Clinical Evaluation of Normalized Metal Artifact Reduction in kVCT Using MVCT Prior Images (MVCT-NMAR) for Radiation Therapy Treatment Planning

A qualitative study of improving megavoltage computed tomography image quality and maintaining dose accuracy using cycleGAN-based image synthesis

BACKGROUND

Due to inconsistent positioning, tumor shrinking, and weight loss during fractionated treatment, the initial plan was no longer appropriate after a few fractional treatments, and the patient will require adaptive helical tomotherapy (HT) to overcome the issue. Patients are scanned with megavoltage computed tomography (MVCT) before each fractional treatment, which is utilized for patient setup and provides information for dose reconstruction. However, the low contrast and high noise of MVCT make it challenging to delineate treatment targets and organs at risk (OAR).

PURPOSE

This study developed a deep-learning-based approach to generate high-quality synthetic kilovoltage computed tomography (skVCT) from MVCT and meet clinical dose requirements.

METHODS

Data from 41 head and neck cancer patients were collected; 25 (2995 slices) were used for training, and 16 (1898 slices) for testing. A cycle generative adversarial network (cycleGAN) based on attention gate and residual blocks was used to generate MVCT-based skVCT. For the 16 patients, kVCT-based plans were transferred to skVCT images and electron density profile-corrected MVCT images to recalculate the dose. The quantitative indices and clinically relevant dosimetric metrics, including the mean absolute error (MAE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), gamma passing rates, and dose-volume-histogram (DVH) parameters (Dmax , Dmean , Dmin ), were used to assess the skVCT images.

RESULTS

The MAE, PSNR, and SSIM of MVCT were 109.6 ± 12.3 HU, 27.5 ± 1.1 dB, and 91.9% ± 1.7%, respectively, while those of skVCT were 60.6 ± 9.0 HU, 34.0 ± 1.9 dB, and 96.5% ± 1.1%. The image quality and contrast were enhanced, and the noise was reduced. The gamma passing rates improved from 98.31% ± 1.11% to 99.71% ± 0.20% (2 mm/2%) and 99.77% ± 0.18% to 99.98% ± 0.02% (3 mm/3%). No significant differences (p > 0.05) were observed in DVH parameters between kVCT and skVCT.

CONCLUSION

With training on a small data set (2995 slices), the model successfully generated skVCT with improved image quality, and the dose calculation accuracy was similar to that of MVCT. MVCT-based skVCT can increase treatment accuracy and offer the possibility of implementing adaptive radiotherapy.

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

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

A novel approach for eliminating metal artifacts based on MVCBCT and CycleGAN

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.

Performance evaluation of image reconstruction algorithms for a megavoltage computed tomography system on a helical tomotherapy unit

Objective. To evaluate the impact of image reconstruction algorithm selection, as well as imaging mode and the reconstruction interval, on image quality metrics for megavoltage computed tomography (MVCT) image acquisition for use in image-guided (IGRT) and adaptive radiotherapy (ART) on a next-generation helical tomotherapy system.Approach. A CT image quality phantom was scanned across all available acquisition modes for filtered back projection (FBP) and both iterative reconstruction (IR) algorithms available on the system. Image quality metrics including noise, uniformity, contrast, spatial resolution, and mean CT number were compared. Analysis of DICOM data was performed using ImageJ software and Python code. ANOVA single factor and Tukey's honestly significant difference post-hoc tests were utilized for statistical analysis.Main Results. Application of both IR algorithms noticeably improved noise and image contrast when compared to the FBP algorithm available on all previous-generation helical tomotherapy systems. Use of the FBP algorithm improved image uniformity and spatial resolution in the axial plane, though values for the IR algorithms were well within tolerances recommended for IGRT and/or MVCT-based ART implementation by the American Association of Physicists in Medicine (AAPM). Additionally, longitudinal resolution showed little dependence on the reconstruction algorithm, while a negligible variation in mean CT number was observed regardless of the reconstruction algorithm or acquisition parameters. Statistical analysis confirmed the significance of these results.Significance. An overall improvement in image quality for metrics most important to IGRT and ART-mainly image noise and contrast-was evident in the application of IR when compared to FBP. Furthermore, since other imaging parameters remain identical regardless of the reconstruction algorithm, this improved image quality does not come at the expense of additional patient dose or an increased scan acquisition time for otherwise identical parameters. These improvements are expected to enhance fidelity in IGRT and ART implementation.

Quantitative assessment of three vendor's metal artifact reduction techniques for CT imaging using a customized phantom

A metal implant was placed in an acrylic phantom to enable quantitative analysis of the metal artifact reduction techniques used in computed tomography (CT) scanners from three manufacturers. Two titanium rods were placed in a groove in a cylindrical phantom made by acrylic, after which the groove was filled with water. The phantom was scanned using three CT scanners (Toshiba, GE, Siemens) under the abdomen CT setting. CT number accuracy, contrast-to-noise ratio, area of the metal rods in the images, and fraction of affected pixel area of water were measured using ImageJ. Different iterative reconstruction, dual energy, and metal artifact reduction techniques were compared within three vendors. The highest contrast-to-noise ratio of three scanners were 85.7 ± 8.4 (Toshiba), 85.9 ± 11.7 (GE), and 55.0 ± 14.8 (Siemens); and the most correct results of metal area were 157.1 ± 1.4 mm² (Toshiba), 155.0 ± 1.0 (GE), and 170.6 ± 5.3 (Siemens). The fraction of affected pixel area obtained using single-energy metal artifact reduction of Toshiba scanner was 2.2% ± 0.7%, which is more favorable than 4.1% ± 0.7% obtained using metal artifact reduction software of GE scanner (p = 0.002). Among all quantitative results, the estimations with fraction of affected pixel areas matched the effect of metal artifact reduction in the actual images. Therefore, the single-energy metal artifact reduction technique of Toshiba scanner had a desirable effect. The metal artifact reduction software of GE scanner effectively reduced the effect of metal artifacts; however, it underestimated the size of the metal rods. The monoenergetic and dual energy composition techniques of Siemens scanner could not effectively reduce metal artifacts.

Dual-energy-based metal segmentation for metal artifact reduction in dental computed tomography

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 kV_p ), 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.

Deformable Known Component Model-Based Reconstruction for Coronary CT Angiography

Purpose

Atherosclerosis detection remains challenging in coronary CT angiography for patients with cardiac implants. Pacing electrodes of a pacemaker or lead components of a defibrillator can create substantial blooming and streak artifacts in the heart region, severely hindering the visualization of a plaque of interest. We present a novel reconstruction method that incorporates a deformable model for metal leads to eliminate metal artifacts and improve anatomy visualization even near the boundary of the component.

Methods

The proposed reconstruction method, referred as STF-dKCR, includes a novel parameterization of the component that integrates deformation, a 3D-2D preregistration process that estimates component shape and position, and a polyenergetic forward model for x-ray propagation through the component where the spectral properties are jointly estimated. The methodology was tested on physical data of a cardiac phantom acquired on a CBCT testbench. The phantom included a simulated vessel, a metal wire emulating a pacing lead, and a small Teflon sphere attached to the vessel wall, mimicking a calcified plaque. The proposed method was also compared to the traditional FBP reconstruction and an interpolation-based metal correction method (FBP-MAR).

Results

Metal artifacts presented in standard FBP reconstruction were significantly reduced in both FBP-MAR and STF-dKCR, yet only the STF-dKCR approach significantly improved the visibility of the small Teflon target (within 2 mm of the metal wire). The attenuation of the Teflon bead improved to 0.0481 mm^-1 with STF-dKCR from 0.0166 mm^-1 with FBP and from 0.0301 mm^-1 with FBP-MAR - much closer to the expected 0.0414 mm^-1.

Conclusion

The proposed method has the potential to improve plaque visualization in coronary CT angiography in the presence of wire-shaped metal components.

Metal artifact reduction (MAR) based on two-compartment physical modeling: evaluation in patients with hip implants

BACKGROUND

Artifacts from metallic implants can hinder image interpretation in computed tomography (CT). Image quality can be improved using metal artifact reduction (MAR) techniques.

PURPOSE

To evaluate the impact of a MAR algorithm on image quality of CT examinations in comparison to filtered back projection (FBP) in patients with hip prostheses.

MATERIAL AND METHODS

Twenty-two patients with 25 hip prostheses who underwent clinical abdominopelvic CT on a 64-row CT were included in this retrospective study. Axial images were reconstructed with FBP and five increasing MAR levels (M30-34). Objective artifact strength (OAS) (SI_art-SI_norm) was assessed by region of interest (ROI) measurements in position of the strongest artifact (SI_art) and in an osseous structure without artifact (SI_norm) (in Hounsfield units [HU]). Two independent readers evaluated subjective image quality regarding metallic hardware, delineation of bone, adjacent muscle, and pelvic organs on a 5-point scale (1, non-diagnostic; 5, excellent image quality). Artifacts in the near field, far field, and newly induced artifacts due to the MAR technique were analyzed.

RESULTS

OAS values were: M34: 243.8 ± 155.4 HU; M33: 294.3 ± 197.8 HU; M32: 340.5 ± 210.1 HU; M31: 393.6 ± 225.2 HU; M30: 446.8 ± 224.2 HU and FBP: 528.9 ± 227.7 HU. OAS values were significantly lower for M32-34 compared to FBP (P < 0.01). For overall subjective image quality, results were: FBP, 2.0 ± 0.2; M30, 2.3 ± 0.8; M31, 2.6 ± 0.5; M32, 3.0 ± 0.6; M33, 3.5 ± 0.6; and M34, 3.8 ± 0.4 (P < 0.001 for M30-M34 vs. FBP, respectively). Increasing MAR levels resulted in new artifacts in 17% of reconstructions.

CONCLUSION

The investigated MAR algorithm led to a significant reduction of artifacts from metallic hip implants. The highest MAR level provided the least severe artifacts and the best overall image quality.

Metal artifact reduction through MVCBCT and kVCT in radiotherapy

This study proposes a new method for removal of metal artifacts from megavoltage cone beam computed tomography (MVCBCT) and kilovoltage CT (kVCT) images. Both images were combined to obtain prior image, which was forward projected to obtain surrogate data and replace metal trace in the uncorrected kVCT image. The corrected image was then reconstructed through filtered back projection. A similar radiotherapy plan was designed using the theoretical CT image, the uncorrected kVCT image, and the corrected image. The corrected images removed most metal artifacts, and the CT values were accurate. The corrected image also distinguished the hollow circular hole at the center of the metal. The uncorrected kVCT image did not display the internal structure of the metal, and the hole was misclassified as metal portion. Dose distribution calculated based on the corrected image was similar to that based on the theoretical CT image. The calculated dose distribution also evidently differed between the uncorrected kVCT image and the theoretical CT image. The use of the combined kVCT and MVCBCT to obtain the prior image can distinctly improve the quality of CT images containing large metal implants.

High-kVp Assisted Metal Artifact Reduction for X-ray Computed Tomography

In X-ray computed tomography (CT), the presence of metallic parts in patients causes serious artifacts and degrades image quality. Many algorithms were published for metal artifact reduction (MAR) over the past decades with various degrees of success but without a perfect solution. Some MAR algorithms are based on the assumption that metal artifacts are due only to strong beam hardening and may fail in the case of serious photon starvation. Iterative methods handle photon starvation by discarding or underweighting corrupted data, but the results are not always stable and they come with high computational cost. In this paper, we propose a high-kVp-assisted CT scan mode combining a standard CT scan with a few projection views at a high-kVp value to obtain critical projection information near the metal parts. This method only requires minor hardware modifications on a modern CT scanner. Two MAR algorithms are proposed: dual-energy normalized MAR (DNMAR) and high-energy embedded MAR (HEMAR), aiming at situations without and with photon starvation respectively. Simulation results obtained with the CT simulator CatSim demonstrate that the proposed DNMAR and HEMAR methods can eliminate metal artifacts effectively.

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.

Single-energy metal artefact reduction with CT for carbon-ion radiation therapy treatment planning

OBJECTIVE

One approach to improving image quality of CT is to use metal artefact reduction image processing, such as single-energy metal artefact reduction (SEMAR). To quantify the impact of image correction on the quality of carbon-ion dose distribution, treatment planning using SEMAR was evaluated.

METHODS

Using a head phantom into which metal screws could be inserted, we acquired standard planning CT images. We calculated dose distributions using phantom images with and without metal added, and with and without SEMAR. Hounsfield unit (HU) and dose distribution variation of these images with and without SEMAR were measured using metal-free image subtraction. We similarly analysed the image data sets of two patients with head and neck cancer who had dental implants.

RESULTS

HU difference between metal-containing images and metal-free images without and with SEMAR were -79.5 ± 97.2 HU and -1.4 ± 19.5 HU on severe artefact area, respectively. The range of dose distribution difference from the prescribed dose between uncorrected and SEMAR-corrected images varied from -19.5% to -3.4% within planning target volume (PTV). PTV-D95 (%) for uncorrected and SEMAR-corrected image data were 82.4% and 95.4%, respectively. For data in patients with metal dental work, PTV-D95 (%) for uncorrected and SEMAR-corrected data were 92.2% and 92.5% (Patient 1), and 90.9% and 95.7% (Patient 2), respectively.

CONCLUSION

SEMAR algorithm shows promise in improving CT image quality and in ensuring an accurate representation of dose distribution.

ADVANCES IN KNOWLEDGE

SEMAR may improve treatment accuracy without the need for dental implant extraction in patients with head and neck cancer.

DOSIMETRIC CONSEQUENCES OF USING CONTRAST-ENHANCED COMPUTED TOMOGRAPHIC IMAGES FOR INTENSITY-MODULATED STEREOTACTIC BODY RADIOTHERAPY PLANNING

Potential benefits of planning radiation therapy on a contrast-enhanced computed tomography scan (ceCT) should be weighed against the possibility that this practice may be associated with an inadvertent risk of overdosing nearby normal tissues. This study investigated the influence of ceCT on intensity-modulated stereotactic body radiotherapy (IM-SBRT) planning. Dogs with head and neck, pelvic, or appendicular tumors were included in this retrospective cross-sectional study. All IM-SBRT plans were constructed on a pre- or ceCT. Contours for tumor and organs at risk (OAR) were manually constructed and copied onto both CT's; IM-SBRT plans were calculated on each CT in a manner that resulted in equal radiation fluence. The maximum and mean doses for OAR, and minimum, maximum, and mean doses for targets were compared. Data were collected from 40 dogs per anatomic site (head and neck, pelvis, and limbs). The average dose difference between minimum, maximum, and mean doses as calculated on pre- and ceCT plans for the gross tumor volume was less than 1% for all anatomic sites. Similarly, the differences between mean and maximum doses for OAR were less than 1%. The difference in dose distribution between plans made on CTs with and without contrast enhancement was tolerable at all treatment sites. Therefore, although caution would be recommended when planning IM-SBRT for tumors near "reservoirs" for contrast media (such as the heart and urinary bladder), findings supported the use of ceCT with this dose calculation algorithm for both target delineation and IM-SBRT treatment planning.