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The geometry of (a) an equiangular fan-beam (all angles are positive as shown). (b) Opposite angular positions.
Source publication
To present a conceptually new method for metal artifact reduction (MAR) that can be used on patients with multiple objects within the scan plane that are also of small sized along the longitudinal (scanning) direction, such as dental fillings.
The proposed algorithm, named opposite view replacement, achieves MAR by first detecting the projection da...
Contexts in source publication
Context 1
... the idea of using uncorrupted data along the scanning axis remains valid in a multislice CT; the algorithm to find the uncorrupted data will simply change. In single-slice helical scanning, the patient table is moved continuously as the tube and 1D detector array rotate around the patient. The geometry of this scanning technique is shown in Fig. 2a. In this discussion, an equiangular fanbeam geometry in which the detectors lie on an arc of a circle is considered. The fundamentals of fan-beam projection can be found in Ref. 12. Let the x rays project into the xy plane and the direction normal to the scan plane be z. The view and detector angles are denoted by ranging from 0 to 2 ...Context 2
... question is how to compute the opposite view of a projection since in a fan-beam scanner the opposite views are not exactly 180° apart. Figure 2b shows the corresponding paths for computing the opposite angular positions. As can be seen, the opposite view of an x-ray beam or a projection depends on the fan angle or of this beam in the x-ray source. ...Similar publications
This study proposed a metal artifact reduction (MAR) method based on noncoplanar scanning for cone beam computed tomography (CBCT) imaging. Cylindrical aluminum and stainless steel bars were inserted into a phantom for cross-sectional and sagittal CBCT scanning. The metal bars were removed from scanning to obtain real CBCT images without metal arti...
One limitation of positron emission tomography (PET) imaging of the torso is patient motion. Motion-compensated image reconstruction (MCIR) is one method employed to reduce the deleterious effects of motion. Existing MCIR algorithms use a single sensitivity correction term, which provides inexact normalization for multigate data. Consequently, in t...
The streak artifacts caused by dental fillings are known to generate artifacts in attenuation maps thus leading to overestimation/underestimation of tracer uptake in resulting PET images. Correction of these artifacts is therefore mandatory to achieve accurate attenuation maps to be used by the CT-based attenuation correction procedure. Our group r...
Objectives:
To evaluate metal artefact reduction (MAR) techniques from four computed tomography (CT) vendors in hip prosthesis imaging.
Methods:
Bilateral hip prosthesis phantom images, obtained by using MAR algorithms for single energy CT data or dual energy CT (DECT) data and by monoenergetic reconstructions of DECT data, were visually graded...
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 shap...
Citations
... Traditional metal artifact reduction (MAR) algorithms mainly include the interpolation method and iterative method (6)(7)(8), which often introduce new artifacts into images, resulting in image distortion (9)(10)(11)(12). In recent years, deep learning technology has developed rapidly and has been widely applied in the field of image processing; it has provided new ideas for MAR in CT images. ...
Purpose
To develop a metal artifact reduction (MAR) algorithm and eliminate the adverse effects of metal artifacts on imaging diagnosis and radiotherapy dose calculations.
Methods
Cycle-consistent adversarial network (CycleGAN) was used to generate synthetic CT (sCT) images from megavoltage cone beam CT (MVCBCT) images. In this study, there were 140 head cases with paired CT and MVCBCT images, from which 97 metal-free cases were used for training. Based on the trained model, metal-free sCT (sCT_MF) images and metal-containing sCT (sCT_M) images were generated from the MVCBCT images of 29 metal-free cases and 14 metal cases, respectively. Then, the sCT_MF and sCT_M images were quantitatively evaluated for imaging and dosimetry accuracy.
Results
The structural similarity (SSIM) index of the sCT_MF and metal-free CT (CT_MF) images were 0.9484, and the peak signal-to-noise ratio (PSNR) was 31.4 dB. Compared with the CT images, the sCT_MF images had similar relative electron density (RED) and dose distribution, and their gamma pass rate (1 mm/1%) reached 97.99% ± 1.14%. The sCT_M images had high tissue resolution with no metal artifacts, and the RED distribution accuracy in the range of 1.003 to 1.056 was improved significantly. The RED and dose corrections were most significant for the planning target volume (PTV), mandible and oral cavity. The maximum correction of Dmean and D50 for the oral cavity reached 90 cGy.
Conclusions
Accurate sCT_M images were generated from MVCBCT images based on CycleGAN, which eliminated the metal artifacts in clinical images completely and corrected the RED and dose distributions accurately for clinical application.
... Apart from cone-beam artifacts, there are many other types of artifacts in dental CT images such as metal artifacts [24][25][26], motion artifacts [27][28][29], and limited-view-induced streak artifacts [30][31][32]. These artifacts can also induce errors in the 3D models. ...
Cone-beam dental CT can provide high-precision 3D images of the teeth and surrounding bones. From the 3D CT images, 3D models, also called digital impressions, can be computed for CAD/CAM-based fabrication of dental restorations or orthodontic devices. However, the cone-beam angle-dependent artifacts, mostly caused by the incompleteness of the projection data acquired in the circular cone-beam scan geometry, can induce significant errors in the 3D models. Using a micro-CT, we acquired CT projection data of plaster cast models at several different cone-beam angles, and we investigated the dependency of the model errors on the cone-beam angle in comparison with the reference models obtained from the optical scanning of the plaster models. For the 3D CT image reconstruction, we used the conventional Feldkamp algorithm and the combined half-scan image reconstruction algorithm to investigate the dependency of the model errors on the image reconstruction algorithm. We analyzed the mean of positive deviations and the mean of negative deviations of the surface points on the CT-image-derived 3D models from the reference model, and we compared them between the two image reconstruction algorithms. It has been found that the model error increases as the cone-beam angle increases in both algorithms. However, the model errors are smaller in the combined half-scan image reconstruction when the cone-beam angle is as large as 10 degrees
... Comparing the SD of regions provides information about the discrepancy between CT numbers of similar regions before and after correction. Also, root mean square error (RMSE), normalized root mean square difference (NRMSD), and mean absolute deviation, peak signal to noise ratio (PSNR) are a prevalent metrics to evaluate the difference between the two images and have been extensively used for MAR validation [10][11][12][13][14][15]. In a recent study other metric has been introduced, which is the universal quality index (UQI) [15]. ...
Image quality (IQ) assessment plays an important role in the medical world. New methods to evaluate image quality have been developed, but their application in the context of computer tomography is yet limited. In this paper the performance of fifteen well-known full reference (FR) IQ metrics is compared with human judgment using images affected by metal artifacts and processed with metal artifact reduction methods from a phantom. Five region of interest with different sizes were selected. IQ was evaluated by seven experienced radiologists completely blinded to the information. To measure the correlation between FR-IQ, and the score assigned by radiologists non-parametric Spearman rank-order correlation coefficient and Kendall’s Rank-order Correlation coefficient were used; so as root mean square error and the mean absolute error to measure the prediction accuracy. Cohen’s kappa was employed with the purpose of assessing inter-observer agreement. The metrics GMSD, IWMSE, IWPSNR, WSNR and OSS-PSNR were the best ranked. Inter-observer agreement was between 0.596 and 0.954, with p<0.001 in all study. The objective scores predicted by these methods correlate consistently with the subjective evaluations. The application of this metrics will make possible a better evaluation of metal artifact reduction algorithms in future works.
... Artifacts caused by the presence of dentals affect the calculation accuracy when attempting to obtain an accurate dose distribution. 7,8 In recent years, several techniques, such as metal artifact reduction (MAR), have been reported [9][10][11][12] to reduce the influence of metal artifacts during CT imaging. However, the MAR function is not available in all facilities that perform radiotherapy. ...
Purpose:
During treatment planning for head-and-neck volumetric-modulated arc therapy (VMAT), manual contouring of the metal artifact area of artificial teeth is done, and the area is replaced with water computed tomography (CT) values for dose calculation. This contouring of the metal artifact areas, which is performed manually, is subject to human variability. The purpose of this study is to evaluate and analyze the effect of inter-observer variation on dose distribution.
Methods:
The subjects were 25 cases of cancer of the oropharynx for which VMAT was performed. Six radiation oncologists (ROs) performed metal artifact contouring for all of the cases. Gross tumor volume, clinical target volume, planning target volume (PTV), and oral cavity were evaluated. The contouring of the six ROs was divided into two groups, small and large groups. A reference RO was determined for each group and the dose distribution was compared with those of the other radiation oncologists by gamma analysis (GA). As an additional experiment, we changed the contouring of each dental metal artifact area, creating enlarged contours (L), reduced contours (S), and undrawn contours (N) based on the contouring by the six ROs and compared these structure sets.
Results:
The evaluation of inter-observer variation showed no significant difference between the large and small groups, and the GA pass rate was 100%. Similar results were obtained comparing structure sets L and S, but in the comparison of structure sets L and N, there were cases with pass rates below 70%.
Conclusions:
The results show that the artificial variability of manual artificial tooth metal artifact contouring has little effect on the dose distribution of VMAT. However, it should be noted that the dose distribution may change depending on the contouring method in cases where the overlap between PTV and metal artifact areas is large.
... 11 Metal artifact reduction is a topic of active research; a large number of artifact reduction algorithms have been developed in recent years. [12][13][14][15][16][17][18][19][20] They mitigate the effects of metallic objects, but the magnitude of improvement depends on the details of the algorithm and the clinical situation; an algorithm that performs well in one case may yield only a negligible improvement under different circumstances. [21][22] This study seeks to quantify the effects of using Orthopedic Metal Artifact Reduction (O-MAR), 23 a commercially available algorithm developed by Philips Healthcare, on the dosimetry and ease of contouring in head and neck cancer patients. ...
Metallic objects, such as dental fillings, cause artifacts in computed tomography (CT) scans. We quantify the contouring and dosimetric effects of Orthopedic Metal Artifact Reduction (O-MAR), in head and neck radiotherapy.
The ease of organ contouring was assessed by having a radiation oncologist identify the CT data set with or without O-MAR for each of 28 patients that was easier to contour. The effect on contouring was quantified further by having the physician recontour parotid glands, previously drawn by him on the O-MAR scans, on uncorrected scans, and calculating the Dice coefficent (a measure of overlap) for the contours.
Radiotherapy plans originally generated on scans reconstructed with O-MAR were recalculated on scans without metal artifact correction. The study was done using the Analytical Anisotropic Algorithm (AAA) dose calculation algorithm. The 15 patients with a planning target volume (PTV) extending to the same slice as the artifacts were used for this part of the study.
The normal tissue doses were not significantly affected. The PTV mean dose and V95 were not affected, but the cold spots became less severe in the O-MAR corrected plans, with the minimum point dose on average being 4.1% higher. In 79% of the cases, the radiation oncologist identified the O-MAR scan as easier to contour; in 11% he chose the uncorrected scan and in 11% the scans were judged to have equal quality. A total of nine parotid glands (on both scans—18 contours in total) in 5 patients were recontoured. The average Dice coefficient for parotids drawn with and without O-MAR was found to be 0.775 +/− 0.045.
The O-MAR algorithm does not produce a significant dosimetric effect in head and neck plans when using the AAA dose calculation algorithm. It can therefore be used for improved contouring accuracy without updating the critical structure tolerance doses and target coverage expectations.
... There are a few MAR algorithms developed and validated for dental CT [21][22][23][24]. Tohnak et al. [21] developed a sequential substitution algorithm that replaces the corrupted projection data with the adjacent uncorrected projection data. ...
... Therefore, this method highly depends on the metal segmentation accuracy. Yazdi et al. [22] also developed an algorithm that replaces the corrupted projection data with the values from the opposed projection data. They claimed that the opposite projection values are a better approximation than the linear interpolation. ...
Unlike medical computed tomography (CT), dental CT often suffers from severe metal artifacts stemming from high-density materials employed for dental prostheses. Despite the many metal artifact reduction (MAR) methods available for medical CT, those methods do not sufficiently reduce metal artifacts in dental CT images because MAR performance is often compromised by the enamel layer of teeth, whose X-ray attenuation coefficient is not so different from that of prosthetic materials. We propose a deep learning-based metal segmentation method on the projection domain to improve MAR performance in dental CT. We adopted a simplified U-net for metal segmentation on the projection domain without using any information from the metal-artifacts-corrupted CT images. After training the network with the projection data of five patients, we segmented the metal objects on the projection data of other patients using the trained network parameters. With the segmentation results, we corrected the projection data by applying region filling inside the segmented region. We fused two CT images, one from the corrected projection data and the other from the original raw projection data, and then we forward-projected the fused CT image to get the fused projection data. To get the final corrected projection data, we replaced the metal regions in the original projection data with the ones in the fused projection data. To evaluate the efficacy of the proposed segmentation method on MAR, we compared the MAR performance of the proposed segmentation method with a conventional MAR method based on metal segmentation on the CT image domain. For the MAR performance evaluation, we considered the three primary MAR performance metrics: the relative error (REL), the sum of square difference (SSD), and the normalized absolute difference (NAD). The proposed segmentation method improved MAR performances by around 5.7% for REL, 6.8% for SSD, and 8.2% for NAD. The proposed metal segmentation method on the projection domain showed better MAR performance than the conventional segmentation on the CT image domain. We expect that the proposed segmentation method can improve the performance of the existing MAR methods that are based on metal segmentation on the CT image domain.
... For these studies that excluded the artifact-affected slices or patients, manual filtering was applied, which is a very time-consuming process. There are also some approaches proposed for the metal artifacts reduction (MAR) [8][9][10][11][12]. Yet, these methods are likely to introduce new artifacts to images, degrade their resolution, and influence the statistical distribution of the original images, rendering them detrimental to any subsequent radiomic analysis [13,14]. ...
Background and purpose
Computed tomography (CT) radiomics of head and neck cancer (HNC) images is susceptible to dental implant artifacts. This work devised and validated an automated algorithm to detect CT metal artifacts and investigate their impact on subsequent radiomics analyses. A new method based on features from total variation, gradient directional distribution, and Hough transform was developed and evaluated.
Materials and methods
Two HNC datasets were analyzed: a training set of 131 patients for developing the detection algorithm and a testing set of 220 patients. Seven designated features were extracted from ROIs (regions of interest) and machine learning with random forests was used for building the artifact detection algorithm. Performance was assessed using the area under the receiver operating characteristics curve (AUC).
Results
The testing results of artifacts detection yielded a cross-validated AUC of 0.91 (95% CI: 0.89–0.94), and a test AUC of 0.89. External testing validation yielded an accuracy of 0.82. For radiomics model prediction, training with artifacts yielded an AUC of 0.64 (95% CI: 0.63–0.65), while training on images without artifacts improved the AUC to 0.75 (95% CI: 0.74–0.76). This was compared to visual inspection of artifacts (AUC = 0.71 [95% CI: 0.69–0.73]).
Conclusion
We developed a new method for automated and efficient detection of streak artifacts. We also showed that such streak artifacts in HNC CT images can worsen the performance of radiomics modeling.
... In many MAR algorithms for a cone-beam CT, metal regions are segmented on the uncorrected CT images then the binary images generated from segmentation is reprojected to identify the metal trace on the projection images [12], [15], [21], [23], [24], [118]. It is possible to apply segmentation directly in the projection images [119], [120]. ...
In a dental CT scan, the presence of dental fillings or dental implants generates severe metal artifacts that often compromise readability of the CT images. Many metal artifact reduction (MAR) techniques have been introduced, but dental CT scans still suffer from severe metal artifacts particularly when multiple dental fillings or implants exist around the region of interest. The high attenuation coefficient of teeth often causes erroneous metal segmentation, compromising the MAR performance. We proposed a metal segmentation method for a dental CT that is based on dual-energy imaging with a narrow energy gap. Unlike a conventional dual-energy CT, we acquired two projection data sets at two close tube voltages (80kVp and 90kVp), and then, we computed the difference image between the two projection images with an optimized weighting factor so as to maximize the contrast of the metal regions. We reconstructed the CT images from the weighted difference image to identify the metal region with global thresholding. We forward projected the identified metal region to designate metal trace on the projection image. We substituted the pixel values on the metal trace with the ones computed by the region filling method. The region filling in the metal trace removes high-intensity data made by the metallic objects from the projection image. We reconstructed the final CT images from the region-filled projection image with the fusion-based approach. We made simulation phantoms to test the performance of the proposed MAR and show the effect of segmentation error on the metal artifact reduction. We have done imaging experiments on a dental phantom and a human skull phantom using lab-built micro-CT systems and a commercial dental CT system. We also used a guinea pig skull phantom for evaluation purpose, as it was easy to be accessed.
We have corrected the simulated projection data of the simulation phantoms using mis-segmented and well-segmented metal object to evaluate the step of metal artifact reduction.We also corrected the projection images of a dental phantom and a human skull phantom using the single-energy and dual-energy-based metal segmentation methods. The single-energy-based method often failed in correcting the metal artifacts on the slices on which tooth enamel exists. The dual-energy-based method showed better MAR performances in all cases regardless of the presence of tooth enamel on the slice of interest. We have compared the MAR performances between both methods in terms of the relative error (REL), the sum of squared difference (SSD) and the normalized absolute difference (NAD). For the dental phantom images corrected by the single-energy-based method, the metric values were 95.3%, 94.5%, and 90.6%, respectively, while they were 90.1%, 90.05%, and 86.4%, respectively, for the images corrected by the dual-energy-based method. For the human skull phantom images, the metric values were improved from 95.6%, 91.5%, and 89.6%, respectively, to 88.2%, 82.5%, and 81.3%, respectively. The proposed dual-energy-based method has shown better performance in metal segmentation leading to better MAR performance in dental imaging. We corrected the projection data of the guinea pig skull phantom that have metal object using LI MAR and the proposed MAR. Then we compared the result with respect to the CT image reconstructed from the projection data without a metal object. The proposed MAR showed better performance over the LI MAR which is widely used in the clinical field. We expect the proposed metal segmentation method can be used to improve the MAR performance of existing MAR techniques that have metal segmentation steps in their correction procedures.
... In many MAR algorithms for a cone-beam CT, metal regions are segmented on the initial CT images to identify the metal trace on the projection image. 25,29,33,[37][38][39] If there are mis-segmentations of metal regions on the CT images, the metal traces identified on the projection images will be erroneous, and they will compromise the MAR performance. On dental CT images, it is challenging to make accurate metal segmentation especially when there are multiple metallic objects. ...
Purpose
In a dental CT scan, the presence of dental fillings or dental implants generates severe metal artifacts that often compromise readability of the CT images. Many metal artifact reduction (MAR) techniques have been introduced, but dental CT scans still suffer from severe metal artifacts particularly when multiple dental fillings or implants exist around the region of interest. The high attenuation coefficient of teeth often causes erroneous metal segmentation, compromising the MAR performance. We propose a metal segmentation method for a dental CT that is based on dual‐energy imaging with a narrow energy gap.
Methods
Unlike a conventional dual‐energy CT, we acquire two projection data sets at two close tube voltages (80 and 90 kVp), and then, we compute the difference image between the two projection images with an optimized weighting factor so as to maximize the contrast of the metal regions. We reconstruct CT images from the weighted difference image to identify the metal region with global thresholding. We forward project the identified metal region to designate metal trace on the projection image. We substitute the pixel values on the metal trace with the ones computed by the region filling method. The region filling in the metal trace removes high‐intensity data made by the metallic objects from the projection image. We reconstruct final CT images from the region‐filled projection image with the fusion‐based approach. We have done imaging experiments on a dental phantom and a human skull phantom using a lab‐built micro‐CT and a commercial dental CT system.
Results
We have corrected the projection images of a dental phantom and a human skull phantom using the single‐energy and dual‐energy‐based metal segmentation methods. The single‐energy‐based method often failed in correcting the metal artifacts on the slices on which tooth enamel exists. The dual‐energy‐based method showed better MAR performances in all cases regardless of the presence of tooth enamel on the slice of interest. We have compared the MAR performances between both methods in terms of the relative error (REL), the sum of squared difference (SSD) and the normalized absolute difference (NAD). For the dental phantom images corrected by the single‐energy‐based method, the metric values were 95.3%, 94.5%, and 90.6%, respectively, while they were 90.1%, 90.05%, and 86.4%, respectively, for the images corrected by the dual‐energy‐based method. For the human skull phantom images, the metric values were improved from 95.6%, 91.5%, and 89.6%, respectively, to 88.2%, 82.5%, and 81.3%, respectively.
Conclusions
The proposed dual‐energy‐based method has shown better performance in metal segmentation leading to better MAR performance in dental imaging. We expect the proposed metal segmentation method can be used to improve the MAR performance of existing MAR techniques that have metal segmentation steps in their correction procedures.
... A variety of methods to reduce metal artifacts in CT have been suggested and are described in the literature (Glover 1981, Kalender et al 1987, Prince and Willsky 1990, Zhao et al 2000, De Man et al 2001, Bazalova et al 2007, Oehler et al 2008, Lemmens et al 2009, Meyer et al 2010, Veldkamp et al 2010, Xu et al 2011, Yazdi et al 2011, Zhang et al 2011, Verburg and Seco 2012. Most methods can generally be categorized into two major groups: (a) sinogram completion methods and (b) model-based iterative algorithms. ...
A significant and increasing number of patients receiving radiation therapy present with metal objects close to, or even within, the treatment area, resulting in artifacts in computed tomography (CT) imaging, which is the most commonly used imaging method for treatment planning in radiation therapy. In the presence of metal implants, such as dental fillings in treatment of head-and-neck tumors, spinal stabilization implants in spinal or paraspinal treatment or hip replacements in prostate cancer treatments, the extreme photon absorption by the metal object leads to prominent image artifacts. Although current CT scanners include a series of correction steps for beam hardening, scattered radiation and noisy measurements, when metal implants exist within or close to the treatment area, these corrections do not suffice. CT metal artifacts affect negatively the treatment planning of radiation therapy either by causing difficulties to delineate the target volume or by reducing the dose calculation accuracy. Various metal artifact reduction (MAR) methods have been explored in terms of improvement of organ delineation and dose calculation in radiation therapy treatment planning, depending on the type of radiation treatment and location of the metal implant and treatment site. Including a brief description of the available CT MAR methods that have been applied in radiation therapy, this article attempts to provide a comprehensive review on the dosimetric effect of the presence of CT metal artifacts in treatment planning, as reported in the literature, and the potential improvement suggested by different MAR approaches. The impact of artifacts on the treatment planning and delivery accuracy is discussed in the context of different modalities, such as photon external beam, brachytherapy and particle therapy, as well as by type and location of metal implants.
