TDMAR-Net: a frequency-aware tri-domain diffusion network for CT metal artifact reduction

Accurate attenuation correction (AC) of PET data is a prerequisite for quantitative PET/MR imaging. However, MR images are susceptible to metal artifacts leading to void MR signal around metallic implants. Moreover, for large patients, exceeding the scanner’s field-of-view (FOV), MR images of the body will be truncated, mostly in the arms, thus hampering the accurate delineation of the body contour. Both metal-induced artifacts and body truncation affect PET AC by causing segmentation errors in MRI-based attenuation map generation. In this work, a deep learning convolutional neural network-based algorithm is proposed for the completion of MR images affected by body truncation or metal-induced artifacts. The core of the network utilizes dilated convolutions and residual connections to render an end-to-end 3D shape completion. The training of the network was performed using co-registered PET, CT and MR whole-body images of 15 patients. The evaluation of the proposed method was carried out on 10 patients with severe metal-induced artifacts and truncated MR images. Body contours from the corresponding non-attenuation corrected PET (non-AC PET) or CT images were segmented to estimate the amount of truncation in MR images. The estimated truncated volumes were later used as reference to assess the efficiency of the proposed method to recover the truncated or metal artifact affected areas. Moreover, the impact of the truncation compensation and metal-induced artifact reduction was investigated in the context of segmentation-based PET/MRI attenuation correction. The activity recovery in the affected areas was estimated before and after application of the shape completion method. The body truncation affected 11.1±2.3% of the body volume and consequently the MRI segmentation-based attenuation maps of 10 patients. After shape completion using the proposed method, the amount of truncated volume dropped to 0.7±0.2%. The SUV bias in the truncated area improved from -44.5±10% to -10.5±3% considering PET-CT AC as reference. Likewise, 8.5±1.9% of the head volume was affected by the metal-induced artifact leading to SUV bias of -59.5±11%. These were reduced to 0.3±0.1% and -23.5±9%, respectively, after shape completion. It was concluded that the proposed algorithm exhibited promising results towards the completion of MRI affected by truncation and metal-induced artifacts in whole-body PET/MRI.

This chapter reviews metal artifact reduction (MAR) methods for low-dose cone-beam computed tomography (CBCT). MAR is of vital significance because the number of aged people with artificial prostheses and metallic implants is swiftly increasing with the rapidly aging population. Metallic objects present in the CBCT field of view produce streaking artifacts that highly degrade the reconstructed CT images, resulting in a loss of information on the teeth and other anatomical structures. Metallic object-related artifacts are associated with beam hardening, scattering, partial volume effects, and a high degree of inhomogeneous attenuation to name a few. As metal-induced artifacts are complex and nonlinearly intertwined, MAR has remained a challenging problem over the last four decades. Metal artifacts are caused mainly due to a mismatch in the forward model of the filtered back-projection (FBP) algorithm. The presence of metallic objects in an imaging subject violates the model’s assumption that the CT sinogram data is equal to the Radon transform of an image. FBP ignores the polychromatic nature of the X-ray data $$\textbf{P}$$ , which has nonlinear dependence on the distribution of the metallic object. Various MAR methods have been suggested, but the existing MAR methods do not reduce the metal artifacts effectively in low-dose CBCT environments and may introduce new streaking artifacts that did not previously exist. We hope that this chapter will help develop new MAR algorithms that overcome the limitations of existing MAR methods and effectively reduce metal artifacts to facilitate diagnosis, preoperative and presurgical assessments, surgical navigation, and workflows for rapid prototyping.

ObjectivesThe susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning–based metal artefact reduction (MAR) in quantitative PET/CT imaging.MethodsDeep learning–based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques.ResultsThe evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR.ConclusionThe DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images.Key Points• The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images.• The aim of this work is to develop and evaluate a deep learning–based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging.• Deep learning–based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.

During the process of computed tomography (CT), metallic implants often cause disruptive artifacts in the reconstructed images, impeding accurate diagnosis. Many supervised deep learning-based approaches have been proposed for metal artifact reduction (MAR). However, these methods heavily rely on training with paired simulated data, which are challenging to acquire. This limitation can lead to decreased performance when applying these methods in clinical practice. Existing unsupervised MAR methods, whether based on learning or not, typically work within a single domain, either in the image domain or the sinogram domain. In this paper, we propose an unsupervised MAR method based on the diffusion model, a generative model with a high capacity to represent data distributions. Specifically, we first train a diffusion model using CT images without metal artifacts. Subsequently, we iteratively introduce the diffusion priors in both the sinogram domain and image domain to restore the degraded portions caused by metal artifacts. Besides, we design temporally dynamic weight masks for the image-domian fusion. The dual-domain processing empowers our approach to outperform existing unsupervised MAR methods, including another MAR method based on diffusion model. The effectiveness has been qualitatively and quantitatively validated on synthetic datasets. Moreover, our method demonstrates superior visual results among both supervised and unsupervised methods on clinical datasets. Codes are available in github.com/DeepXuan/DuDoDp-MAR.

Metal artifacts reduce the quality of CT images and increase the difficulty of interpretation. This study compared the ability of model-based iterative reconstruction and hybrid iterative reconstruction to improve CT image quality in patients with metallic dental artifacts when both techniques were combined with a metal artifact reduction algorithm. This retrospective clinical study included 40 patients (men, 31; women, 9; mean age, 62.9 ± 12.3 years) with oral and oropharyngeal cancer who had metallic dental fillings or implants and underwent contrast-enhanced ultra-high-resolution CT of the neck. Axial CT images were reconstructed using hybrid iterative reconstruction and model-based iterative reconstruction, and the metal artifact reduction algorithm was applied to all images. Finally, hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithm data were obtained. In the quantitative analysis, SDs were measured in ROIs over the apex of the tongue (metal artifacts) and nuchal muscle (no metal artifacts) and were used to calculate the metal artifact indexes. In a qualitative analysis, 3 radiologists blinded to the patients' conditions assessed the image-quality scores of metal artifact reduction and structural depictions. Hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithms yielded significantly different metal artifact indexes of 82.2 and 73.6, respectively (95% CI, 2.6-14.7; P < .01). The latter algorithms resulted in significant reduction in metal artifacts and significantly improved structural depictions(P < .01). Model-based iterative reconstruction + metal artifact reduction algorithms significantly reduced the artifacts and improved the image quality of structural depictions on neck CT images.

Background: The common streak artifacts in computed tomographic (CT) images result from metal implants in patients. Metal artifact suppresses and obstructs diagnosis or misdiagnoses as it occurred in ten percent of the patients’ tomographic images. Objectives: To develop the method for metal artifact reduction in CT images using MATLAB software and implement it in phantoms with the metal artifact as well as in patients with the metal artifact in the head and neck region. Materials and methods: The new method of metal artifact reduction in CT images using MATLAB software. The homogeneous polymethylmethacrylate (PMMA) phantom, the Alderson Rando phantom, and patients with a metal implant in the head and neck region were scanned by the Philips Brilliance Big Bore CT system. Commercial orthopedic metal artifact reduction (OMAR) software and a new method software were applied to the CT images of phantoms and patients. The quantitative analysis of image quality on a metal artifact of the head and neck region was evaluated in the percent noise. The qualitative analysis in clinical imaging was evaluated in scoring by two radiologists with the same experience. Results: In the Alderson Rando phantom, the new algorithm indicated higher efficiency in metal artifact reduction than OMAR software. In contrast, for the patient at head and neck CT images with metal artifact reduction, OMAR, and the new method showed comparable results. The new method suppressed the artifact in homogeneous PMMA, Alderson Rando phantoms, and patients with a metal implant in the head and neck region with approximately 40%, 40%, and 60% percentage of noise reduction, respectively. The qualitative analysis by two radiologists showed comparable results of OMAR and the new method. Conclusion: The efficiency of metal artifact reduction of the new method is better than no correction and OMAR in homogeneous PMMA phantom and Alderson Rando phantom. However, the efficiency of OMAR is better than the new method, and no correction regarding the percent noise.

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.

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.

Novel 3-fold metal artifact reduction method for CT images

Objective. Since the invention of modern Computed Tomography (CT) systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and limited-angle projection data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used. Approach. To solve this problem, we present a fine-grained projection-domain segmentation-based metal artifact reduction (MAR) method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain. Main results. The proposed method is tested on both digital phantom data and real scanned cone-beam computed tomography (CBCT) data. It achieves much-improved quantitative results in both metal segmentation and artifact reduction in our phantom study. The results on real scanned data also show the superiority of this method. Significance. The concept of projection-domain metal segmentation would advance MAR techniques in CBCT and has the potential to push forward the use of intraoperative CBCT in human-handed and robotic-assisted minimal invasive spine surgeries.

While modern clinical CT scanners under normal circumstances produce high quality images, severe artifacts degrade the image quality and the diagnostic value if metal prostheses or other metal objects are present in the field of measurement. Standard methods for metal artifact reduction (MAR) replace those parts of the projection data that are affected by metal (the so-called metal trace or metal shadow) by interpolation. However, while sinogram interpolation methods efficiently remove metal artifacts, new artifacts are often introduced, as interpolation cannot completely recover the information from the metal trace. The purpose of this work is to introduce a generalized normalization technique for MAR, allowing for efficient reduction of metal artifacts while adding almost no new ones. The method presented is compared to a standard MAR method, as well as MAR using simple length normalization. In the first step, metal is segmented in the image domain by thresholding. A 3D forward projection identifies the metal trace in the original projections. Before interpolation, the projections are normalized based on a 3D forward projection of a prior image. This prior image is obtained, for example, by a multithreshold segmentation of the initial image. The original rawdata are divided by the projection data of the prior image and, after interpolation, denormalized again. Simulations and measurements are performed to compare normalized metal artifact reduction (NMAR) to standard MAR with linear interpolation and MAR based on simple length normalization. Promising results for clinical spiral cone-beam data are presented in this work. Included are patients with hip prostheses, dental fillings, and spine fixation, which were scanned at pitch values ranging from 0.9 to 3.2. Image quality is improved considerably, particularly for metal implants within bone structures or in their proximity. The improvements are evaluated by comparing profiles through images and sinograms for the different methods and by inspecting ROIs. NMAR outperforms both other methods in all cases. It reduces metal artifacts to a minimum, even close to metal regions. Even for patients with dental fillings, which cause most severe artifacts, satisfactory results are obtained with NMAR. In contrast to other methods, NMAR prevents the usual blurring of structures close to metal implants if the metal artifacts are moderate. NMAR clearly outperforms the other methods for both moderate and severe artifacts. The proposed method reliably reduces metal artifacts from simulated as well as from clinical CT data. Computationally efficient and inexpensive compared to iterative methods, NMAR can be used as an additional step in any conventional sinogram inpainting-based MAR method.

This study aims to investigate the impact of combining deep learning image reconstruction (DLIR) and metal artifacts reduction (MAR) algorithms on the quality of CT images with metal implants under different scanning conditions. Four images of the maxillofacial region in pigs were taken using different metal implants for evaluation. The scans were conducted at three different dose levels (CTDIvol: 20/10/5mGy). The images were reconstructed using three different methods: filtered back projection (FBP), adaptive statistical iterative reconstruction with Veo at a 50% level (AV50), and DLIR at three levels (low, medium, and high). Regions of interest (ROIs) were identified in various tissues (near/far/reference fat, muscle, bone) both with and without metal implants and artifacts. Parameters such as standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and metal artifact index (MAI) were calculated. Additionally, two experienced radiologists evaluated the subjective image quality (IQ) using a 5-point Likert scale. (1) Both observers rated MAR generated significantly lower artifact scores than non-MAR in all types of tissues (P < 0.01), except for the far shadow and bloom in bone (phantoms 1, 3, 4) and the far bloom in muscle (phantom 3) without significant differences (P = 1.0). (2) Under the same scanning condition, DLIR at three levels produced a smaller SD than those of FBP and AV50 (P < 0.05). (3) Compared to FBP and AV50, DLIR denoted a better reduction of MAI and improvement of SNR and CNR (P < 0.05) for most tissues between the four phantoms. (4) Subjective overall IQ was superior with the increasement of DLIR level (P < 0.05) and both observers agreed that DLIR produced better artifact reductions compared with FBP and AV50. The combination of DLIR and MAR algorithms can enhance image quality, significantly reduce metal artifacts, and offer high clinical value.

Metal artifacts significantly degrade the image quality in computed tomography (CT) imaging, obscuring or even feigning pathology. While many different algorithms for metal artifact reduction (MAR) have been proposed, no comprehensive, clinically relevant evaluation benchmark exists. A major contributing factor to this is the lack of artifact-free ground truth data in clinical cases. Similarly, deep-learning based algorithms are hindered by the lack of paired training datasets with and without artifacts. We propose the simulation of a large training database for deep-learning based MAR algorithms as well as the definition of a comprehensive evaluation benchmark for MAR. For this we utilize and validate a framework for the realistic simulation of metal artifacts on clinical CT data. A clinical and a generic CT scanner geometry is modelled in the CatSim CT simulator within the open-access toolkit XCIST. Since most MAR research is performed in 2D, all datasets are simulated in 2D. The metal artifact simulation capability is experimentally validated in CT phantom scans containing various metal types and -geometries. The tool is then used to simulate metal artifact scenarios as training data for deep-learning algorithms utilizing two public CT databases. Lastly, a benchmark is defined for clinically realistic metal artifact scenarios and applied to a numerical and a deep-learning based MAR algorithm, respectively. Within specified regions of interest, the mean CT number deviation between simulation and real data was less than 2%, making the simulation tool suitable for the aspired tasks. In total, 14,000 metal scenarios in the head, thorax and pelvis regions were simulated. For the clinical benchmark, a set of metrics covering CT number accuracy, noise, image sharpness, streak amplitude, structural integrity, and the effect on range in proton therapy, were defined for a range of clinical scenarios. Metal scenarios covered the most relevant clinical use cases, covering small metal implants such as fiducial markers up to large metal implants such as hip replacements. Both the simulation tools and the benchmark with the test cases were made publicly available. We developed and distributed tools and datasets for the development and evaluation of MAR algorithms. This is the first comprehensive evaluation benchmark covering a large number of clinically realistic metal artifact scenarios.

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.

With the rapid increase of orthopedic metal implants, more and more computed tomography (CT) scans of such patients with metal implantation were conducted. However, CT metal artifact caused by these implants would interfer clinical diagnosis and treatment. In order to solve this problem, large amount of researches had been carried out to reduce the metal artifact in the aspect of engineers and doctors in recent years. In the view of engineers, methods mainly include: transformation of the type and the property of CT machine such as dual-energy CT (DECT) or cone beam CT (CBCT), instead of spiral CT. The methods of doctors mainly include: the transformation of CT scanning parameters, such as tube voltage, field of view (FOV), layer thickness; the CT post-processing software such as Orthopedics Metal Artifact Reduction (O-MAR) which was applied comprehensively in orthopedics in recent years. Domestic and international progress of relevant studies on metal artifact reduction technology will be demonstrated in both aspects of engineers and doctors. Final reference for reduction of metal artifact in orthopedic application will be provided based on the above methods. Key words: Metal implant; Computed tomography; Metal artifact; Orthopedics