Metal Artifact Reduction Method Research Articles

Metal artifacts reduction in kV-CT images with polymetallic dentures and complex metals based on MV-CBCT images in radiotherapy

This paper proposes a metal artifact reduction method of using MV-CBCT images to correct metal artifacts in kV-CT images, especially for the complex metal artifacts caused by multi-metal interaction of patients with head and neck tumors. The different tissue regions are segmented in the MV-CBCT images to obtain template images and the metal region is segmented in the kV-CT images. Forward projection is performed to get sinogram of the template images, kV-CT images and metal region images. Artifact images can be reconstructed through those sonograms. Corrected images is generated by subtracting the artifact images from the original kV-CT images. After the first correction, the template images are generated again and brought into the previous step for iteration to get better correction result. CT data set of 7 patients are used in this study, compared with linear interpolation metal artifact (LIMAR) and normalized metal artifact reduction method, mean relative error of CT value is reduced by 50.5% and 63.3%, noise is reduced by 56.2% and 58.9%. The Identifiability Score of the tooth, upper/lower jaw, tongue, lips, masseter muscle and cavity in the corrected images by the proposed method have significantly improved (P < 0.05) than original images. The artifacts correction method proposed in this paper can effectively remove the metal artifacts in the images and greatly improve the CT value accuracy, especially in the case of multi-metal and complex metal implantation.

InDuDoNet+: A deep unfolding dual domain network for metal artifact reduction in CT images.

During the computed tomography (CT) imaging process, metallic implants within patients often cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based methods have achieved promising performance. However, most of them share two main common limitations: (1) the CT physical imaging geometry constraint is not comprehensively incorporated into deep network structures; (2) the entire framework has weak interpretability for the specific MAR task; hence, the role of each network module is difficult to be evaluated. To alleviate these issues, in the paper, we construct a novel deep unfolding dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. Concretely, we derive a joint spatial and Radon domain reconstruction model and propose an optimization algorithm with only simple operators for solving it. By unfolding the iterative steps involved in the proposed algorithm into the corresponding network modules, we easily build the InDuDoNet+ with clear interpretability. Furthermore, 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. Comprehensive experiments on synthesized data and clinical data substantiate the superiority of the proposed methods as well as the superior generalization performance beyond the current state-of-the-art (SOTA) MAR methods. Code is available at https://github.com/hongwang01/InDuDoNet_plus.

Virtual monochromatic imaging with projection-based material decomposition algorithm for metal artifacts reduction in photon-counting detector computed tomography.

Metal artifacts present a major challenge to computed tomography (CT) because they reduce the image quality in medical diagnosis and treatment. Several metal artifact reduction (MAR) methods have been proposed to address this issue in previous studies. This study aimed to synthesize a virtual monochromatic image for MAR in CT images using projection-based material decomposition (MD) algorithms. We developed a spectral micro-CT prototype system equipped with a photon-counting detector (PCD) and PCD-CT imaging simulator to assess the performances of different MAR methods. Two projection-based MD algorithms were implemented and evaluated for their MAR performances in CT images and compared with conventional sinogram inpainting MAR methods. Different parts of digital 4D-extended cardiac torso (XCAT) phantoms with metal implants were designed to simulate various real scenarios. A homemade metal artifact evaluation (MAE) phantom was used to evaluate the MAR performance in experiments. The simulated results of the XCAT phantom indicated that the projection-based virtual monochromatic CT (VMCT) images provided better image quality than the conventional MAR images without blurring the normal tissues at the position of the metal artifacts. Various quantitative indicators support this conclusion. Additionally, the experimental results of the MAE phantom reveal that projection-based VMCT images can avoid image distortion caused by metal artifacts, unlike conventional MAR methods. In regards to the projection-based VMCT images, the simulated and experimental results demonstrated that using the linear maximum likelihood estimators with an error correction look-up table algorithm yielded better MAR performance compared to that obtained using a polynomial algorithm. Furthermore, projection-based VMCT images can not only reduce metal artifacts effectively but also simultaneously prevents object blurring at the metal artifact position and image distortion of the metal implants. Hence, the CT image quality can be further improved to increase the abilities for both preoperative and postoperative assessment of metal implants.

Physics-informed sinogram completion for metal artifact reduction in CT imaging

Objective. Metal artifacts in the computed tomography (CT) imaging are unavoidably adverse to the clinical diagnosis and treatment outcomes. Most metal artifact reduction (MAR) methods easily result in the over-smoothing problem and loss of structure details near the metal implants, especially for these metal implants with irregular elongated shapes. To address this problem, we present the physics-informed sinogram completion (PISC) method for MAR in CT imaging, to reduce metal artifacts and recover more structural textures. Approach. Specifically, the original uncorrected sinogram is firstly completed by the normalized linear interpolation algorithm to reduce metal artifacts. Simultaneously, the uncorrected sinogram is also corrected based on the beam-hardening correction physical model, to recover the latent structure information in metal trajectory region by leveraging the attenuation characteristics of different materials. Both corrected sinograms are fused with the pixel-wise adaptive weights, which are manually designed according to the shape and material information of metal implants. To furtherly reduce artifacts and improve the CT image quality, a post-processing frequency split algorithm is adopted to yield the final corrected CT image after reconstructing the fused sinogram. Main results. We qualitatively and quantitatively evaluated the presented PISC method on two simulated datasets and three real datasets. All results demonstrate that the presented PISC method can effectively correct the metal implants with various shapes and materials, in terms of artifact suppression and structure preservation. Significance. We proposed a sinogram-domain MAR method to compensate for the over-smoothing problem existing in most MAR methods by taking advantage of the physical prior knowledge, which has the potential to improve the performance of the deep learning based MAR approaches.

The Relationship between the Contouring Time of the Metal Artifacts Area and Metal Artifacts in Head and Neck Radiotherapy.

(1) Background: The impacts of metal artifacts (MAs) on the contouring workload for head and neck radiotherapy have not yet been clarified. Therefore, this study evaluated the relationship between the contouring time of the MAs area and MAs on head and neck radiotherapy treatment planning. (2) Methods: We used treatment planning computed tomography (CT) images for head and neck radiotherapy. MAs were classified into three severities by the percentage of CT images containing MAs: mild (<25%), moderate (25−75%), and severe (>75%). We randomly selected nine patients to evaluate the relationship between MAs and the contouring time of the MAs area. (3) Results: The contouring time of MAs showed moderate positive correlations with the MAs volume and the number of CT images containing MAs. Interobserver reliability of the extracted MAs volume and contouring time were excellent and poor, respectively. (4) Conclusions: Our study suggests that the contouring time of MAs areas is related to individual commitment rather than clinical experience. Therefore, the development of software combining metal artifact reduction methods with automatic contouring methods is necessary to reducing interobserver variability and contouring workload.

DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction.

To reduce the potential risk of radiation to the patient, low-dose computed tomography (LDCT) has been widely adopted in clinical practice for reconstructing cross-sectional images using sinograms with reduced x-ray flux. The LDCT image quality is often degraded by different levels of noise depending on the low-dose protocols. The image quality will be further degraded when the patient has metallic implants, where the image suffers from additional streak artifacts along with further amplified noise levels, thus affecting the medical diagnosis and other CT-related applications. Previous studies mainly focused either on denoising LDCT without considering metallic implants or full-dose CT metal artifact reduction (MAR). Directly applying previous LDCT or MAR approaches to the issue of simultaneous metal artifact reduction and low-dose CT (MARLD) may yield sub-optimal reconstruction results. In this work, we develop a dual-domain under-to-fully-complete progressive restoration network, called DuDoUFNet, for MARLD. Our DuDoUFNet aims to reconstruct images with substantially reduced noise and artifact by progressive sinogram to image domain restoration with a two-stage progressive restoration network design. Our experimental results demonstrate that our method can provide high-quality reconstruction, superior to previous LDCT and MAR methods under various low-dose and metal settings.

Combined clustered scan-based metal artifact reduction algorithm (CCS-MAR) for ultrasound-guided cardiac radioablation

Cardiac radioablation is a promising treatment for cardiac arrhythmias, but accurate dose delivery can be affected by heart motion. For this reason, real-time cardiac motion monitoring during radioablation is of paramount importance. Real-time ultrasound (US) guidance can be a solution. The US-guided cardiac radioablation workflow can be simplified by the simultaneous US and planning computed tomography (CT) acquisition, which can result in US transducer-induced metal artifacts on the planning CT scans. To reduce the impact of these artifacts, a new metal artifact reduction (MAR) algorithm (named: Combined Clustered Scan-based MAR [CCS-MAR]) has been developed and compared with iMAR (Siemens), O-MAR (Philips) and MDT (ReVision Radiology) algorithms. CCS-MAR is a fully automated sinogram inpainting-based MAR algorithm, which uses a two-stage correction process based on a normalized MAR method. The second stage aims to correct errors remaining from the first stage to create an artifact-free combined clustered scan for the process of metal artifact reduction. To evaluate the robustness of CCS-MAR, conventional CT scans and/or dual-energy CT scans from three anthropomorphic phantoms and transducers with different sizes were used. The performance of CCS-MAR for metal artifact reduction was compared with other algorithms through visual comparison, image quality metrics analysis, and HU value restoration evaluation. The results of this study show that CCS-MAR effectively reduced the US transducer-induced metal artifacts and that it improved HU value accuracy more or comparably to other MAR algorithms. These promising results justify future research into US transducer-induced metal artifact reduction for the US-guided cardiac radioablation purposes.

IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction

Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several neural network models have been proposed for metal artifact reduction (MAR). Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single domain methods. However, current dual-domain methods usually operate on both domains in a specific order, which implicitly imposes a certain priority prior into MAR and may ignore the latent information interaction between both domains. To address this problem, in this article, we propose a novel interactive dual-domain parallel network for CT MAR, dubbed as IDOL-Net. Specifically, different from existing dual-domain methods, the proposed IDOL-Net is composed of two modules. First, to obtain high-quality prior sinogram and image to guide following MAR, we propose a novel artifact disentanglement network that disentangles the noise and artifacts in sinogram and image domain, respectively. The follow-up refinement module consists of two parallel and interactive branches that simultaneously operate on image and sinogram, fully exploiting the latent information interaction between both domains. Extensive experiments on simulated and clinical artifacts data demonstrate that the proposed IDOL-Net outperforms several state-of-the-art models in both qualitative and quantitative aspects.

Metal artifact correction in photon-counting detector computed tomography: metal trace replacement using high-energy data.

Metal artifacts have been an outstanding issue in computed tomography (CT) since its first uses in the clinic and continue to interfere. Metal artifact reduction (MAR) methods continue to be proposed and photon-counting detectors (PCDs) have recently been the subject of research toward this purpose. PCDs offer the ability to distinguish the energy of incident x-rays and sort them in a set number of energy bins. High-energy data captured using PCDs have been shown to reduce metal artifacts in reconstructions due to reduced beam hardening. High-energy reconstructions using PCD-CT have their drawbacks, such as reduced image contrast and increased noise. Here, we demonstrate a MAR algorithm, trace replacement MAR (TRMAR), in which the data corrupted by metal artifacts in full energy spectrum projections are corrected using the high-energy data captured during the same scan. The resulting reconstructions offer similar MAR to that seen in high-energy reconstructions, but with improved image quality. Experimental data were collected using a bench-top PCD-CT system with a cadmium zinc telluride PCD. Simulations were performed to determine the optimal high-energy threshold and to test TRMAR in simulations using the XCAT phantom and a biological sample. For experiments a 100-mm diameter cylindrical phantom containing vials of water, two screws, various densities of Ca(ClO4 )2 , and a spatial resolution phantom was imaged with and without the screws. The screws were segmented in the initial reconstruction and forward projected to identify them in the sinogram space in order to perform TRMAR. The resulting reconstructions were compared to the control and to reconstructions corrected using normalized metal artifact reduction (NMAR). Additionally, a beef short rib was imaged with and without metal to provide a more realistic phantom. XCAT simulations showed a reduction in the streak artifact from -978HU in uncorrected images to -10HU with TRMAR. The magnitude of the metal artifact in uncorrected images of the 100-mm phantom was -442 HU, compared to the desired -81HU with no metal. TRMAR reduced the magnitude of the artifact to -142 HU, with NMAR reducing the magnitude to -96 HU. Relative image noise was reduced from 176% in the high-energy image to 56% using TRMAR. Density quantification was better with NMAR, with the Ca(ClO4 )2 vial affected most by metal artifacts showing 0.8% error compared to 2.1% with TRMAR. Small features were preserved to a greater extent with TRMAR, with the limiting spatial frequency at 20% of the MTF fully maintained at 1.31 lp/mm, while with NMAR it was reduced to 1.22 lp/mm. Images of the beef short rib showed better delineation of the shape of the metal using TRMAR. NMAR offers slightly better performance compared to TRMAR in streak reduction and image quality metrics. However, TRMAR is less susceptible to metal segmentation errors and can closely approximate the reduction in the streak metal artifact seen in NMAR at 1/3 the computation time. With the recent introduction of PCD-CT into the clinic, TRMAR offers notable potential for fast, effective MAR.

Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan

Objective. Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolution with minimal equipment cost. In low-dose CBCT environments, metallic inserts such as implants, crowns, and dental fillings cause severe artifacts, which result in a significant loss of morphological structures of teeth in reconstructed images. Such metal artifacts prevent accurate 3D bone-teeth-jaw modeling for diagnosis and treatment planning. However, the performance of existing metal artifact reduction (MAR) methods in handling the loss of the morphological structures of teeth in reconstructed CT images remains relatively limited. In this study, we developed an innovative MAR method to achieve optimal restoration of anatomical details. Approach. The proposed MAR approach is based on a two-stage deep learning-based method. In the first stage, we employ a deep learning network that utilizes intra-oral scan data as side-inputs and performs multi-task learning of auxiliary tooth segmentation. The network is designed to improve the learning ability of capturing teeth-related features effectively while mitigating metal artifacts. In the second stage, a 3D bone-teeth-jaw model is constructed with weighted thresholding, where the weighting region is determined depending on the geometry of the intra-oral scan data. Main results. The results of numerical simulations and clinical experiments are presented to demonstrate the feasibility of the proposed approach. Significance. We propose for the first time a MAR method using radiation-free intra-oral scan data as supplemental information on the tooth morphological structures of teeth, which is designed to perform accurate 3D bone-teeth-jaw modeling in low-dose CBCT environments.

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

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 revisionrates. 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 insertedimplants. 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. 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 athand. 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 clinicalcases.

Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme.

Sparse-view sampling has attracted attention for reducing the scan time and radiation dose of dental cone-beam computed tomography (CBCT). Recently, various deep learning-based image reconstruction techniques for sparse-view CT have been employed to produce high-quality image while effectively reducing streak artifacts caused by the lack of projection views. However, most of these methods do not fully consider the effects of metal implants. As sparse-view sampling strengthens the artifacts caused by metal objects, simultaneously reducing both metal and streak artifacts in sparse-view CT images has been challenging. To solve this problem, in this study, we propose a novelframework. The proposed method was based on the normalized metal artifact reduction (NMAR) method, and its performance was enhanced using two convolutional neural networks (CNNs). The first network reduced the initial artifacts while preserving the fine details to generate high-quality priors for NMAR processing. Subsequently, the second network was employed to reduce the streak artifacts after NMAR processing of sparse-view CT data. To validate the proposed method, we generated training and test data by computer simulations using both extended cardiac-torso (XCAT) and clinical data sets. Visual inspection and quantitative evaluations demonstrated that the proposed method effectively reduced both metal and streak artifacts while preserving the details of anatomical structures compared with the conventional metal artifact reduction methods. We propose a framework for reconstructing accurate CT images in metal-inserted sparse-view CT. The proposed method reduces streak artifacts from both metal objects and sparse-view sampling while recovering the anatomical details, indicating the feasibility of fast-scan dental CBCTimaging.

Investigation of BGA underfill process based on multipositional computed tomography

In this paper, defect inspection related to an epoxy underfilling process between integrated circuits (ICs) and printed circuit boards (PCBs) is presented utilizing the data fusion of multipositional computed tomography (DFMCT). The work aims to verify the existence of underfilling epoxy which was not possible utilizing standard computed tomography due to strong metal artifacts created by ball grid array (BGA) packages. By interpreting the results obtained from the scanned samples, we were able to correlate the excess epoxy defects and the success of the underfilling process. We present the data fusion approach as an alternative method when traditional metal artifact reduction methods would be insufficient. The study was commenced by making a proper radiographic simulation using aRTist simulation software. The simulation enabled us to choose the right mounting positions which produce less metal artifacts for the experimental study. The DFMCT was achieved by acquiring multiple computed tomographic scans at different sample orientations. The reconstructed volumes were then aligned, and the fused volume was obtained by combining the corrected data and suppressing the disrupted data of the reconstructed volumes.

Streak Metal Artifact Reduction Based on Sinogram Fusion and Tissue-Class Model in CT Images

The presence of streak metal artifacts seriously degrades the diagnostic value and deteriorates the qualities of CT images. Analyzing the causes and classical streak metal artifact reduction (MAR) methods, the paper proposes the streak metal artifact reduction method based on sinogram fusion and tissue-class mode for CT images (F-MAR). Firstly, the original CT images are corrected using a linear interpolation streak metal artifact reduction (L-MAR) scheme in the raw data domain. Subsequently, to preserve the edge information, the metal artifact-reduced images are then smoothed into smoothed images (tissue-class model) by using the mean filter. Segment the original CT image that contained the streak artifacts. The original CT image and the CT image that contained high-density material are projected into the original sinogram and the high density material sinogram, respectively. Secondly, the simple linear interpolation is used to correct the CT original CT image into the corrected CT image. The mean filter is applied in the corrected CT image. The corrected CT image is projected into the corrected sinogram. Thirdly, according to the position of the high density material sinogram located in the original sinogram and the corrected sinogram, the original position sinogram included in the original sinogram and the corrected position sinogram included in the corrected sinogram are, respectively, obtained. The two sinograms are fused into the fused sinogram. The fused sinogram, the original sinogram, and the high-density material sinogram are fused into the final sinogram. Finally, the filtered back projection reconstruction algorithm is used to reconstruct the final sinogram into the reconstructed CT image. The reconstructed CT image and high density material image are fused into the final image. The experimentation results show that the method proposed in the paper can obtain better correction effect than the classical correction methods in vision.

A semi-supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction.

X-ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic-artifact CT images. However, this causes the neural network to be biased toward learning specific synthetic-artifact patterns and leads to a poor generalization for unlabeled real-artifact CT images. In this study, a semi-supervised learning method of latent features based on convolutional neural networks (SLF-CNN) is developed to remove metal artifacts while ensuring a good generalization ability for real-artifact CT images. The proposed semi-supervised method extracts CT image features in alternate iterations of a synthetic-artifact learning stage and a real-artifact learning stage. In the synthetic-artifact learning stage, SLF-CNN is fed with paired synthetic-artifact CT images and is constrained using mean-squared-error (MSE) loss and perceptual loss in a supervised learning fashion. In the real-artifact learning stage, the network weight is updated by minimizing the error between the pseudo-ground truths and the predicted latent features. The feature level pseudo-ground truths are obtained by modeling latent features using the Gaussian process. The overall framework of SLF-CNN adopts an encoder-decoder structure. The encoder is composed of artifact information collection groups to map the input artifact-affected synthetic-artifact CT images and real-artifact CT images into latent features. The decoder is composed of stacked ResNeXt blocks and is responsible for decoding latent features with high-level semantic information to reconstruct artifact-free CT images. The performance of the proposed method is evaluated through contrast experiments and ablation experiments. The contrast experimental results indicate that the artifact-free CT images obtained by SLF-CNN have good metrics values, which are close to or better than those of typical supervised MAR methods. The metal artifacts in artifact-affected CT images are eliminated and the tissue structure details are preserved using SLF-CNN. The ablation experiment shows that adding real-artifact CT images greatly improves the generalization ability of the network. The proposed semi-supervised learning method of latent features for MAR effectively suppresses metal artifacts and improves the generalization ability of the network.

DICDNet: Deep Interpretable Convolutional Dictionary Network for Metal Artifact Reduction in CT Images.

Computed tomography (CT) images are often impaired by unfavorable artifacts caused by metallic implants within patients, which would adversely affect the subsequent clinical diagnosis and treatment. Although the existing deep-learning-based approaches have achieved promising success on metal artifact reduction (MAR) for CT images, most of them treated the task as a general image restoration problem and utilized off-the-shelf network modules for image quality enhancement. Hence, such frameworks always suffer from lack of sufficient model interpretability for the specific task. Besides, the existing MAR techniques largely neglect the intrinsic prior knowledge underlying metal-corrupted CT images which is beneficial for the MAR performance improvement. In this paper, we specifically propose a deep interpretable convolutional dictionary network (DICDNet) for the MAR task. Particularly, we first explore that the metal artifacts always present non-local streaking and star-shape patterns in CT images. Based on such observations, a convolutional dictionary model is deployed to encode the metal artifacts. To solve the model, we propose a novel optimization algorithm based on the proximal gradient technique. With only simple operators, the iterative steps of the proposed algorithm can be easily unfolded into corresponding network modules with specific physical meanings. Comprehensive experiments on synthesized and clinical datasets substantiate the effectiveness of the proposed DICDNet as well as its superior interpretability, compared to current state-of-the-art MAR methods. Code is available at https://github.com/hongwang01/DICDNet.

Metal Artifact Reduction in Spectral X-ray CT Using Spectral Deep Learning.

Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured. We show that the additional information in the energy domain provides relevance for restoring the quality of low-energy reconstruction affected by metal artifacts. The correction method is parameter free and only takes around 15 ms per energy channel, satisfying near-real time requirement of industrial scanners.

Flexible Generation of Prior Images for Metal Artifact Reduction in Industrial Computed Tomography

In computed tomography, artifacts caused by metal objects pose a major challenge, as they severely degrade the final image. Reduction of these artifacts is a topic of ongoing research, and many possible approaches have been developed so far. Among these, projection completion methods are effective and reasonably simple to implement compared to others. The most effective of these methods require a prior image, a synthetic, artifact-free representation of the data. This prior is used to recover data obscured by the metals and thus reduce the presence of potential secondary artifacts, but it may not be trivial to obtain especially in industrial computed tomography, where sample variability tends to be high. Traditional methods of prior generation are often based on multi-segmentation of a preliminary tomographic slice, which requires substantial a-priori knowledge about the scanned sample, its morphology, and the materials it is made of. This is a major limit for applications where such information is not readily available. Initial results for a more flexible method of prior image generation based on sinogram decomposition are presented here. The goal of this method is to decrease the need to fine-tune parameters for different samples and make it possible to use existing advanced metal artifact reduction methods in new fields.

Multi-modal feature-fusion for CT metal artifact reduction using edge-enhanced generative adversarial networks

Computed Tomography (CT) imaging is one of the most widely-used and cost-effective technology for organ screening and diseases diagnosis. Because of existence of metallic implants in some patients, the CT images acquired from these patients are often corrupted by undesirable metal artifacts, which causes severe problem of metal artifact. Although there have been proposed many methods to reduce metal artifact, reduction is still challenging and inadequate, and results are suffering from symptom variance, second artifact and poor subjective evaluation. To address these problems, we propose a novel metal artifact reduction method based on generative adversarial networks to simultaneously reduce metal artifacts and enhance texture structure of corrected CT images. Specifically, we firstly incorporate interactive information (text) and imaging CT (image) into a comprehensive feature to yield multi-modal feature-fusion representation, which overcomes the representative ability limitation of single-modal data. The incorporation of interaction information constrains the feature generation to ensure symptom consistency between corrected and target CT. Then, we design an edge-enhance sub-network to avoid second artifact and suppress noise. Besides, we invite three professional physicians to evaluate corrected CT image subjectively. In this paper, We achieved average increment of 11.3% PSNR and 12.1% SSIM on DeepLesion dataset. The subjective evaluations by physicians show that ours outperforms over 6.3%, 7.1%, 5.50% and 6.9% in term of sharpness, resolution, invariance and acceptability, respectively. Our proposed method can achieve high-quality metal artifact reduction results.

A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Metal objects in X-ray computed tomography can cause severe artifacts. The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods. This paper proposes a projection-domain algorithm to reduce the metal artifacts. In this algorithm, the unknowns are the metal-affected projections, while the objective function is set up in the image domain. The data fidelity term is not utilized in the objective function. The objective function of the proposed algorithm consists of two terms: the total variation of the metal-removed image and the energy of the negative-valued pixels in the image. After the metal-affected projections are modified, the final image is reconstructed via the filtered backprojection algorithm. The feasibility of the proposed algorithm has been verified by real experimental data.