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The purpose of the study was to evaluate the accuracy of monochromatic energy (MonoE) computed tomography (CT) images reconstructed by spectral CT in predicting the stopping power ratio (SPRw) of materials in the presence of metal...
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The purpose of the study was to evaluate the accuracy of monochromatic energy (MonoE) computed tomography (CT) images reconstructed by spectral CT in predicting the stopping power ratio (SPRw) of materials in the presence of metal. The CIRS062 phantom was scanned three times using spectral CT. In the first scan, a solid water insert was placed at the center of the phantom (CTno metal)?. In the second scan, the solid water insert was replaced with a titanium alloy femoral head (CTmetal)?. The metal artifact reduction (MAR) algorithm was used in the last scan (CTmetal+MAR)?. The MonoE-CT images of 40 keV and 80 keV were reconstructed. Finally, the single-energy CT method (SECT) and the dual-energy CT method (DECT) were used to calculate the SPRw?. The mean absolute error (MAE) of the SPRw of the inner layer inserts calculated by the SECT method were 3.19%, 13.88% and 2.71%, corresponding to CTno metal?, CTmetal and CTmetal+MAR?, respectively. For the outer layer inserts, the MAE of SPRw were 3.43%, 5.42% and 2.99%, respectively. Using the DECT method, the MAE of the SPRw of the inner layer inserts was 1.30%, 3.69% and 1.46% and the MAE of the outer layer inserts– was 1.34%, 1.36% and 1.05%. The studies shows that, compared with the SECT method, the accuracy of the DECT method in predicting the SPRw of a material is more robust to the presence of metal. Using the MAR algorithm when performing CT scans can further improve the accuracy of predicting the SPR of materials in the presence of metal.
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Purpose: Dual Energy CT (DECT) provides so-called monoenergetic images based on a linear combination of the original polychromatic images. At certain patient-specific energy levels, corresponding to certain patient-and slice-depen...
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Purpose: Dual Energy CT (DECT) provides so-called monoenergetic images based on a linear combination of the original polychromatic images. At certain patient-specific energy levels, corresponding to certain patient-and slice-dependent linear combination weights, e. g., E = 160 keV corresponds to alpha = 1.57, a significant reduction of metal artifacts may be observed. The authors aimed at analyzing the method for its artifact reduction capabilities to identify its limitations. The results are compared with raw data-based processing.
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X-ray CT plays a great role both in medical fields and in industrial nondestructive tests. In imaging, metal objects absorb X-rays greatly, which introduces streaks on reconstructed images. In this paper, we propose a method for m...
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X-ray CT plays a great role both in medical fields and in industrial nondestructive tests. In imaging, metal objects absorb X-rays greatly, which introduces streaks on reconstructed images. In this paper, we propose a method for metal artifacts reduction. Firstly, the metal projection region is accurately identified and an interpolation method based on such identification is applied to get the projection data without metal. The image excluding metal is reconstructed from the modified projection data. Secondly, the image without metal is combined with the image of metal to form a complete reconstruction. Numerical simulations and a phantom experiment demonstrate that the metal artifacts can be effectively suppressed using our method and the reconstructed image is more accurate in depicting the details of cross-sections, especially in the immediate neighborhood of the metal object. The proposed method is computationally efficient and can be easily adapted to current commercial CT scanners.
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Purpose Metallic dental implants cause severe streaking artifacts in computed tomography (CT) data, which affect the accuracy of dose calculations in radiation therapy. The aim of this study was to investigate the benefit of the m...
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Purpose Metallic dental implants cause severe streaking artifacts in computed tomography (CT) data, which affect the accuracy of dose calculations in radiation therapy. The aim of this study was to investigate the benefit of the metal artifact reduction algorithm iterative metal artifact reduction (iMAR) in terms of correct representation of Hounsfield units (HU) and dose calculation accuracy.
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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 formulatin...
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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-$L_{0}$ synthesis prior was utilized to exploit and promote the sparsity of wavelet coefficients. The proposed $L_{0}$-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.
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Background: This study aimed to evaluate the effectiveness of spectral computed tomography (CT) mono-energy imaging combined with metal artifact reduction software (MARs) for metal implant artifact reduction using a phantom. Metho...
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Background: This study aimed to evaluate the effectiveness of spectral computed tomography (CT) mono-energy imaging combined with metal artifact reduction software (MARs) for metal implant artifact reduction using a phantom. Methods: A quantitative standard phantom with 9 cylinders was used to simulate the attenuation of the different tissues of the human body around the metal implant. Groups A and B were divided according to conventional CT scan mode and spectral CT scan mode. Three sets of reconstructed images, including 120 kVp-like + MARs images, mono-energy images (MonoE), and MonoE + MARs images, were generated after spectral CT scanning. High-attenuation artifacts and low-attenuation artifacts were observed around the coil in the images of groups A and B. The CT values (Hounsfield unit) and standard deviation (SD) values of the artifacts were measured, and the artifact index and hardening artifact removal rate were calculated. Results: Compared to conventional poly-energy CT images, for high-attenuation and low-attenuation artifacts, the artifact indices of 120 kVp-like + MARs, MonoE, and MonoE + MARs images were all reduced significantly. The hardening artifact removal rates of the high-attenuation and low-attenuation artifacts of 120 kVp-like + MARs images were 82% and 92%, respectively. The hardening artifact removal rate of the high-attenuation and low-attenuation artifacts of MonoE and MonoE + MARs images increased with the mono-energy level. Conclusions: Spectral CT using the 120 kVp-like + MARs, 110–140 keV MonoE, and MonoE + MARs reconstruction methods can reduce metal implant artifacts in varying degrees. MonoE + MARs reconstruction was the best method for reducing metal artifacts.
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The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam harden...
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The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as dark and bright streaks. A new reconstruction algorithm is proposed with the aim to reduce these artifacts by incorporating information about shape and known attenuation coefficients of a metal implant. Image reconstruction is considered as a variational optimization problem. The afore-mentioned prior knowledge is introduced in terms of equality constraints. An augmented Lagrangian approach is adapted in order to minimize the associated log-likelihood function for transmission CT. During iterations, temporally appearing artifacts are reduced with a bilateral filter and new projection values are calculated, which are used later on for the reconstruction. A detailed evaluation in cooperation with radiologists is performed on software and hardware phantoms, as well as on clinically relevant patient data of subjects with various metal implants. Results show that the proposed reconstruction algorithm is able to outperform contemporary metal artifact reduction methods such as normalized metal artifact reduction.
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To evaluate the distortion and artifact area of metal in MR images and to compare artifact reduction using different metal artifact-reducing sequences in patients with metal-on-metal (MoM) and non-MoM total hip prostheses.
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Abstract Objective To evaluate the performance and reliability of the single-energy metal artifact reduction (SEMAR) algorithm in patients with different orthopedic hardware at the hips.Materials and methods A total of 153 patient...
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Abstract Objective To evaluate the performance and reliability of the single-energy metal artifact reduction (SEMAR) algorithm in patients with different orthopedic hardware at the hips.Materials and methods A total of 153 patients with hip instrumentation who had undergone CT with adaptive iterative dose reduction (AIDR) 3D and SEMAR algorithms between February 2015 and October 2019 were included retrospectively. Patients were divided into 5 groups by the hardware type. Two readers with 21 and 13?years of experience blindly reviewed all image sets and graded the extent of artifacts and imaging quality using 5-point scales. To evaluate reliability, the mean densities and image noises were measured at the urinary bladder, veins, and fat in images with artifacts and the reference images.Results No significant differences were found in the mean densities of the urinary bladder, veins, and fat between the SEMAR images with artifacts (7.57?±?9.49, 40.29?±?23.07, 86.78?±?38.34) and the reference images (7.77?±?6.2, 40.27?±?8.66, 89.10?±?20.70) (P?=?.860, .994, .392). Image noises of the urinary bladder in the SEMAR images with artifacts (14.25?±?4.50) and the SEMAR reference images (9.69?±?1.29) were significantly higher than those in the AIDR 3D reference images (9.11?±?1.12) (P?收起
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