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Background Purpose Methods Results Conclusions For patients with metal implants, computed tomography (CT) imaging results suffer from metal artifacts, which seriously affect image evaluation and even lead to misdiagnosis. Because spectral CT technology has the advantage of quantitative imaging, basis material decomposition, and so on, the current metal artifact reduction methods are utilizing spectral information to reduce metal artifacts with good results. However, they usually require projection data from multiple spectra or energy‐windows, which is difficult to realize in conventional CT.To satisfy the status quo, the aim of this work is to propose a metal artifact reduction (MAR) method based on single spectral CT (MARSS). By incorporating prior information, the average density of some base materials, and a constrained image reconstruction model is established. It forces the solution spaces of the materials to be discrete and finite, making the model easier to solve.The MARSS method uses the idea of discrete tomography to establish a constrained reconstruction model. By incorporating priori knowledge, the constraint forces the solution spaces of some materials to be discrete, which can effectively downsize the solution space and reduce the ill‐posedness of this problem. Then, an iteration algorithm is developed to solve this model. This algorithm iterates alternately between reconstruction and discretization. It ensures that the solution spaces are discrete while the polychromatic projection of the reconstructed image converges to that of the scanned object.The MRASS method significantly reduces artifacts and restores structures near metal to a large extent. Unlike the comparison MAR methods, it effectively prevents the introduction of new artifacts and distortion of the structure.The MARSS method can achieve MAR based on single spectral CT. Subjective and quantitative evaluation of the results show that the method significantly improves image quality compared to competing methods. [ABSTRACT FROM AUTHOR]

Background: Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons. Purpose: The overall quality of magnetic resonance (MR)‐based AC in whole‐body PET/MRI was evaluated in direct comparison to computed tomography (CT)‐based AC serving as reference. The quantitative impact of isolated tissue classes in the MR‐AC was systematically investigated to identify potential optimization needs and strategies. Methods: Data of n = 60 whole‐body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC‐models based on the CT data for each patient, simulating variations of MR‐AC. The original continuous CT‐AC (CT‐org) is referred to as reference. A pseudo MR‐AC (CT‐mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR‐AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated. Results: The overall performance of MR‐AC is in good agreement compared to CT‐AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT‐AC (myocardium −15%, bone tissue −14%, and lungs ±20%). Using single‐valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT‐AC to < ±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over‐ or undercorrections in PET signal compared to CT‐AC (±5%). Conclusions: The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment‐based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT‐AC reference. [ABSTRACT FROM AUTHOR]

Background: Metallic implants, which are inserted into the patient's body during trauma interventions, are the main cause of heavy artifacts in 3D X‐ray acquisitions. These artifacts then hinder the evaluation of the correct implant's positioning, thus leading to a disturbed patient's healing process and increased revision rates. Purpose: This problem is tackled by so‐called metal artifact reduction (MAR) methods. This paper examines possible advances in the inpainting process of such MAR methods to decrease disruptive artifacts while simultaneously preserving important anatomical structures adjacent to the inserted implants. Methods: In this paper, a learning‐based inpainting method for cone‐beam computed tomography is proposed that couples a convolutional neural network (CNN) with an estimated metal path length as prior knowledge. Further, the proposed method is solely trained and evaluated on real measured data. Results: The proposed inpainting approach shows advantages over the inpainting method used by the currently clinically approved frequency split metal artifact reduction (fsMAR) method as well as the learning‐based state‐of‐the‐art (SOTA) method PConv‐Net. The major improvement of the proposed inpainting method lies in the ability to correctly preserve important anatomical structures in those regions adjacent to the metal implants. Especially these regions are highly important for a correct implant's positioning in an intraoperative setup. Using the proposed inpainting, the corresponding MAR volumes reach a mean structural similarity index measure (SSIM) score of 0.9974 and outperform the other methods by up to 6 dB on single slices regarding the peak signal‐to‐noise ratio (PSNR) score. Furthermore, it can be shown that the proposed method can generalize to clinical cases at hand. Conclusions: In this paper, a learning‐based inpainting network is proposed that leverages prior knowledge about the metal path length of the inserted implant. Evaluations on real measured data reveal an increased overall MAR performance, especially regarding the preservation of anatomical structures adjacent to the inserted implants. Further evaluations suggest the ability of the proposed approach to generalize to clinical cases. [ABSTRACT FROM AUTHOR]

Purpose: This paper introduces a new approach for the dedicated reduction of high‐frequency metal artifacts, which applies a nonlinear scaling (NLS) transfer function on the high‐frequency projection domain to reduce artifacts, while preserving edge information and anatomic detail by incorporating prior image information. Methods: An NLS function is applied to suppress high‐frequency streak artifacts, but to restrict the correction to metal projections only, scaling is performed in the sinogram domain. Anatomic information should be preserved and is excluded from scaling by incorporating a prior image from tissue classification. The corrected high‐frequency sinogram is reconstructed and combined with the low‐frequency component of a normalized metal artifact reduction (NMAR) image. Scans of different anthropomorphic phantoms were acquired (unilateral hip, bilateral hip, dental implants, and embolization coil). Multiple regions of interest (ROIs) were drawn around the metal implants and hounsfield unit (HU) deviations were analyzed. Clinical data sets including single image slices of dental fillings, a bilateral hip implant, spinal fixation screws, and an aneurysm coil were reconstructed and assessed. Results: The prior image‐controlled NLS can remove streak artifacts while preserving anatomic detail within the bone and soft tissue. The qualitative analysis of clinical cases showed a tremendous enhancement within dental fillings and neuro coils, and a significant enhancement within spinal screws or hip implants. The phantom scan measurements support this observation. In all phantom setups, the NLS‐corrected result showed lowest HU derivation and the best visualization of the data. Conclusions: The prior image‐controlled NLS provides a method to reduce high‐frequency streaks in metal‐corrupted computed tomography (CT) data. [ABSTRACT FROM AUTHOR]

Purpose: Sparse‐view sampling has attracted attention for reducing the scan time and radiation dose of dental cone‐beam computed tomography (CBCT). Recently, various deep learning–based image reconstruction techniques for sparse‐view CT have been employed to produce high‐quality image while effectively reducing streak artifacts caused by the lack of projection views. However, most of these methods do not fully consider the effects of metal implants. As sparse‐view sampling strengthens the artifacts caused by metal objects, simultaneously reducing both metal and streak artifacts in sparse‐view CT images has been challenging. To solve this problem, in this study, we propose a novel framework. Methods: The proposed method was based on the normalized metal artifact reduction (NMAR) method, and its performance was enhanced using two convolutional neural networks (CNNs). The first network reduced the initial artifacts while preserving the fine details to generate high‐quality priors for NMAR processing. Subsequently, the second network was employed to reduce the streak artifacts after NMAR processing of sparse‐view CT data. To validate the proposed method, we generated training and test data by computer simulations using both extended cardiac‐torso (XCAT) and clinical data sets. Results: Visual inspection and quantitative evaluations demonstrated that the proposed method effectively reduced both metal and streak artifacts while preserving the details of anatomical structures compared with the conventional metal artifact reduction methods. Conclusions: We propose a framework for reconstructing accurate CT images in metal‐inserted sparse‐view CT. The proposed method reduces streak artifacts from both metal objects and sparse‐view sampling while recovering the anatomical details, indicating the feasibility of fast‐scan dental CBCT imaging. [ABSTRACT FROM AUTHOR]

Purpose: X‐ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic‐artifact CT images. However, this causes the neural network to be biased toward learning specific synthetic‐artifact patterns and leads to a poor generalization for unlabeled real‐artifact CT images. In this study, a semi‐supervised learning method of latent features based on convolutional neural networks (SLF‐CNN) is developed to remove metal artifacts while ensuring a good generalization ability for real‐artifact CT images. Methods: The proposed semi‐supervised method extracts CT image features in alternate iterations of a synthetic‐artifact learning stage and a real‐artifact learning stage. In the synthetic‐artifact learning stage, SLF‐CNN is fed with paired synthetic‐artifact CT images and is constrained using mean‐squared‐error (MSE) loss and perceptual loss in a supervised learning fashion. In the real‐artifact learning stage, the network weight is updated by minimizing the error between the pseudo‐ground truths and the predicted latent features. The feature level pseudo‐ground truths are obtained by modeling latent features using the Gaussian process. The overall framework of SLF‐CNN adopts an encoder–decoder structure. The encoder is composed of artifact information collection groups to map the input artifact‐affected synthetic‐artifact CT images and real‐artifact CT images into latent features. The decoder is composed of stacked ResNeXt blocks and is responsible for decoding latent features with high‐level semantic information to reconstruct artifact‐free CT images. The performance of the proposed method is evaluated through contrast experiments and ablation experiments. Results: The contrast experimental results indicate that the artifact‐free CT images obtained by SLF‐CNN have good metrics values, which are close to or better than those of typical supervised MAR methods. The metal artifacts in artifact‐affected CT images are eliminated and the tissue structure details are preserved using SLF‐CNN. The ablation experiment shows that adding real‐artifact CT images greatly improves the generalization ability of the network. Conclusions: The proposed semi‐supervised learning method of latent features for MAR effectively suppresses metal artifacts and improves the generalization ability of the network. [ABSTRACT FROM AUTHOR]

Purpose: Metal implants in the patient's body can generate severe metal artifacts in x‐ray computed tomography (CT) images. These artifacts may cover the tissues around the metal implants in CT images and even corrupt the tissue regions, thus affecting disease diagnosis using these images. Previous deep learning metal trace inpainting methods used both valid pixels of uncorrupted areas and invalid pixels of corrupted areas to patch metal trace (i.e., the holes of removed metal‐corrupted regions). Such methods cannot recover fine details well and often suffer information mismatch due to interference of invalid pixels, thus incurring considerable secondary artifacts. In this paper, we develop a new irregular metal trace inpainting network for reducing metal artifacts. Methods: We develop a new deep learning network to patch irregular metal trace in metal‐corrupted sinograms to reduce metal artifacts for isometric fan‐beam CT. Our new method patches irregular metal trace in CT sinograms using only valid pixels, avoiding interference from invalid pixels. Furthermore, to enable the inpainting network to recover as many details as possible, we design an auxiliary inpainting network to suppress the probable secondary artifacts in CT images to assist fine detail restoration. The image produced by the auxiliary network is then projected onto a sinogram via a forward projection (FP) algorithm and is fused with the sinogram predicted by the inpainting network in order to predict the final recovered sinogram. Our entire network is trained end‐to‐end to extract cross‐domain information between the sinogram domain and CT image domain. Results: We compare our proposed method with two traditional and four deep learning‐based metal trace inpainting methods, and with an iterative reconstruction method on four datasets: dental fillings (panoramic and local perspectives), hip prostheses, and spine fixations. We use both quantitative and qualitative indices to evaluate our method, and the analyses suggest that our method reduces the most metal artifacts and produces the best quality CT images. Additionally, our proposed method takes 0.1512 s on average to process a CT slice, which meets the clinical requirement. Conclusions: This paper proposes a new deep learning network to patch irregular metal trace in corrupted sinograms to reduce metal artifacts. Our method restores more fine details in irregular metal trace and has a superior capability on metal artifact reduction compared with state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Purpose: Although several metal artifact reduction (MAR) algorithms for computed tomography (CT) scanning are commercially available, no quantitative, rigorous, and reproducible method exists for assessing their performance. The lack of assessment methods poses a challenge to regulators, consumers, and industry. We explored a phantom‐based framework for assessing an important aspect of MAR performance: how applying MAR in the presence of metal affects model observer performance at a low‐contrast detectability (LCD) task This work is, to our knowledge, the first model observer–based framework for the evaluation of MAR algorithms in the published literature. Methods: We designed a numerical head phantom with metal implants. In order to incorporate an element of randomness, the phantom included a rotatable inset with an inhomogeneous background. We generated simulated projection data for the phantom. We applied two variants of a simple MAR algorithm, sinogram inpainting, to the projection data, that we reconstructed using filtered backprojection. To assess how MAR affected observer performance, we examined the detectability of a signal at the center of a region of interest (ROI) by a channelized Hotelling observer (CHO). As a figure of merit, we used the area under the ROC curve (AUC). Results: We used simulation to test our framework on two variants of the MAR technique of sinogram inpainting. We found that our method was able to resolve the difference in two different MAR algorithms' effect on LCD task performance, as well as the difference in task performances when MAR was applied, vs not. Conclusion: We laid out a phantom‐based framework for objective assessment of how MAR impacts low‐contrast detectability, that we tested on two MAR algorithms. Our results demonstrate the importance of testing MAR performance over a range of object and imaging parameters, since applying MAR does not always improve the quality of an image for a given diagnostic task. Our framework is an initial step toward developing a more comprehensive objective assessment method for MAR, which would require developing additional phantoms and methods specific to various clinical applications of MAR, and increasing study efficiency. [ABSTRACT FROM AUTHOR]

Purpose: In this study, the dosimetric accuracy of analytical anisotropic algorithm (AAA) and Acuros XB (AXB) dose calculation algorithms (Varian Medical Systems, Palo Alto, CA) was investigated for small radiation fields incident on phantoms of various metals that include stainless steel grade 316L (SS316L) and titanium alloy grade 5 (Ti5) implants. In addition, the effects of using metal artifact reduction for orthopedic implants (O‐MAR, Philips Healthcare, Cleveland, OH) were evaluated. Methods: The evaluations of AAA and AXB were performed by comparing the crossline profiles calculated by AAA and AXB with GafChromicTM EBT3 film measurements at the phantom–implant interfaces and in close vicinity of implant materials for small field sizes (1 × 1 cm2, 2 × 2 cm2, 3 × 3 cm2, and 4 × 4 cm2) of a 6 MV flattening filter free photon beam. O‐MAR corrected and uncorrected (UC) computed tomography (CT) images were used for dose calculations. The values of average and standard deviations (SD) of Hounsfield unit (HU) for selected regions of each case were evaluated. The differences in average dose percentages in defined regions were calculated to quantify the relative dosimetric changes between doses calculated on UC and O‐MAR corrected CT images. Results: Compared to UC images, the values of SD were reduced, and the average HU became closer to its reference value in the O‐MAR images. There was some discrepancy in average dose percentage differences between calculations using UC and O‐MAR images at 1 cm above the SS316L implant (average dose percentage differences were AXB/UC = 5.9% and AXB/O‐MAR = −1.2%; AAA/UC = 2.2%, and AAA/O‐MAR = −0.8%). Neither AAA nor AXB algorithms predict increase in dose at upper phantom–implant interface (4.9%, 9.9%. 13.5%, and 13.8% for the fields from 1 × 1 cm2 to 4 × 4 cm2, respectively). At the side of the SS316L implant (where dark streak artifacts exist), dose difference averages were estimated as − 1.1% and 22.3% when AXB/O‐MAR and AXB/UC calculations are compared with EBT3 measurements, respectively. Dose predictions at 1 cm below the SS316L implant were underestimated by AXB/O‐MAR (average −0.5%) and AXB/UC (average 2.0%). Conclusions: The O‐MAR tool was shown to have a favorable dosimetric effect or no effect on the calculations in the upper proximity of the implant materials. The dose differences between EBT3 film measurements and calculations at upper phantom–implant interfaces were smaller when they were calculated using O‐MAR images. However, the dose differences increased when O‐MAR corrected images were used for AAA calculations at lower phantom–implant interfaces. Use of O‐MAR enabled closer agreement for the AXB algorithm, especially in the dark streak artifact regions. The O‐MAR algorithm should be used when the dose is calculated with the AXB algorithm in cases of patients with the metal implants. The estimations using AAA and AXB algorithms, in phantom setups, with Ti5 implant material were found to be closer to the EBT3 film measurements, when compared with the same estimations using SS316L implant material. [ABSTRACT FROM AUTHOR]

Purpose In this work, we develop a methodology for using Fricke gel dosimeters for dose distribution measurements surrounding high-density implants which circumvents artifact production by removing the obstruction during imaging. Methods Custom 3D printed molds were used to set cavities in Fricke gel phantoms to allow for the suspension of high-density implants in different geometries. This allowed for the metal valve extracted from a temporary tissue expander to be suspended during irradiation, and removed during optical- CT scanning. Results The removal of the metal implant and subsequent backfilling of the remaining cavity with optically matched fluid prior to dose evaluation enables accurate optical- CT scanning of the gel dosimeters. Results have shown very good agreement between measured and calculated doses within 2 mm from the surface of the implant. Slight deviations are present within 1 mm of the interface. Conclusions Artifacts in the form of radial streaking, cold spots, and hot spots were all reduced using this technique, enabling the broader and more accurate use of optical- CT for the imaging of gels containing opaque objects. [ABSTRACT FROM AUTHOR]

Purpose: Dose calculation errors near metal implants are caused by limitations of the dose calculation algorithm in modeling tissue/metal interface effects as well as density assignment errors caused by imaging artifacts. The purpose of this study was to investigate two strategies for reducing dose calculation errors near metal implants: implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) dose calculation method and use of metal artifact reduction methods for computed tomography (CT) imaging. Methods: Both error reduction strategies were investigated using a simple geometric slab phantom with a rectangular metal insert (composed of titanium or Cerrobend), as well as two anthropomorphic phantoms (one with spinal hardware and one with dental fillings), designed to mimic relevant clinical scenarios. To assess the dosimetric impact of metal kernels, the authors implemented titanium and silver kernels in a commercial collapsed cone C/S algorithm. To assess the impact of CT metal artifact reduction methods, the authors performed dose calculations using baseline imaging techniques (uncorrected 120 kVp imaging) and three commercial metal artifact reduction methods: Philips Healthcare's O-MAR, GE Healthcare's monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI with metal artifact reduction software (MARS) applied. For the simple geometric phantom, radiochromic film was used to measure dose upstream and downstream of metal inserts. For the anthropomorphic phantoms, ion chambers and radiochromic film were used to quantify the benefit of the error reduction strategies. Results: Metal kernels did not universally improve accuracy but rather resulted in better accuracy upstream of metal implants and decreased accuracy directly downstream. For the clinical cases (spinal hardware and dental fillings), metal kernels had very little impact on the dose calculation accuracy (<1.0%). Of the commercial CT artifact reduction methods investigated, the authors found that O-MAR was the most consistent method, resulting in either improved dose calculation accuracy (dental case) or little impact on calculation accuracy (spine case). GSI was unsuccessful at reducing the severe artifacts caused by dental fillings and had very little impact on calculation accuracy. GSI with MARS on the other hand gave mixed results, sometimes introducing metal distortion and increasing calculation errors (titanium rectangular implant and titanium spinal hardware) but other times very successfully reducing artifacts (Cerrobend rectangular implant and dental fillings). Conclusions: Though successful at improving dose calculation accuracy upstream of metal implants, metal kernels were not found to substantially improve accuracy for clinical cases. Of the commercial artifact reduction methods investigated, O-MAR was found to be the most consistent candidate for all-purpose CT simulation imaging. The MARS algorithm for GSI should be used with caution for titanium implants, larger implants, and implants located near heterogeneities as it can distort the size and shape of implants and increase calculation errors. [ABSTRACT FROM AUTHOR]

Purpose: Metal artifact reduction (MAR) is a major problem and a challenging issue in x-ray computed tomography (CT) examinations. Iterative reconstruction from sinograms unaffected by metals shows promising potential in detail recovery. This reconstruction has been the subject of much research in recent years. However, conventional iterative reconstruction methods easily introduce new artifacts around metal implants because of incomplete data reconstruction and inconsistencies in practical data acquisition. Hence, this work aims at developing a method to suppress newly introduced artifacts and improve the image quality around metal implants for the iterative MAR scheme. Methods: The proposed method consists of two steps based on the general iterative MAR framework. An uncorrected image is initially reconstructed, and the corresponding metal trace is obtained. The iterative reconstruction method is then used to reconstruct images from the unaffected sinogram. In the reconstruction step of this work, an iterative strategy utilizing unmatched projector/backprojector pairs is used. A ramp filter is introduced into the back-projection procedure to restrain the inconsistency components in low frequencies and generate more reliable images of the regions around metals. Furthermore, a constrained total variation (TV) minimization model is also incorporated to enhance efficiency. The proposed strategy is implemented based on an iterative FBP and an alternating direction minimization (ADM) scheme, respectively. The developed algorithms are referred to as "iFBP-TV" and "TV-FADM," respectively. Two projection-completion-based MAR methods and three iterative MAR methods are performed simultaneously for comparison. Results: The proposed method performs reasonably on both simulation and real CT-scanned datasets. This approach could reduce streak metal artifacts effectively and avoid the mentioned effects in the vicinity of the metals. The improvements are evaluated by inspecting regions of interest and by comparing the root-mean-square errors, normalized mean absolute distance, and universal quality index metrics of the images. Both iFBP-TV and TV-FADM methods outperform other counterparts in all cases. Unlike the conventional iterative methods, the proposed strategy utilizing unmatched projector/backprojector pairs shows excellent performance in detail preservation and prevention of the introduction of new artifacts. Conclusions: Qualitative and quantitative evaluations of experimental results indicate that the developed method outperforms classical MAR algorithms in suppressing streak artifacts and preserving the edge structural information of the object. In particular, structures lying close to metals can be gradually recovered because of the reduction of artifacts caused by inconsistency effects. [ABSTRACT FROM AUTHOR]

Purpose:Metal artifact reduction (MAR) produces images with improved quality potentially leading to confident and reliable clinical diagnosis and therapy planning. In this work, the authors evaluate the performance of five MAR techniques for the assessment of computed tomographyimages of patients with hip prostheses. Methods: Five MAR algorithms were evaluated using simulation and clinical studies. The algorithms included one-dimensional linear interpolation (LI) of the corrupted projection bins in the sinogram, two-dimensional interpolation (2D), a normalized metal artifact reduction (NMAR) technique, a metal deletion technique, and a maximum a posteriori completion (MAPC) approach. The algorithms were applied to ten simulated datasets as well as 30 clinical studies of patients with metallic hip implants. Qualitative evaluations were performed by two blinded experienced radiologists who ranked overall artifact severity and pelvic organ recognition for each algorithm by assigning scores from zero to five (zero indicating totally obscured organs with no structures identifiable and five indicating recognition with high confidence). Results: Simulation studies revealed that 2D, NMAR, and MAPC techniques performed almost equally well in all regions. LI falls behind the other approaches in terms of reducing dark streaking artifacts as well as preserving unaffected regions (p < 0.05). Visual assessment of clinical datasets revealed the superiority of NMAR and MAPC in the evaluated pelvic organs and in terms of overall image quality. Conclusions: Overall, all methods, except LI, performed equally well in artifact-free regions. Considering both clinical and simulation studies, 2D, NMAR, and MAPC seem to outperform the other techniques. [ABSTRACT FROM AUTHOR]

Purpose: In radiotherapy, metallic implants often detach from their deposited sites and migrate to other locations. This undesirable migration could cause inadequate dose coverage for permanent brachytherapy and difficulties in image-guided radiation delivery for patients. To prevent migration of implanted seeds, the authors propose a potential strategy to use a biocompatible and tissue-adhesive material called polydopamine. Methods: In this study, nonradioactive dummy seeds that have the same geometry and composition as commercial I-125 seeds were coated in polydopamine. Using scanning electron microscopy and x-ray photoelectron spectroscopy, the surface of the polydopamine-coated and noncoated seeds was characterized. The detachment stress between the two types of seeds and the tissue was measured. The efficacy of polydopamine-coated seed was investigated through in vitro migration tests by tracing the seed location after tissue implantation and shaking for given times. The cytotoxicity of the polydopamine coating was also evaluated. Results: The results of the coating characterization have shown that polydopamine was successfully coated on the surface of the seeds. In the adhesion test, the polydopamine-coated seeds had 2.1-fold greater detachment stress than noncoated seeds. From the in vitro test, it was determined that the polydopamine-coated seed migrated shorter distances than the noncoated seed. This difference was increased with a greater length of time after implantation. Conclusions: The authors suggest that polydopamine coating is an effective technique to prevent migration of implanted seeds, especially for permanent prostate brachytherapy. [ABSTRACT FROM AUTHOR]

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 = 160keV corresponds to α = 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. Methods: Clinical DECT uses a simplified version of monochromatic imaging by linearly combining the low and the high kV images and by assigning an energy to that linear combination. Those pseudo-monochromatic images can be used by radiologists to obtain images with reduced metal artifacts. The authors analyzed the underlying physics and carried out a series expansion of the polychromatic attenuation equations. The resulting nonlinear terms are responsible for the artifacts, but they are not linearly related between the low and the high kV scan: A linear combination of both images cannot eliminate the nonlinearities, it can only reduce their impact. Scattered radiation yields additional noncanceling nonlinearities. This method is compared to raw data-based artifact correction methods. To quantify the artifact reduction potential of pseudo-monochromatic images, they simulated the FORBILD abdomen phantom with metal implants, and they assessed patient data sets of a clinical dual source CT system (100, 140kVSn) containing artifacts induced by a highly concentrated contrast agent bolus and by metal. In each case, they manually selected an optimal α and compared it to a raw data-based material decomposition in case of simulation, to raw data-based material decomposition of inconsistent rays in case of the patient data set containing contrast agent, and to the frequency split normalized metal artifact reduction in case of the metal implant. For each case, the contrast-to-noise ratio (CNR) was assessed. Results: In the simulation, the pseudo-monochromatic images yielded acceptable artifact reduction results. However, the CNR in the artifact-reduced images was more than 60% lower than in the original polychromatic images. In contrast, the raw data-based material decomposition did not significantly reduce the CNR in the virtual monochromatic images. Regarding the patient data with beam hardening artifacts and with metal artifacts from small implants the pseudo-monochromatic method was able to reduce the artifacts, again with the downside of a significant CNR reduction. More intense metal artifacts, e.g., as those caused by an artificial hip joint, could not be suppressed. Conclusions: Pseudo-monochromatic imaging is able to reduce beam hardening, scatter, and metal artifacts in some cases but it cannot remove them. In all cases, the CNR is significantly reduced, thereby rendering the method questionable, unless special post processing algorithms are implemented to restore the high CNR from the original images (e.g., by using a frequency split technique). Raw data-based dual energy decomposition methods should be preferred, in particular, because the CNR penalty is almost negligible. [ABSTRACT FROM AUTHOR]

Purpose: The overall goal of this work is to improve the computed tomography (CT) image quality for patients with metal implants or fillings by completing the missing kilovoltage (kV) projection data with selectively acquired megavoltage (MV) data that do not suffer from photon starvation. When both of these imaging systems, which are available on current radiotherapy devices, are used, metal streak artifacts are avoided, and the soft-tissue contrast is restored, even for regions in which the kV data cannot contribute any information. Methods: Three image-reconstruction methods, including two filtered back-projection (FBP)-based analytic methods and one iterative method, for combining kV and MV projection data from the two on-board imaging systems of a radiotherapy device are presented in this work. The analytic reconstruction methods modify the MV data based on the information in the projection or image domains and then patch the data onto the kV projections for a FBP reconstruction. In the iterative reconstruction, the authors used dual-energy (DE) penalized weighted least-squares (PWLS) methods to simultaneously combine the kV/MV data and perform the reconstruction. Results: The authors compared kV/MV reconstructions to kV-only reconstructions using a dental phantom with fillings and a hip-implant numerical phantom. Simulation results indicated that dual-energy sinogram patch FBP and the modified dual-energy PWLS method can successfully suppress metal streak artifacts and restore information lost due to photon starvation in the kV projections. The root-mean-square errors of soft-tissue patterns obtained using combined kV/MV data are 10-15 Hounsfield units smaller than those of the kV-only images, and the structural similarity index measure also indicates a 5%-10% improvement in the image quality. The added dose from the MV scan is much less than the dose from the kV scan if a high efficiency MV detector is assumed. Conclusions: The authors have shown that it is possible to improve the image quality of kV CTs for patients with metal implants or fillings by completing the missing kV projection data with selectively acquired MV data that do not suffer from photon starvation. Numerical simulations demonstrated that dual-energy sinogram patch FBP and a modified kV/MV PWLS method can successfully suppress metal streak artifacts and restore information lost due to photon starvation in kV projections. Combined kV/MV images may permit the improved delineation of structures of interest in CT images for patients with metal implants or fillings [ABSTRACT FROM AUTHOR]

Purpose: The problem of metal artifact reduction (MAR) is almost as old as the clinical use of computed tomography itself. When metal implants are present in the field of measurement, severe artifacts degrade the image quality and the diagnostic value of CT images. Up to now, no generally accepted solution to this issue has been found. In this work, a method based on a new MAR concept is presented: frequency split metal artifact reduction (FSMAR). It ensures efficient reduction of metal artifacts at high image quality with enhanced preservation of details close to metal implants. Methods: FSMAR combines a raw data inpainting-based MAR method with an image-based frequency split approach. Many typical methods for metal artifact reduction are inpainting-based MAR methods and simply replace unreliable parts of the projection data, for example, by linear interpolation. Frequency split approaches were used in CT, for example, by combining two reconstruction methods in order to reduce cone-beam artifacts. FSMAR combines the high frequencies of an uncorrected image, where all available data were used for the reconstruction with the more reliable low frequencies of an image which was corrected with an inpainting-based MAR method. The algorithm is tested in combination with normalized metal artifact reduction (NMAR) and with a standard inpainting-based MAR approach. NMAR is a more sophisticated inpainting-based MAR method, which introduces less new artifacts which may result from interpolation errors. A quantitative evaluation was performed using the examples of a simulation of the XCAT phantom and a scan of a spine phantom. Further evaluation includes patients with different types of metal implants: hip prostheses, dental fillings, neurocoil, and spine fixation, which were scanned with a modern clinical dual source CT scanner. Results: FSMAR ensures sharp edges and a preservation of anatomical details which is in many cases better than after applying an inpainting-based MAR method only. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to implants. Conclusions: FSMAR should be used together with NMAR, a combination which ensures an accurate correction of both high and low frequencies. The algorithm is computationally inexpensive compared to iterative methods and methods with complex inpainting schemes. No parameters were chosen manually; it is ready for an application in clinical routine. [ABSTRACT FROM AUTHOR]

Purpose: The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization. Methods: After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of 'metal shadows' in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective. Results: The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images. Conclusions: Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general 'missing data' image reconstruction problems. [ABSTRACT FROM AUTHOR]

Purpose: To develop a binary image reconstruction method for the autolocalization of metallic object(s) in CT with sparse projections. Methods: The authors divide the system into two types of contents: Metal(s) and nonmetal(s). The boundaries of metallic objects are obtained by using a penalized weighted least-squares algorithm with the adequate intensity gradient-controlled. A novel mechanism of 'amplifying' the difference between metal(s) and nonmetallic substances is introduced by preprocessing the sinogram data, which is shown to be necessary in dealing with a case with sparse projection data. A series of experimental studies are performed to evaluate the proposed approach. Results: A novel binary CT image reconstruction formalism is established for the autodetermination of the shape and location of metallic objects in the presence of limited number of projections. Experimental studies reveal that the presented algorithm works well even when the embedded metal object(s) has different shape(s). It is also shown that when the projection data are sparse, a differential manipulation of projection data can greatly facilitate the binary reconstruction process and allow the authors to obtain accurate binary CT images that would otherwise be unattainable. Conclusions: Binary CT reconstruction provides a viable method for determining the geometric distribution information of the implanted metal objects in CT imaging. [ABSTRACT FROM AUTHOR]

Purpose: Large metallic implants, such as hip prosthesis and shoulder implants, can cause severe artifacts in CT exams. As such, there have been significant efforts on the design of various image- or projection-based correction methods or iterative reconstruction methods with the hope to reconstruct artifact-free images. Unfortunately, suppression of metal artifacts remains a very challenging problem, in which metal region segmentation is one of the most important steps in assuring the efficiency of artifact suppression. In this article, the authors propose a novel, semiautomatic metal region segmentation algorithm based on a dual-front active contour model and a boundary mapping strategy to detect multiple large metal implants on reformatted projection data and to effectively suppress or eliminate metal artifacts on reconstructed images. Methods: First, the projections created from helical scan data were reformatted by combining data at the same view angle over the full longitudinal scan range. In this way, the shape, location, and number of the metal structures show up clearly on each reformatted projection, changing only slightly between adjacent projections. Second, an initial boundary on one of the reformatted projections is defined, and a boundary mapping strategy was utilized to map the metal boundary on the first reformatted projection to the next adjacent projection. Third, a novel dual-front active contour model was used to evolve the mapped boundary from the prior projection to the actual boundary in the current projection. By iteratively performing the boundary mapping and boundary evolution procedure, the metal structures (one or multiple) on all the projections can be extracted efficiently and accurately. Finally, a Delaunay triangulation was applied to fill the metal shadows and the corrected projection data were reconstructed with a commercially available algorithm. Results: Experimental studies on clinical hip and shoulder CT exams and a comparison with a gradient-based threshold method were performed. The results demonstrated that the proposed segmentation strategy was able to segment multiple metal implants more accurately than the threshold method. Soft-tissue visibility was improved dramatically. Conclusions: In total, the artifacts caused by dense metal implants were suppressed dramatically with the proposed metal artifact suppression technique. [ABSTRACT FROM AUTHOR]

Purpose: Several simplifications used in clinical implementations of the convolution/superposition (C/S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C/S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C/S algorithm. Methods: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C/S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C/S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels. Results: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found that depth was the most dominant factor affecting the pattern of energy deposition; however, the effects of field size and off-axis distance were not negligible. For the material-specific kernels, we found that as the density of the material increased, more energy was deposited laterally by charged particles, as opposed to in the forward direction. Thus, density scaling of water kernels becomes a worse approximation as the density and the effective atomic number of the material differ more from water. Implementation of spatially variant, polyenergetic kernels increased the percent depth dose value at 25 cm depth by 2.1%-5.8% depending on the field size, while implementation of titanium kernels gave 4.9% higher dose upstream of the metal cavity (i.e., higher backscatter dose) and 8.2% lower dose downstream of the cavity. Conclusions: Of the various kernel refinements investigated, inclusion of depth-dependent and metal-specific kernels into the C/S method has the greatest potential to improve dose calculation accuracy. Implementation of spatially variant polyenergetic kernels resulted in a harder depth dose curve and thus has the potential to affect beam modeling parameters obtained in the commissioning process. For metal implants, the C/S algorithms generally underestimate the dose upstream and overestimate the dose downstream of the implant. Implementation of a metal-specific kernel mitigated both of these errors. [ABSTRACT FROM AUTHOR]

Purpose: The increasing number of clinically oriented MRI studies at 7 T motivates the safety assessment of implants, since many 7 T research sites conservatively exclude all subjects with metallic implants, regardless of type or location. The purpose of this study was to investigate potential RF-induced heating during a 7 T MRI scan using a self-built transmit/receive RF coil in patients with implants used for refixation of the bone flap after craniotomy. Going beyond standard ASTM safety tests, a comprehensive test procedure for safety assessments at 7 T is presented which takes into account the more complex coupling of the electromagnetic field with the human body and the implant as well as polarization effects. Methods: The safety assessment consisted of three main investigations using (1) numerical simulations in simplified models, (2) electric and magnetic field measurements and validation procedures in homogeneous phantoms, and (3) analysis of exposure scenarios in a heterogeneous human body model including thermal simulations. Finally, 7 T in vivo images show the degree of image artifact around the implants. Results: The simulations showed that the field distortions remain localized within the direct vicinity of the implants. A parallel E-field polarization was found to be the most relevant component in creating local SAR deviations, resulting in a 10% increase in 10-g-averaged SAR and 53% in 1-g-averaged SAR. Using a heterogeneous human head model, the implants caused field distortions and SAR elevations in the numerical simulations which were distinctly lower than the maximum local SAR value caused by the RF coil alone. Also, the position of the maximum 10-g-averaged SAR remained unchanged by the presence of the implants. Similarly, the maximum absolute local temperature remained below 39 °C in the thermal simulations. Only minor artifacts from the implants were observed in the in vivo images that would not likely affect the diagnostic image quality in patients. Conclusions: The findings suggested no evidence for noteworthy RF-related heating in humans after craniotomy using the described implants and for the particular RF coil that was used in this study. Here, identical transmit power restrictions apply with or without the implants. For other RF coils, the maximum permissible input power should be reduced by 10% until further simulations may indicate otherwise. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]