A prior-based metal artifact reduction algorithm for x-ray CT.

Assessment of cardiac implantable electric device lead perforation using a metal artifact reduction algorithm in cardiac computed tomography

In patients bearing metallic implants, CT images reconstructed by the filtered back-projection algorithm usually suffer substantially from streaking metal artifacts. The CT-based attenuation correction of PET using such images can lead to pseudo-uptakes and thus equivocal findings. In this paper, we introduce a new metal artifact reduction (MAR) algorithm based on Bayesian iterative restoration techniques applied in the sinogram space. We proposed a Sobolev prior in the maximum a posteriori (MAP) estimation of the projections corrupted by metallic implants. The Sobolev prior was invoked in order to impose the a priori knowledge that a CT sinogram is a smooth dataset in which it is highly probable that neighboring projections have similar photon counts. We compared the proposed prior with a total variation (TV) one which imposes piece-wise smoothness on the sinograms being restored. We also compared it with a smoothed TV (TVs) prior which ranks between the Sobolev and TV ones. We formulated the MAP estimation as a convex constrained optimization problem and solved it for the Sobolev and TVs priors by a projected gradient descent algorithm and for the TV/s priors by a sophisticated primal-dual projected gradient algorithm. The results of artifact simulations in a real CT image showed that the Sobolev-based MAR algorithm outperforms its TV and TVs-based counterparts in terms of convergence rate and is comparable with the TVs in projection recovery. It was demonstrated that the proposed MAR algorithm has high applicability in fast and efficient CT-based attenuation correction of PET data.

The aim of the present study was to investigate the impact of metal artifact reduction (MAR) algorithm of cone-beam computed tomography (CBCT) on the diagnostic accuracy of fenestration and dehiscence around dental implants. Twenty-three dental implants were placed adjacent to the dehiscence and 23 adjacent to the fenestration defects on bovine bone blocks. The blocks were scanned with CBCT unit in two modes, with and without MAR algorithm. The area under the receiver operator characteristic (ROC) curves (Az value), specificity, sensitivity, positive predictive value, negative predictive value, and accuracy were determined for all modes. For both defects, the Az values were higher in off MAR condition. The values of sensitivity, positive predictive value, negative predictive value, and accuracy, were higher in off MAR condition for both defects. However, the specificity in both defects in the two modes was equal. The MAR algorithm in CBCT unit may not be helpful in increasing the diagnostic accuracy of fenestration and dehiscence around dental implants.

Background and purposeTwo techniques for metal artefact reduction for computed tomography were studied in order to identify their impact on tumour delineation in radiotherapy. Materials and methodsUsing specially designed phantoms containing metal implants (dental, spine and hip) as well as patient images, we investigated the impact of two methods for metal artefact reduction on (A) the size and severity of metal artefacts and the accuracy of Hounsfield Unit (HU) representation, (B) the visual impact of metal artefacts on image quality and (C) delineation accuracy. A metal artefact reduction algorithm (MAR) and two types of dual energy virtual monochromatic (DECT VM) reconstructions were used separately and in combination to identify the optimal technique for each implant site. ResultsThe artefact area and severity was reduced (by 48–76% and 58–79%, MAR and DECT VM respectively) and accurate Hounsfield-value representation was increased by 22–82%. For each energy, the observers preferred MAR over non-MAR reconstructions (p < 0.01 for dental and hip cases, p < 0.05 for the spine case). In addition, DECT VM was preferred for spine implants (p < 0.01). In all cases, techniques that improved target delineation significantly (p < 0.05) were identified. ConclusionsDECT VM and MAR techniques improve delineation accuracy and the optimal of reconstruction technique depends on the type of metal implant.

A newly-developed metal artifact reduction algorithm improves the visibility of oral cavity lesions on 320-MDCT volume scans

Computed tomographic imaging of tissue surrounding metallic implants is often limited by metal artifacts. This paper compares 3 existing metal artifact reduction techniques that are based on segmentation of metal-affected regions in native images, followed by reprojection of segmented areas into original Radon space, removal of metal trace(s), and renewed reconstruction: Detector row-wise linear interpolation, 2-dimensional interpolation, and combination of row-wise linear interpolation and adaptive filtering. For each method, improvements of CT number accuracy and signal-noise as well as contrast-noise ratios near the prosthesis and in the image periphery over the values found for native images were evaluated in a phantom experiment simulating osteolytic bone lesions of different size and density around a Chrome-Cobalt hip prosthesis stem. Improvement in diagnostic usability was evaluated as lesion detectability by size. Quantitative and qualitative results showed that the linear interpolation and the combination method removed the artifacts most effectively. The mean accuracy error over different regions of interest placed in the direct vicinity of the metal and in the periphery of the object decreased 10-fold with linear interpolation. These methods increased contrast-noise ratio up to 68% of that measured on artifact-free images for the least dense lesion. Qualitatively, the linear interpolation and the combination method improved the lesion detectability and enabled differentiation of different lesion densities. However, in proximity to the stem, some artifacts remained for all methods. We conclude that published algorithms for metal artifact reduction substantially improve image quality for CT imaging of a metallic object and may be adequate for quantitative measurements except for the direct vicinity of the metallic object.

Metal in CT-imaged objects drastically reduces the quality of these images due to the severe artifacts it can cause. Most metal artifacts reduction (MAR) algorithms consider the metal-affected sinogram portions as the corrupted data and replace them via sophisticated interpolation methods. While these schemes are successful in removing the metal artifacts, they fail to recover some of the edge information. To address these problems, the frequency shift metal artifact reduction algorithm (FSMAR) was recently proposed. It exploits the information hidden in the uncorrected image and combines the high frequency (edge) components of the uncorrected image with the low frequency components of the corrected image. Although this can effectively transfer the edge information of the uncorrected image, it also introduces some unwanted artifacts. The essential problem of these algorithms is that they lack the capability of detecting the artifacts and as a result cannot discriminate between desired and undesired edges. We propose a scheme that does better in these respects. Our Metal Artifact Detection and Reduction (MADR) scheme constructs a weight map which stores whether a pixel in the uncorrected image belongs to an artifact region or a non-artifact region. This weight matrix is optimal in the Linear Minimum Mean Square Sense (LMMSE). Our results demonstrate that MADR outperforms the existing algorithms and ensures that the anatomical structures close to metal implants are better preserved.

PurposeThe aim of this study was to assess artifacts generated in cone-beam computed tomography (CBCT) of 3 types of dental implants using 3 metal artifact reduction (MAR) algorithm conditions (pre-acquisition MAR, postacquisition MAR, and no MAR), and 2 peak kilovoltage (kVp) settings.Materials and MethodsTitanium-zirconium, titanium, and zirconium alloy implants were placed in a dry mandible. CBCT images were acquired using 84 and 90 kVp and at normal resolution for all 3 MAR conditions. The images were analyzed using ImageJ software (National Institutes of Health, Bethesda, MD) to calculate the intensity of artifacts for each combination of material and settings. A 3-factor analysis of variance model with up to 3-way interactions was used to determine whether there was a statistically significant difference in the mean intensity of artifacts associated with each factor.ResultsThe analysis of all 3 MAR conditions showed that using no MAR resulted in substantially more severe artifacts than either of the 2 MAR algorithms for the 3 implant materials; however, there were no significant differences between pre- and post-acquisition MAR. The 90 kVp setting generated less intense artifacts on average than the 84 kVp setting. The titanium-zirconium alloy generated significantly less intense artifacts than zirconium. Titanium generated artifacts at an intermediate level relative to the other 2 implant materials, but was not statistically significantly different from either.ConclusionThis in vitro study suggests that artifacts can be minimized by using a titanium-zirconium alloy at the 90 kVp setting, with either MAR setting.

The impact of a metal artefact reduction algorithm on treatment planning for patients undergoing radiotherapy of the pelvis.

Attenuation correction of PET data requires accurate determination of the attenuation map (mumap), which represents the spatial distribution of linear attenuation coefficients of different tissues at 511 keV. The presence of high-density metallic dental filling material in head and neck X-ray computed tomography (CT) scanning is known to generate streak artefacts in the resulting CT images and thus in the corresponding mumaps generated using CT-based attenuation correction. Consequently, an under/overestimation of activity concentration occurs in corresponding regions of the corrected PET images. The purpose of this study is to develop a simple yet practical approach for reduction of metallic dental implant artefacts in the generated mumaps. Currently available sinogram-based metal artefact reduction (MAR) algorithms operate directly on the raw sinograms. These usually consist of huge files stored in proprietary format not easily disclosed by the manufacturers and thus are not straightforward to read and manipulate. The proposed method uses the concept of virtual sinograms produced by forward projection of CT images in Dicom format for MAR. The projection data affected by metallic objects are detected in the sinogram space through segmentation of metallic objects in the CT image followed by forward projection of the metal-only image. Thereafter, the affected sinogram bins are replaced by interpolated values from adjacent projections using the spline interpolation technique. The algorithm was assessed using a polyethylene phantom containing materials simulating different tissues and a dedicated jaw phantom scanned before and after the insertion of metallic objects, where the corrected and noncorrected mumaps were compared with the artefact-free mumap. In addition, the Jaszczak and standard germanium phantoms including four metallic inserts were scanned on a PET/CT scanner to evaluate the impact of the MAR procedure on PET data through the comparison of uncorrected and corrected PET images to the actual activity concentrations in the phantoms. The proposed algorithm was also applied to head and neck CT images of 10 patients with metallic dental implants. The MAR method proved to be practical in a clinical setting and reduced substantially the visible metal induced artefacts. The mean relative error in regions close to metallic objects is reduced by approximately 90%. The statistical analysis of the Jaszczak and solid Ge-68 phantoms PET images did not reveal statistically significant differences between the corrected and artefact-free images (P>0.05). Moreover, the evaluation of clinical studies did not reveal statistically significant differences between the attenuation coefficients of the corrected mumaps and the expected theoretical values. The proposed MAR method provides a simple and convenient approach allowing correction for the presence of metal artefacts caused by dental implants without the need to manipulate the complex raw CT data. Further evaluation using a larger clinical PET/CT database is under way to evaluate the potential of the technique in a clinical setting.

Background: Beam hardening and scattering artifacts from high-density objects such as dental implants adversely affect the image quality and subsequently the detection of fenestration or dehiscence around dental implants. Objective: This study aimed to assess the efficacy of metal artifact reduction (MAR) algorithm of two cone-beam computed tomography (CBCT) systems for detection of peri-implant fenestration and dehiscence.Material and Methods: In this experimental study, thirty-six titanium implants were placed in bone blocks of bovine ribs. Fenestration and dehiscence were created in the buccal bone around implants.CBCT images were obtained using Cranex 3D and ProMax 3D CBCT systems with and without MAR algorithm. Two experienced radiologists observed the images. Data were analyzed using SPSS software.The Kappa coefficient of agreement, the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy of different imaging modalities were calculated and analyzed. Results: In both CBCT systems, the use of MAR algorithm decreased the area under the ROC curve and subsequently the diagnostic accuracy for the detection of fenestration and dehiscence.The sensitivity, specificity and accuracy of both CBCT systems were higher in absence of the MAR algorithm. The specificity of ProMax 3D for detection of fenestration was equal with/without the MAR algorithm. Conclusion: Although CBCT is suitable for detection of peri-implant defects, the application of the MAR algorithm does not enhance the detection of peri-implant fenestration and dehiscence.

Objectives: To compare image quality between filtered back projection (FBP) and iterative reconstruction algorithm and dedicated metal artifact reduction (iMAR) algorithms during antenna positioning for computed tomography-guided microwave ablation (MWA).Materials and methods: An MWA antenna was positioned in the liver of five pigs under CT guidance. Different exposure settings (120kVp/200mAs-120kVp/50mAs) and image reconstruction techniques (FBP, iterative reconstruction with and without iMAR) were applied. Quantitative image analysis included density measurements in six positions (e.g., liver in extension of the antenna [ANTENNA] and liver >3 cm away from the antenna [LIVER-1]). Qualitative image analysis included assessment of overall quality, image noise, artifacts at the antenna tip, artifacts in liver parenchyma bordering antenna tip and newly generated artifacts. Two independent observers performed the analyses twice and interreader agreement was compared with Bland–Altman analysis.Results: For all exposure and reconstruction settings, density measurements for ANTENNA were significantly higher for the I30-1 iMAR compared with FBP and I30-1 (e.g., 8.3−17.2HU vs. −104.5 to 155.1HU; p ≤ 0.01, respectively). In contrast, for all exposure settings, density measurements for LIVER-1 were comparable between FBP and I30-1 iMAR (e.g., 49.4−50.4HU vs. 50.1−52.5U, respectively). For all exposure and reconstruction settings, subjective image quality for LIVER-1 was better for the I30-1 iMAR algorithm compared with FBP and I30-1. Bland–Altman interobserver agreement was from −0.2 to 0.2 for FBP and iMAR, and Cohen’s kappa was 0.74.Conclusion: Iterative algorithms I30-1 with iMAR algorithm improves image quality during antenna positioning and placement for CT-guided MWA and is applicable over a range of exposure settings.

Increased computational power allows computed tomography (CT) software to process very advanced mathematical algorithms to generate better quality images at lower doses. One such algorithm, iterative metal artifact reduction (iMAR) has proven to decrease metal artifacts seen in CT images of adults with orthopedic implants. To evaluate artifact reduction capability of the algorithm in lower-dose pediatric CT compared to our routine third-generation advanced modeled iterative reconstruction (ADMIRE) algorithm. Thirteen children (11-17years old) with metal implants underwent routine clinically indicated CT. Data sets were reconstructed with an iMAR algorithm. Hounsfield units and image noise were measured in bone, muscle and fat in the streak artifact (near the implant) and at the greatest distance from the artifact (far from the implant). A regression model compared the effects of the algorithm (standard ADMIRE vs. iMAR) near and far from the implant. Near the implant, Hounsfield units with iMAR were significantly different in our standard ADMIRE vs. iMAR for bone, muscle and fat (P<0.001). Noise was significantly different in standard ADMIRE vs. iMAR in bone (P<0.003). Far from the implant, Hounsfield units and noise were not significantly different for ADMIRE vs. iMAR, for the three tissue types. These preliminary results demonstrate that iMAR algorithms improves Hounsfield units near the implant and decreases image noise in bone in low-dose pediatric CT. It does this without changing baseline tissue density or noise far from the implant.

Metal artifact reduction techniques in musculoskeletal CT-imaging

ObjectiveTo evaluate the efficacy of single-energy metal artifact reduction (SEMAR) algorithm in removing metallic artifacts for patients with novel leadless pacemakers (PMs) compared to those with conventional transvenous PMs.Methods30 patients with transvenous PMs and 30 patients with leadless PMs having coronary CT angiography (CCTA) were included. Images were reconstructed using two methods: a conventional hybrid-iterative reconstruction (HIR) algorithm separately, and the HIR combined with SEMAR algorithm. The artifact index (AI) was calculated for both datasets to quantitatively assess the artifact reduction. Two senior radiologists independently scored the artifact extent and diagnostic confidence of surrounding tissues. The position of artifacts-impaired tissues was also visually evaluated as follows: non-impaired, impaired, and equivocal. The study was ethics committee-approved.ResultsThe leadless PMSEMAR exhibited significantly lower AIs than the leadless PMnon−SEMAR, with an improvement of hypodense artifacts and a significant reduction of hyperdense artifacts (all p < 0.001). The AIs of leadless PMs performed with and without SEMAR were slightly lower than those of transvenous PMs, although no statistical difference existed (all p > 0.05). Qualitatively, the artifact extent of SEMAR significantly outperformed non-SEMAR in both types of PM (p < 0.001). The aorta, right coronary artery, right atrium, and left ventricle were less impaired in leadless PMnon−SEMAR compared to transvenous PMnon−SEMAR (all p < 0.05).ConclusionsSEMAR provides effective reduction of metallic artifacts from both types of cardiac PMs. The artifact-impaired tissues are fewer on leadless PMs, resulting in an enhanced diagnostic confidence in the surrounding tissues.