An algorithm for efficient metal artifact reductions in permanent seed

Analysis of objective quality metrics in computed tomography images affected by metal artifacts

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.

The effects of the orthopedic metal artifact reduction (O-MAR) algorithm on contouring and dosimetry of head and neck radiotherapy 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.

Automatic recognition and analysis of metal streak artifacts in head and neck computed tomography for radiomics modeling

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.

Image-based Metal Artifact Reduction in X-ray Computed Tomography utilizing Local Anatomical Similarity

X-ray computed tomography (CT) is widely used in radiation therapy treatment planning in recent years. However, metal implants such as dental fillings and hip prostheses can cause severe bright and dark streaking artifacts in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. In this work, a metal artifact reduction method is proposed based on the intrinsic anatomical similarity between neighboring CT slices. Neighboring CT slices from the same patient exhibit similar anatomical features. Exploiting this anatomical similarity, a gamma map is calculated as a weighted summation of relative HU error and distance error for each pixel in an artifact-corrupted CT image relative to a neighboring, artifact-free image. The minimum value in the gamma map for each pixel is used to identify an appropriate pixel from the artifact-free CT slice to replace the corresponding artifact-corrupted pixel. With the proposed method, the mean CT HU error was reduced from 360 HU and 460 HU to 24 HU and 34 HU on head and pelvis CT images, respectively. Dose calculation accuracy also improved, as the dose difference was reduced from greater than 20% to less than 4%. Using 3%/3mm criteria, the gamma analysis failure rate was reduced from 23.25% to 0.02%. An image-based metal artifact reduction method is proposed that replaces corrupted image pixels with pixels from neighboring CT slices free of metal artifacts. This method is shown to be capable of suppressing streaking artifacts, thereby improving HU and dose calculation accuracy.

Reduce beam hardening artifacts of polychromatic X-ray computed tomography by an iterative approximation approach

BACKGROUND

The beam hardening artifact is one of most important modalities of metal artifact for polychromatic X-ray computed tomography (CT), which can impair the image quality seriously.

OBJECTIVE

An iterative approach is proposed to reduce beam hardening artifact caused by metallic components in polychromatic X-ray CT.

METHODS

According to Lambert-Beer law, the (detected) projections can be expressed as monotonic nonlinear functions of element geometry projections, which are the theoretical projections produced only by the pixel intensities (image grayscale) of certain element (component). With help of a prior knowledge on spectrum distribution of X-ray beam source and energy-dependent attenuation coefficients, the functions have explicit expressions. Newton-Raphson algorithm is employed to solve the functions. The solutions are named as the synthetical geometry projections, which are the nearly linear weighted sum of element geometry projections with respect to mean of each attenuation coefficient. In this process, the attenuation coefficients are modified to make Newton-Raphson iterative functions satisfy the convergence conditions of fixed pointed iteration(FPI) so that the solutions will approach the true synthetical geometry projections stably. The underlying images are obtained using the projections by general reconstruction algorithms such as the filtered back projection (FBP). The image gray values are adjusted according to the attenuation coefficient means to obtain proper CT numbers.

RESULTS

Several examples demonstrate the proposed approach is efficient in reducing beam hardening artifacts and has satisfactory performance in the term of some general criteria. In a simulation example, the normalized root mean square difference (NRMSD) can be reduced 17.52% compared to a newest algorithm.

CONCLUSIONS

Since the element geometry projections are free from the effect of beam hardening, the nearly linear weighted sum of them, the synthetical geometry projections, are almost free from the effect of beam hardening. By working out the synthetical geometry projections, the proposed approach becomes quite efficient in reducing beam hardening artifacts.

A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation

Background

Metal artifacts appearing as streaks and shadows often compromise readability of computed tomography (CT) images. Particularly in a dental CT in which high resolution imaging is crucial for precise preparation of dental implants or orthodontic devices, reduction of metal artifacts is very important. However, metal artifact reduction algorithms developed for a general medical CT may not work well in a dental CT since teeth themselves also have high attenuation coefficients.

Methods

To reduce metal artifacts in dental CT images, we made prior images by weighted summation of two images: one, a streak-reduced image reconstructed from the metal-region-modified projection data, and the other a metal-free image reconstructed from the original projection data followed by metal region deletion. To make the streak-reduced image, we precisely segmented the metal region based on adaptive local thresholding, and then, we modified the metal region on the projection data using linear interpolation. We made forward projection of the prior image to make the prior projection data. We replaced the pixel values at the metal region in the original projection data with the ones taken from the prior projection data, and then, we finally reconstructed images from the replaced projection data. To validate the proposed method, we made computational simulations and also we made experiments on teeth phantoms using a micro-CT. We compared the results with the ones obtained by the fusion prior-based metal artifact reduction (FP-MAR) method.

Results

In the simulation studies using a bilateral prostheses phantom and a dental phantom, the proposed method showed a performance similar to the FP-MAR method in terms of the edge profile and the structural similarity index when an optimal global threshold was chosen for the FP-MAR method. In the imaging studies of teeth phantoms, the proposed method showed a better performance than the FP-MAR method in reducing the streak artifacts without introducing any contrast anomaly.

Conclusions

The simulation and experimental imaging studies suggest that the proposed method can be used for reducing metal artifacts in dental CT images.

Single-energy metal artifact reduction in postimplant computed tomography for I-125 prostate brachytherapy: Impact on seed identification

PURPOSE

To evaluate the effectiveness of the single-energy metal artifact reduction (SEMAR) technique for improving the accuracy of I-125 seed identification in postimplant computed tomography (CT) after prostate brachytherapy.

METHODS AND MATERIALS

Postimplant CT images of 40 patients treated with I-125 prostate brachytherapy were acquired. For all patients, 2 data sets were reconstructed, 1 with SEMAR algorithms (SEMAR image), and the other without SEMAR algorithms (non-SEMAR image). Seed locations are automatically detected by the automatic seed finder tool, and their locations were compared between the SEMAR and non-SEMAR images. Dosimetric parameters using seed locations as detected were compared.

RESULTS

The true-positive fraction of properly detected seeds on the SEMAR image as determined from a reference seed distribution defined by one investigator was significantly higher than the true-positive fraction on the non-SEMAR image (p = 0.011). The variabilities in D₉₀ (p = 0.001), V₁₀₀ (p = 0.007), and V₁₅₀ (p = 0.007) were significantly reduced for seed location on the SEMAR image as compared with non-SEMAR image.

CONCLUSIONS

Prostate postimplant CT with SEMAR improved the accuracy of seed localization and postimplant dosimetric parameters.

Generation of hybrid sinograms for the recovery of kV-CT images with metal artifacts for helical tomotherapy

PURPOSE

The overall goal of this study is to restore kilovoltage computed tomography (kV-CT) images which are disfigured by patients' metal prostheses. By generating a hybrid sinogram that is a combination of kV and megavoltage (MV) projection data, the authors suggest a novel metal artifact-reduction (MAR) method that retains the image quality to match that of kV-CT and simultaneously restores the information of metal prostheses lost due to photon starvation.

METHODS

CT projection data contain information about attenuation coefficients and the total length of the attenuation. By normalizing raw kV projections with their own total lengths of attenuation, mean attenuation projections were obtained. In the same manner, mean density projections of MV-CT were obtained by the normalization of MV projections resulting from the forward projection of density-calibrated MV-CT images with the geometric parameters of the kV-CT device. To generate the hybrid sinogram, metal-affected signals of the kV sinogram were identified and replaced by the corresponding signals of the MV sinogram following a density calibration step with kV data. Filtered backprojection was implemented to reconstruct the hybrid CT image. To validate the authors' approach, they simulated four different scenarios for three heads and one pelvis using metallic rod inserts within a cylindrical phantom. Five inserts describing human body elements were also included in the phantom. The authors compared the image qualities among the kV, MV, and hybrid CT images by measuring the contrast-to-noise ratio (CNR), the signal-to-noise ratio (SNR), the densities of all inserts, and the spatial resolution. In addition, the MAR performance was compared among three existing MAR methods and the authors' hybrid method. Finally, for clinical trials, the authors produced hybrid images of three patients having dental metal prostheses to compare their MAR performances with those of the kV, MV, and three existing MAR methods.

RESULTS

The authors compared the image quality and MAR performance of the hybrid method with those of other imaging modalities and the three MAR methods, respectively. The total measured mean of the CNR (SNR) values for the nonmetal inserts was determined to be 14.3 (35.3), 15.3 (37.8), and 25.5 (64.3) for the kV, MV, and hybrid images, respectively, and the spatial resolutions of the hybrid images were similar to those of the kV images. The measured densities of the metal and nonmetal inserts in the hybrid images were in good agreement with their true densities, except in cases of extremely low densities, such as air and lung. Using the hybrid method, major streak artifacts were suitably removed and no secondary artifacts were introduced in the resultant image. In clinical trials, the authors verified that kV and MV projections were successfully combined and turned into the resultant hybrid image with high image contrast, accurate metal information, and few metal artifacts. The hybrid method also outperformed the three existing MAR methods with regard to metal information restoration and secondary artifact prevention.

CONCLUSIONS

The authors have shown that the hybrid method can restore the overall image quality of kV-CT disfigured by severe metal artifacts and restore the information of metal prostheses lost due to photon starvation. The hybrid images may allow for the improved delineation of structures of interest and accurate dose calculations for radiation treatment planning for patients with metal prostheses.

Patient-specific Monte Carlo dose calculations for (103)Pd breast brachytherapy

This work retrospectively investigates patient-specific Monte Carlo (MC) dose calculations for (103)Pd permanent implant breast brachytherapy, exploring various necessary assumptions for deriving virtual patient models: post-implant CT image metallic artifact reduction (MAR), tissue assignment schemes (TAS), and elemental tissue compositions. Three MAR methods (thresholding, 3D median filter, virtual sinogram) are applied to CT images; resulting images are compared to each other and to uncorrected images. Virtual patient models are then derived by application of different TAS ranging from TG-186 basic recommendations (mixed adipose and gland tissue at uniform literature-derived density) to detailed schemes (segmented adipose and gland with CT-derived densities). For detailed schemes, alternate mass density segmentation thresholds between adipose and gland are considered. Several literature-derived elemental compositions for adipose, gland and skin are compared. MC models derived from uncorrected CT images can yield large errors in dose calculations especially when used with detailed TAS. Differences in MAR method result in large differences in local doses when variations in CT number cause differences in tissue assignment. Between different MAR models (same TAS), PTV [Formula: see text] and skin [Formula: see text] each vary by up to 6%. Basic TAS (mixed adipose/gland tissue) generally yield higher dose metrics than detailed segmented schemes: PTV [Formula: see text] and skin [Formula: see text] are higher by up to 13% and 9% respectively. Employing alternate adipose, gland and skin elemental compositions can cause variations in PTV [Formula: see text] of up to 11% and skin [Formula: see text] of up to 30%. Overall, AAPM TG-43 overestimates dose to the PTV ([Formula: see text] on average 10% and up to 27%) and underestimates dose to the skin ([Formula: see text] on average 29% and up to 48%) compared to the various MC models derived using the post-MAR CT images studied herein. The considerable differences between TG-43 and MC models underline the importance of patient-specific MC dose calculations for permanent implant breast brachytherapy. Further, the sensitivity of these MC dose calculations due to necessary assumptions illustrates the importance of developing a consensus modelling approach.

[Effect of Reconstruction Technique for Metal Artifact Reduction in Computed Tomography by Changing Display Field of View]

We evaluated the effect of orthopedic-metal artifact reduction (O-MAR) for metal artifact in computed tomography with 73 simulated seeds for brachytherapy in different sizes of display field of view (DFOV) obtained by helical scan under the same clinical scan condition. The metal artifacts were analyzed with the Gumbel's method by changing DFOV sizes 80 mm, 160 mm, and 320 mm. Gumbel distribution, scale parameter (γ), and location parameter (β) of the metal artifacts with O-MAR were compared with that of the metal artifacts with filtered back projection (FBP). In conclusion, it was considered that the effect of metal artifact reduction with O-MAR was influenced by DFOV size in this study. The reduction rates of scale parameter (γ) were 22.3%, 21.3%, and 10.0% in DFOV 80 mm, 160 mm, and 320 mm, respectively. The reduction rates of location parameter (β) were 27.4%, 23.4 %, and 9.8%. Therefore, the effect of metal artifact reduction with O-MAR showed the tendency of increasing with decreasing DFOV size.

Spectral CT with monochromatic imaging and metal artifacts reduction software for artifacts reduction of ¹²⁵I radioactive seeds in liver brachytherapy

PURPOSE

To investigate the optimal monochromatic energy for artifacts reduction from (125)I seeds as well as image improvement in the vicinity of seeds on monochromatic images with and without metal artifacts reduction software (MARS) and to compare this with traditional 120-kVp images, so as to evaluate the application value of gemstone spectral imaging for reducing artifacts from (125)I seeds in liver brachytherapy.

MATERIALS AND METHODS

A total of 45 tumors from 25 patients treated with (125)I seed brachytherapy in the liver were enrolled in this study. Multiphasic spectral computed tomography (CT) scanning was performed for each patient. After a delay time of 15 s of portal vein phase, a traditional 120-kVp scan was performed, focusing on several planes of (125)I seeds only. The artifact index (AI) in the vicinity of seeds and the standard deviation (SD) of the CT density of region of interest in the outside liver parenchyma were calculated. Artifact appearance was evaluated and classified on reconstructed monochromatic S and 120-kVp images. Image quality in the vicinity of seeds of three data sets were evaluated using a 1-5 scale scoring method. The Friedman rank-sum test was used to estimate the scoring results of image quality.

RESULTS

The greatest noise in monochromatic images was found at 40 keV (SD = 27.38, AI = 206.40). The optimal monochromatic energy was found at 75 keV, which provided almost the least image noise (SD = 10.01) and good performance in artifact reduction (AI = 102.73). Image noise and AI reduction at 75 keV was decreased by 63.44 and 50.23%, compared with at 40 keV. Near-field thick artifacts were obvious in all 45 lesions, in 120-kVp images, and 75-keV images, but basically reduced in 75 keV MARS images and artifacts completely invisible in 7 lesions. The number of diagnosable images (score ≥3) was significantly more in the 75-keV MARS group (28/45), and the 75-keV group (22/45) than in the 120-kVp group (11/45) (p < 0.0167 for both). Compared with 120-kVp images alone, 75-keV images plus 75-keV MARS images can increase tumor visibility around seeds and increase the proportion of diagnostic images to 84.4% (38/45).

CONCLUSION

Spectral CT producing 75-keV MARS images could substantially reduce near-field thick artifacts caused by (125)I seeds and improve image quality, even to a state of being completely free from artifacts. Spectral CT imaging (with and without MARS) can provide more accurate CT images for estimating efficacy after (125)I seed brachytherapy in the liver.

Reduction of metallic coil artefacts in computed tomography body imaging: effects of a new single-energy metal artefact reduction algorithm

OBJECTIVES

We evaluated the effect of a single-energy metal artefact reduction (SEMAR) algorithm for metallic coil artefact reduction in body imaging.

METHODS

Computed tomography angiography (CTA) was performed in 30 patients with metallic coils (10 men, 20 women; mean age, 67.9 ± 11 years). Non-SEMAR images were reconstructed with iterative reconstruction alone, and SEMAR images were reconstructed with the iterative reconstruction plus SEMAR algorithms. We compared image noise around metallic coils and the maximum diameters of artefacts from coils between the non-SEMAR and SEMAR images. Two radiologists visually evaluated the metallic coil artefacts utilizing a four-point scale: 1 = extensive; 2 = strong; 3 = mild; 4 = minimal artefacts.

RESULTS

The image noise and maximum diameters of the artefacts of the SEMAR images were significantly lower than those of the non-SEMAR images (65.1 ± 33.0 HU vs. 29.7 ± 10.3 HU; 163.9 ± 54.8 mm vs. 10.3 ± 19.0 mm, respectively; P < 0.001). Better visual scores were obtained with the SEMAR technique (3.4 ± 0.6 vs. 1.0 ± 0.0, P < 0.001).

CONCLUSIONS

The SEMAR algorithm significantly reduced artefacts caused by metallic coils compared with the non-SEMAR algorithm. This technique can potentially increase CT performance for the evaluation of post-coil embolization complications.

KEY POINTS

• The new algorithm involves a raw data- and image-based reconstruction technique. • The new algorithm mitigates artefacts from metallic coils on body CT images. • The new algorithm significantly reduced artefacts caused by metallic coils. • The metal artefact reduction algorithm improves CT image quality after coil embolization.

Development of virtual patient models for permanent implant brachytherapy Monte Carlo dose calculations: interdependence of CT image artifact mitigation and tissue assignment

This work investigates and compares CT image metallic artifact reduction (MAR) methods and tissue assignment schemes (TAS) for the development of virtual patient models for permanent implant brachytherapy Monte Carlo (MC) dose calculations. Four MAR techniques are investigated to mitigate seed artifacts from post-implant CT images of a homogeneous phantom and eight prostate patients: a raw sinogram approach using the original CT scanner data and three methods (simple threshold replacement (STR), 3D median filter, and virtual sinogram) requiring only the reconstructed CT image. Virtual patient models are developed using six TAS ranging from the AAPM-ESTRO-ABG TG-186 basic approach of assigning uniform density tissues (resulting in a model not dependent on MAR) to more complex models assigning prostate, calcification, and mixtures of prostate and calcification using CT-derived densities. The EGSnrc user-code BrachyDose is employed to calculate dose distributions. All four MAR methods eliminate bright seed spot artifacts, and the image-based methods provide comparable mitigation of artifacts compared with the raw sinogram approach. However, each MAR technique has limitations: STR is unable to mitigate low CT number artifacts, the median filter blurs the image which challenges the preservation of tissue heterogeneities, and both sinogram approaches introduce new streaks. Large local dose differences are generally due to differences in voxel tissue-type rather than mass density. The largest differences in target dose metrics (D90, V100, V150), over 50% lower compared to the other models, are when uncorrected CT images are used with TAS that consider calcifications. Metrics found using models which include calcifications are generally a few percent lower than prostate-only models. Generally, metrics from any MAR method and any TAS which considers calcifications agree within 6%. Overall, the studied MAR methods and TAS show promise for further retrospective MC dose calculation studies for various permanent implant brachytherapy treatments.

Metal artifact reduction in CT using fusion based prior image

PURPOSE

In computed tomography, metallic objects in the scanning field create the so-called metal artifacts in the reconstructed images. Interpolation-based methods for metal artifact reduction (MAR) replace the metal-corrupted projection data with surrogate data obtained from interpolation using the surrounding uncorrupted sinogram information. Prior-based MAR methods further improve interpolation-based methods by better estimating the surrogate data using forward projections from a prior image. However, the prior images in most existing prior-based methods are obtained from segmented images and misclassification in segmentation often leads to residual artifacts and tissue structure loss in the final corrected images. To overcome these drawbacks, the authors propose a fusion scheme, named fusion prior-based MAR (FP-MAR).

METHODS

The FP-MAR method consists of (i) precorrect the image by means of an interpolation-based MAR method and an edge-preserving blur filter; (ii) generate a prior image from the fusion of this precorrected image and the originally reconstructed image with metal parts removed; (iii) forward project this prior image to guide the estimation of the surrogate data using well-developed replacement techniques.

RESULTS

Both simulations and clinical image tests are carried out to show that the proposed FP-MAR method can effectively reduce metal artifacts. A comparison with other MAR methods demonstrates that the FP-MAR method performs better in artifact suppression and tissue feature preservation.

CONCLUSIONS

From a wide range of clinical cases to which FP-MAR has been tested (single or multiple pieces of metal, various shapes, and sizes), it can be concluded that the proposed fusion based prior image preserves more tissue information than other segmentation-based prior approaches and can provide better estimates of the surrogate data in prior-based MAR methods.

A hybrid metal artifact reduction algorithm for x-ray CT

PURPOSE

Presence of metal artifacts is a major reason of degradation of computed tomography image quality and there is still no standard solution to this issue. A class of recently investigated metal artifact reduction (MAR) methods based on forward projection of a prior image that is artifact-free to replace the metal affected projection data have shown promising results. However, usually it is hard to get a good prior image which is close to the true image without artifacts. This work aims at creating a good prior image so that the forward projection can replace the metal affected projection data well.

METHODS

The proposed method consists of four steps based on the forward projection MAR framework. First, metal implants in the reconstructed image are segmented and the corresponding metal traces in the projection domain are identified. Then the prior image is obtained by two steps. A processed precorrected image is generated as an initial prior image first and then in the next step it is used as the initial image of the iterative reconstruction from the unaffected projection data to generate a better prior image. In order to deal with severe artifacts, the iteration incorporates the total variation minimization constraint as well as a novel constraint which forces the soft tissue region near metal to be as flat as possible. Finally, the projection is completed using forward projection of the prior image and the corrected image is reconstructed by FBP. A linear interpolation MAR method and two recently reported forward projection based methods are performed simultaneously for comparison.

RESULTS

The proposed method shows outstanding performance on both phantoms' and patients' datasets. This approach can reduce artifacts dramatically and restore tissue structures near metal to a large extent. Unlike competing MAR methods, it can effectively prevent introduction of new artifacts and false structures. Moreover, the proposed method has the lowest RMSE in regions of both soft tissue and bone tissue among the corrected images and is ranked as the best method for evaluation, by radiologists.

CONCLUSIONS

Both subjective and quantitative evaluations of the results demonstrate the superior performance of the proposed algorithm, compared to that of the competing methods. This method offers a remarkable improvement of the image quality.

X-ray CT metal artifact reduction using wavelet domain L0 sparse regularization

X-ray computed tomography (CT) imaging of patients with metallic implants usually suffers from streaking metal artifacts. In this paper, we propose a new projection completion metal artifact reduction (MAR) algorithm by formulating the completion of missing projections as a regularized inverse problem in the wavelet domain. The Douglas-Rachford splitting (DRS) algorithm was used to iteratively solve the problem. Two types of prior information were exploited in the algorithm: 1) the sparsity of the wavelet coefficients of CT sinograms in a dictionary of translation-invariant wavelets and 2) the detail wavelet coefficients of a prior sinogram obtained from the forward projection of a segmented CT image. A pseudo- L0 synthesis prior was utilized to exploit and promote the sparsity of wavelet coefficients. The proposed L0-DRS MAR algorithm was compared with standard linear interpolation and the normalized metal artifact reduction (NMAR) approach proposed by Meyer using both simulated and clinical studies including hip prostheses, dental fillings, spine fixation and electroencephalogram electrodes in brain imaging. The qualitative and quantitative evaluations showed that our algorithm substantially suppresses streaking artifacts and can outperform both linear interpolation and NMAR algorithms.

Clinical evaluation of a commercial orthopedic metal artifact reduction tool for CT simulations in radiation therapy

PURPOSE

Severe artifacts in kilovoltage-CT simulation images caused by large metallic implants can significantly degrade the conspicuity and apparent CT Hounsfield number of targets and anatomic structures, jeopardize the confidence of anatomical segmentation, and introduce inaccuracies into the radiation therapy treatment planning process. This study evaluated the performance of the first commercial orthopedic metal artifact reduction function (O-MAR) for radiation therapy, and investigated its clinical applications in treatment planning.

METHODS

Both phantom and clinical data were used for the evaluation. The CIRS electron density phantom with known physical (and electron) density plugs and removable titanium implants was scanned on a Philips Brilliance Big Bore 16-slice CT simulator. The CT Hounsfield numbers of density plugs on both uncorrected and O-MAR corrected images were compared. Treatment planning accuracy was evaluated by comparing simulated dose distributions computed using the true density images, uncorrected images, and O-MAR corrected images. Ten CT image sets of patients with large hip implants were processed with the O-MAR function and evaluated by two radiation oncologists using a five-point score for overall image quality, anatomical conspicuity, and CT Hounsfield number accuracy. By utilizing the same structure contours delineated from the O-MAR corrected images, clinical IMRT treatment plans for five patients were computed on the uncorrected and O-MAR corrected images, respectively, and compared.

RESULTS

Results of the phantom study indicated that CT Hounsfield number accuracy and noise were improved on the O-MAR corrected images, especially for images with bilateral metal implants. The γ pass rates of the simulated dose distributions computed on the uncorrected and O-MAR corrected images referenced to those of the true densities were higher than 99.9% (even when using 1% and 3 mm distance-to-agreement criterion), suggesting that dose distributions were clinically identical. In all patient cases, radiation oncologists rated O-MAR corrected images as higher quality. Formerly obscured critical structures were able to be visualized. The overall image quality and the conspicuity in critical organs were significantly improved compared with the uncorrected images: overall quality score (1.35 vs 3.25, P = 0.0022); bladder (2.15 vs 3.7, P = 0.0023); prostate and seminal vesicles∕vagina (1.3 vs 3.275, P = 0.0020); rectum (2.8 vs 3.9, P = 0.0021). The noise levels of the selected ROIs were reduced from 93.7 to 38.2 HU. On most cases (8∕10), the average CT Hounsfield numbers of the prostate∕vagina on the O-MAR corrected images were closer to the referenced value (41.2 HU, an average measured from patients without metal implants) than those on the uncorrected images. High γ pass rates of the five IMRT dose distribution pairs indicated that the dose distributions were not significantly affected by the CT image improvements.

CONCLUSIONS

Overall, this study indicated that the O-MAR function can remarkably reduce metal artifacts and improve both CT Hounsfield number accuracy and target and critical structure visualization. Although there was no significant impact of the O-MAR algorithm on the calculated dose distributions, we suggest that O-MAR corrected images are more suitable for the entire treatment planning process by offering better anatomical structure visualization, improving radiation oncologists' confidence in target delineation, and by avoiding subjective density overrides of artifact regions on uncorrected images.

Metal artifact reduction for CT: development, implementation, and clinical comparison of a generic and a scanner-specific technique

PURPOSE

To develop, implement, and compare two metal artifact reduction methods for CT.

METHODS

Two methods for metal artifact reduction were developed. The first is based on applying corrections in a Radon transformation of the CT images. The second method is based on a forward projection of the CT images and applying corrections in the scanner's original raw data. The first method is generic since it does not depend on the scanner specifications. For the second method, detailed information on the design of the CT scanner and the raw data of the study is required. Clinical implementation and evaluation were performed using pre- and post-operative CT scans of four patients with shoulder prosthesis. For comparison of these methods, the authors developed a quantitative technique that compares improvement in image quality for the two metal artifact reduction techniques with the image quality of the uncorrected images.

RESULTS

Metal artifact reduction using either of the two methods yields a decrease of noise and artifacts in CT scans of patients with shoulder prostheses. Artifacts that appeared as bright and dark streaks were reduced or eliminated and as a result image quality improved. Quantitative assessment of clinical images showed improved image quality for both techniques of metal artifact reduction, but the method based on correction in original raw data performed better in all comparisons.

CONCLUSION

Both methods are effective for metal artifact reduction, but better performance was observed for the method that is based on correcting the original raw data. The used evaluation technique provides an objective way of evaluating the metal artifacts in clinical CT images.

CT metal artifact reduction method correcting for beam hardening and missing projections

We present and validate a computed tomography (CT) metal artifact reduction method that is effective for a wide spectrum of clinical implant materials. Projections through low-Z implants such as titanium were corrected using a novel physics correction algorithm that reduces beam hardening errors. In the case of high-Z implants (dental fillings, gold, platinum), projections through the implant were considered missing and regularized iterative reconstruction was performed. Both algorithms were combined if multiple implant materials were present. For comparison, a conventional projection interpolation method was implemented. In a blinded and randomized evaluation, ten radiation oncologists ranked the quality of patient scans on which the different methods were applied. For scans that included low-Z implants, the proposed method was ranked as the best method in 90% of the reviews. It was ranked superior to the original reconstruction (p = 0.0008), conventional projection interpolation (p < 0.0001) and regularized limited data reconstruction (p = 0.0002). All reviewers ranked the method first for scans with high-Z implants, and better as compared to the original reconstruction (p < 0.0001) and projection interpolation (p = 0.004). We conclude that effective reduction of CT metal artifacts can be achieved by combining algorithms tailored to specific types of implant materials.