high‐frequency metal artifact reduction in computed tomography

Nonlinearly scaled prior image‐controlled frequency split for high‐frequency metal artifact reduction in computed tomography

DL‐based inpainting for metal artifact reduction for cone beam CT using metal path length information

b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT

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