A robust index for metal artifact quantification in computed tomography.

BACKGROUND: Objective assessment of metal artifact strength and the effectiveness of metal artifact reduction algorithms in computed tomography requires a quantitative metric. Metrics described in the literature are typically employed to compare the artifact strength in images reconstructed from the same raw data, but their robustness against varying scan conditions and repeated scans over time as it occurs in periodic quality assurance has not been investigated. PURPOSE: A new robust metric for quantifying metal artifacts in computed-tomography images is proposed and compared to other commonly used metrics. METHODS: The proposed artifact metric is based on the location parameter of the Gumbel distribution, described previously in the literature, but normalized to the location parameter in a background region-of-interest to obtain a noise-independent artifact metric. The metric was compared to three other quantitative metal artifact metrics (artifact-index, contrast-to-noise ratio, Gumbel-evaluation method) by evaluating metals artifacts in phantom scans and in clinical images. Robustness of the artifact metrics was evaluated using repeated scans with varying noise and against small variations in the selected regions-of-interest. RESULTS: The proposed artifact metric was independent of the underlying image noise and could be reproduced more consistently under slight changes of the region-of-interest within the artifact than the other investigated methods. The coefficient-of-variation was 5.7% on average with varying regions-of-interest in phantom scans and 2.5% in patient scans compared to 9.2% in phantoms scans and 9.9% in patient scans for the next-best performing noise-independent metric. Setup reproducibility was better than 5% and was comparable to the other metrics. The new metric correlated linearly with the artifact strength. The contrast-to-noise ratio, although often used in artifact quantification, was found to be an inadequate metric due to its lack of robustness against minute changes in the position, size, and pixel values of the region-of-interest chosen for calculating the metric and because it showed no correlation with the artifact strength. CONCLUSIONS: A new metal artifact metric has been proposed that is robust under changing scan conditions and less sensitive to user-dependent choices of the region-of-interest than other metrics. The new metric is straightforward to calculate and simple to implement in software commonly used for evaluation of medical imaging systems.

Photon-Counting Detector CT Virtual Monoenergetic Images in Cervical Trauma Imaging-Optimization of Dental Metal Artifacts and Image Quality.

OBJECTIVES: The aim of this study was to analyze the extent of dental metal artifacts in virtual monoenergetic (VME) images, as they often compromise image quality by obscuring soft tissue affecting vascular attenuation reducing sensitivity in the detection of dissections. METHODS: Neck photon-counting CT datasets of 50 patients undergoing contrast-enhanced trauma CT were analyzed. Hyperattenuation and hypoattenuation artifacts, muscle with and without artifacts and vessels with and without artifacts were measured at energy levels from 40 keV to 190 keV. The corrected artifact burden, corrected image noise and artifact index were calculated. We also assessed subjective image quality on a Likert-scale. RESULTS: Our study showed a lower artifact burden and less noise in artifact-affected areas above the energy levels of 70 keV for hyperattenuation artifacts (conventional polychromatic CT images 1123 ± 625 HU vs. 70 keV VME 1089 ± 733 HU, p = 0.125) and above of 80 keV for hypoattenuation artifacts (conventional CT images -1166 ± 779 HU vs. 80 keV VME -1170 ± 851 HU, p = 0.927). Vascular structures were less hampered by metal artifacts than muscles (e.g., corrected artifact burden at 40 keV muscle 158 ± 125 HU vs. vessels -63 ± 158 HU p < 0.001), which was also reflected in the subjective image assessment, which showed better ratings at higher keV values and overall better ratings for vascular structures than for the overall artifact burden. CONCLUSIONS: Our research suggests 70 keV might be the best compromise for reducing metal artifacts affecting vascular structures and preventing vascular contrast if solely using VME reconstructions. VME imaging shows only significant effects on the general artifact burden. Vascular structures generally experience fewer metal artifacts than soft tissue due to their greater distance from the teeth, which are a common source of such artifacts.

Balanced-force shim system for correcting magnetic-field inhomogeneities in the heart due to implanted cardioverter defibrillators.

Background: In the US, 1.4 million people have implanted ICDs for reducing the risk of sudden death due to ventricular arrhythmias. Cardiac MRI (cMR) is of particular interest in the ICD patient population as cMR is the optimal imaging modality for distinguishing cardiac conditions that predispose to sudden death, and it is the best method to plan and guide therapy. However, all ICDs contain a ferromagnetic transformer which imposes a large inhomogeneous magnetic field in sections of the heart, creating large image voids that can mask important pathology. A shim system was devised to resolve these ICD issues. A shim coil system (CSS) that corrects ICD artifacts over a user-selected Region-of-Interest (ROI), was constructed and validated. Methods: A shim coil was constructed that can project a large magnetic field for distances of ~15 cm. The shim-coil can be positioned safely anywhere within the scanner bore. The CSS includes a cantilevered beam to hold the shim coil. Remotely controlled MR-conditional motors allow 2 mm-accuracy three-dimensional shim-coil position. The shim coil is located above the subjects and the imaging surface-coils. Interaction of the shim coil with the scanner's gradients was eliminated with an amplifier that is in a constant current mode. Coupling with the scanners' radio-frequency (rf) coils, was reduced with shielding, low-pass filters, and cable shield traps. Software, which utilizes magnetic field (B0) mapping of the ICD inhomogeneity, computes the optimal location for the shim coil and its corrective current. ECG gated single- and multiple-cardiac-phase 2D GRE and SSFP sequences, as well as 3D ECG-gated respiratory-navigated IR-GRE (LGE) sequences were tested in phantoms and N = 3 swine with overlaid ICDs. Results: With all cMR sequences, the system reduced artifacts from >100 ppm to <25 ppm inhomogeneity, which permitted imaging of the entire left ventricle in swine with ICD-related voids. Continuously acquired Gradient recalled echo or Steady State Free Precession images were used to interactively adjust the shim current and coil location. Conclusion: The shim system reduced large field inhomogeneities due to implanted ICDs and corrected most ICD-related image distortions. Externally-controlled motorized translation of the shim coil simplified its utilization, supporting an efficient cardiac MRI workflow.

A kV-MV approach to CBCT metal artifact reduction using multi-layer MV-CBCT.

Objective. To demonstrate that complete cone beam CT (CBCT) scans from both MV-energy and kV-energy LINAC sources can reduce metal artifacts in radiotherapy guidance, while maintaining standard-of-care x-ray doses levels.Approach. MV-CBCT and kV-CBCT scans are acquired at half normal dose. The impact of lowered dose on MV-CBCT data quality is mitigated by the use of a 4-layer MV-imager prototype and reduced LINAC energy settings (2.5 MV) to improve photon capture. Additionally, the MV-CBCT is used to determine the 3D position and pose of metal implants, which in turn is used to guide model-based poly-energetic correction and interleaving of the kV-CBCT and MV-CBCT data. Certain edge-preserving regularization steps incorporated into the model-based correction algorithm further reduce MV data noise.Main results. The method was tested in digital phantoms and a real pelvis phantom with large 2.5″ spherical inserts, emulating hip replacements of different materials. The proposed method demonstrated an appealing compromise between the high contrast of kV-CBCT and low artifact content of MV-CBCT. Contrast-to-noise improved 3-fold compared to MV-CBCT with a clinical 1-layer architecture at matched dose (37 mGy) and edge blur levels. Visual delineation of the bladder and prostate improved noteably over kV- or MV-CBCT alone.Significance. The proposed method demonstrates that a full MV-CBCT scan can be combined with kV-CBCT to reduce metal artifacts without resorting to complicated beam collimation strategies to limit the MV-CBCT dose contribution. Additionally, significant improvements in CNR can be achieved as compared to metal artifact reduction through current clinical MV-CBCT practices.

Metal artifact reduction around cervical spine implant using diffusion tensor imaging at 3T: A phantom study.

PURPOSE: Diffusion MRI continues to play a key role in non-invasively assessing spinal cord integrity and pre-operative injury evaluation. However, post-operative Diffusion Tensor Imaging (DTI) acquisition of patients with metal implants results in severe geometric distortion. We propose and demonstrate a method to alleviate the technical challenges facing the acquisition of DTI on post-operative cases and longitudinal evaluation of therapeutics. MATERIAL AND METHODS: The described technique is based on the combination of the reduced Field-Of-View (rFOV) strategy and the phase segmented EPI, termed rFOV-PS-EPI. A custom-built phantom based on a cervical spine model with metal implants was used to collect DTI data at 3 Tesla scanner using: rFOV-PS-EPI, reduced Field-Of-View single-shot EPI (rFOV-SS-EPI), and conventional full FOV techniques including SS-EPI, PS-EPI, and readout-segmented EPI (RS-EPI). Geometric distortion, SNR, and signal void were assessed to evaluate images and compare the sequences. A two-sample t-test was performed with p-value of 0.05 or less to indicate statistical significance. RESULTS: The reduced FOV techniques showed better capability to reduce distortions compared to the Full FOV techniques. The rFOV-PS-EPI method provided DTI images of the phantom at the level of the hardware whereas the conventional rFOV-SS-EPI is useful only when the metal is approximately 20 mm away. In addition, compared to the rFOV-SS-EPI technique, the suggested approach produced smaller signal voids area as well as significantly reduced geometric distortion in Circularity (p < 0.005) and Eccentricity (p < 0.005) measurements. No statistically significant differences were found for these geometric distortion measurements between the rFOV-PS-EPI DTI sequence and conventional structural T2 images (p > 0.05). CONCLUSION: The combination of rFOV and a phase-segmented acquisition approach is effective for reducing metal-induced distortions in DTI scan on spinal cord with metal hardware at 3 T.

Dosimetric impact of CT metal artifact reduction for spinal implants in stereotactic body radiotherapy planning.

Background: Metal artifacts due to spinal implants may affect the accuracy of dose calculation for radiotherapy. However, the dosimetric impact of metal artifact reduction (MAR) for spinal implants in stereotactic body radiotherapy (SBRT) plans has not been well studied. The objective of this study was to evaluate the dosimetric impact of MAR in spinal SBRT planning with three clinically common dose calculation algorithms. Methods: Gammex phantom and 10 patients' computed tomography (CT) images were studied to investigate the effects of titanium implants. A commercial orthopedic MAR algorithm was employed to reduce artifacts. Dose calculations for SBRT were conducted on both artifact-corrected and uncorrected images using three commercial algorithms [analytical anisotropic algorithm (AAA), Acuros XB (AXB), and Monte Carlo (MC)]. Dose discrepancies between artifact-corrected and uncorrected cases were appraised using a dose-volume histogram (DVH) and 3-dimensional (3D) gamma analysis with different distance to agreement (DTA) and dose difference criteria. The gamma agreement index (GAI) was denoted as G(∆D, DTA). Statistical analysis of t-test was utilized to evaluate the dose differences of different algorithms. Results: The phantom study demonstrated that titanium metal artifacts can be effectively reduced. The patient cases study showed that dose differences between the artifact-corrected and uncorrected datasets were small evaluated by gamma index and DVH. Gamma analysis found that even the strict criterion local G(1,1) had average values ≥93.9% for the three algorithms. For all DVH metrics, average differences did not exceed 0.7% in planning target volume (PTV) and 2.1% in planning risk volume of spinal cord (PRV-SC). Statistical analysis showed that the observed dose differences of MC method were significantly larger than those of AAA (P<0.01 for D98% of PTV and P<0.001 for D0.1cc of spinal cord) and AXB methods (P<0.001 for D98% and P<0.0001 for D0.1cc). Conclusions: Dosimetric impact of artifacts caused by titanium implants is not significant in spinal SBRT planning, which indicates that dose calculation algorithms might not be very sensitive to CT number variation caused by titanium inserts.

Reliability and validity of a new deltoid muscle area measurement method after reverse shoulder arthroplasty.

Background: Accurate deltoid muscle assessment after reverse shoulder arthroplasty (RSA) is difficult using magnetic resonance imaging due to metal artifacts. We hypothesized that measuring the deltoid muscle area (DA) in the middle part of the deltoid's total length postoperatively would reduce metal artifacts and allow for an accurate assessment. This study aimed to assess the reliability and reproducibility of magnetic resonance imaging and evaluate its impact on postoperative outcomes. Methods: The DA in the middle part of the muscle's total length was measured twice by four examiners using pre and postoperative magnetic resonance imaging in 60 patients who underwent RSA (22 men, 38 women; mean age: 77.4 years). The DA at the greater tuberosity was measured preoperatively, and its correlation with the middle part of the deltoid's total length was evaluated. The Constant-Murley Score was measured at 2 years postoperatively, and its correlation with the DA in the middle part of the deltoid's total length pre- and postoperatively was assessed. Results: Intraclass correlation coefficients for intraobserver measurements of preoperative and postoperative DA in the middle part of the deltoid's total length were almost perfect, with mean values of 0.98 and 0.97, respectively. The intraclass correlation coefficients for interobserver reliability regarding the first and second DA measurements in the middle part of the deltoid's total length were 0.95 and 0.95 (preoperatively) and 0.89 and 0.90 (postoperatively). The Constant-Murley Score was assessed at 2 years postoperatively in 51 patients. Muscle strength was weakly and moderately correlated with preoperative DA (r = 0.33, P = .02) and postoperative DA (r = 0.49, P < .01), respectively. Conclusion: DA measurement in the middle part of the deltoid's total length after RSA was not affected by metal artifacts and had excellent reproducibility. This measurement method positively correlated with postoperative muscle strength, suggesting its usefulness for predicting postoperative muscle strength.

Preclinical validation of a novel deep learning-based metal artifact correction algorithm for orthopedic CT imaging.

PURPOSE: To validate a novel deep learning-based metal artifact correction (MAC) algorithm for CT, namely, AI-MAC, in preclinical setting with comparison to conventional MAC and virtual monochromatic imaging (VMI) technique. MATERIALS AND METHODS: An experimental phantom was designed by consecutively inserting two sets of pedicle screws (size Φ 6.5 × 30-mm and Φ 7.5 × 40-mm) into a vertebral specimen to simulate the clinical scenario of metal implantation. The resulting MAC, VMI, and AI-MAC images were compared with respect to the metal-free reference image by subjective scoring, as well as by CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and correction accuracy via adaptive segmentation of the paraspinal muscle and vertebral body. RESULTS: The AI-MAC and VMI images showed significantly higher subjective scores than the MAC image (all p < 0.05). The SNRs and CNRs on the AI-MAC image were comparable to the reference (all p > 0.05), whereas those on the VMI were significantly lower (all p < 0.05). The paraspinal muscle segmented on the AI-MAC image was 4.6% and 5.1% more complete to the VMI and MAC images for the Φ 6.5 × 30-mm screws, and 5.0% and 5.1% for the Φ 7.5 × 40-mm screws, respectively. The vertebral body segmented on the VMI was closest to the reference, with only 3.2% and 7.4% overestimation for Φ 6.5 × 30-mm and Φ 7.5 × 40-mm screws, respectively. CONCLUSIONS: Using metal-free reference as the ground truth for comparison, the AI-MAC outperforms VMI in characterizing soft tissue, while VMI is useful in skeletal depiction.

Revealing pelvic structures in the presence of metal hip prothesis via non-circular CBCT orbits.

As the expansion of Cone Beam CT (CBCT) to new interventional procedures continues, the burdensome challenge of metal artifacts remains. Photon starvation and beam hardening from metallic implants and surgical tools in the field of view can result in the anatomy of interest being partially or fully obscured by imaging artifacts. Leveraging the flexibility of modern robotic CBCT imaging systems, implementing non-circular orbits designed for reducing metal artifacts by ensuring data-completeness during acquisition has become a reality. Here, we investigate using non-circular orbits to reduce metal artifacts arising from metallic hip prostheses when imaging pelvic anatomy. As a first proof-of-concept, we implement a sinusoidal and a double-circle-arc orbit on a CBCT test bench, imaging a physical pelvis phantom, with two metal hip prostheses, housing a 3D-printed iodine-filled radial line-pair target. A standard circular orbit implemented with the CBCT test bench acted as comparator. Imaging data collection and processing, geometric calibration and image reconstruction was completed using in-house developed software programs. Imaging with the standard circular orbit, image artifacts were observed in the pelvic bones and only 33 out of the possible 45 line-pairs of the radial line-pair target were partially resolvable in the reconstructed images. Comparatively, imaging with both the sinusoid and double-circle-arc orbits reduced artifacts in the surrounding anatomy and enabled all 45 line-pairs to be visibly resolved in the reconstructed images. These results indicate the potential of non-circular orbits to assist in revealing previously obstructed structures in the pelvic region in the presence of metal hip prosthesis.

MARGANVAC: metal artifact reduction method based on generative adversarial network with variable constraints.

Objective.Metal artifact reduction (MAR) has been a key issue in CT imaging. Recently, MAR methods based on deep learning have achieved promising results. However, when deploying deep learning-based MAR in real-world clinical scenarios, two prominent challenges arise. One limitation is the lack of paired training data in real applications, which limits the practicality of supervised methods. Another limitation is that image-domain methods suitable for more application scenarios are inadequate in performance while end-to-end approaches with better performance are only applicable to fan-beam CT due to large memory consumption.Approach.We propose a novel image-domain MAR method based on the generative adversarial network with variable constraints (MARGANVAC) to improve MAR performance. The proposed variable constraint is a kind of time-varying cost function that can relax the fidelity constraint at the beginning and gradually strengthen the fidelity constraint as the training progresses. To better deploy our image-domain supervised method into practical scenarios, we develop a transfer method to mimic the real metal artifacts by first extracting the real metal traces and then adding them to artifact-free images to generate paired training data.Main results.The effectiveness of the proposed method is validated in simulated fan-beam experiments and real cone-beam experiments. All quantitative and qualitative results demonstrate that the proposed method achieves superior performance compared with the competing methods.Significance.The MARGANVAC model proposed in this paper is an image-domain model that can be conveniently applied to various scenarios such as fan beam and cone beam CT. At the same time, its performance is on par with the cutting-edge dual-domain MAR approaches. In addition, the metal artifact transfer method proposed in this paper can easily generate paired data with real artifact features, which can be better used for model training in real scenarios.

Effect of metal implants and metal artifacts on back-projected two-dimensional entrance fluence determined by EPID dosimetry.

PURPOSE: To evaluate the errors caused by metal implants and metal artifacts in the two-dimensional entrance fluences reconstructed using the back-projection algorithm based on electronic portal imaging device (EPID) images. METHODS: The EPID in the Varian VitalBeam accelerator was used to acquire portal dose images (PDIs), and then commercial EPID dosimetry software was employed to reconstruct the two-dimensional entrance fluences based on computed tomography (CT) images of the head phantoms containing interchangeable metal-free/titanium/aluminum round bars. The metal-induced errors in the two-dimensional entrance fluences were evaluated by comparing the γ results and the pixel value errors in the metal-affected regions. We obtained metal-artifact-free CT images by replacing the voxel values of non-metal inserts with those of metal inserts in metal-free CT images to evaluate the metal-artifact-induced errors. RESULTS: The γ passing rates (versus PDIs obtained without a phantom in the beam field (PDIair ), 2%/2 mm) for the back-projected two-dimensional entrance fluences of phantoms containing titanium or aluminum (BPTi /BPAl ) were reduced from 92.4% to 90.5% and 90.6%, respectively, relative to the metal-free phantom (BPmetal-free ). Titanium causes more severe metal artifacts in CT images than aluminum, and its removal resulted in a 0.0022 CU (median) reduction in the pixel value of BPTi artifact-free relative to BPTi in the metal-affected region. Moreover, the mean absolute error (MAE) and root mean square error (RMSE) decreased from 0.0050 CU and 0.0063 CU to 0.0034 CU and 0.0040 CU, respectively (vs. BPmetal-free ). CONCLUSION: Metal implants increase the errors in back-projected two-dimensional entrance fluences, and metals with higher electron densities cause more errors. For high-electron-density metal implants that produce severe metal artifacts (e.g., titanium), removing metal artifacts from the CT images can improve the accuracy of the two-dimensional entrance fluences reconstructed by back-projection algorithms.

[Sinogram interpolation combined with unsupervised image-to-image translation network for CT metal artifact correction].

OBJECTIVE: To propose a framework that combines sinogram interpolation with unsupervised image-to-image translation (UNIT) network to correct metal artifacts in CT images. METHODS: The initially corrected CT image and the prior image without artifacts, which were considered as different elements in two different domains, were input into the image transformation network to obtain the corrected image. Verification experiments were carried out to assess the effectiveness of the proposed method using the simulation data, and PSNR and SSIM were calculated for quantitative evaluation of the performance of the method. RESULTS: The experiment using the simulation data showed that the proposed method achieved better results for improving image quality as compared with other methods, and the corrected images preserved more details and structures. Compared with ADN algorithm, the proposed algorithm improved the PSNR and SSIM by 2.4449 and 0.0023 when the metal was small, by 5.9942 and 8.8388 for images with large metals, and by 8.8388 and 0.0130 when both small and large metals were present, respectively. CONCLUSION: The proposed method for metal artifact correction can effectively remove metal artifacts, improve image quality, and preserve more details and structures on CT images.

Radiotherapy planning of spine and pelvis using single-energy metal artifact reduction corrected computed tomography sets.

Metal artifacts produce incorrect Hounsfield units and impact treatment planning accuracy. This work evaluates the use of single-energy metal artifact reduction (SEMAR) algorithm for treatment planning by comparison to manual artifact overriding. CT datasets of in-house 3D-printed spine and pelvic phantoms with and without metal insert(s) and two treated patients with metal implants were analysed. CT number accuracy improved with the use of SEMAR filter: root mean square deviation (RMSD) from reference (without metal) reduced by 35.4 in spine and 98.8 in hip. The plan dose volume histograms (DVHs) and dosimetric measurements showed comparable results. SEMAR reconstruction improved planning efficiency.

An Optimized Method for Microcomputed Tomography Analysis of Trabecular Parameters of Metal Scaffolds for Bone Ingrowth.

Owing to its superior mechanical and biological properties, titanium metal is widely used in dental implants, orthopedic devices, and bone regenerative materials. Advances in 3D printing technology have led to more and more metal-based scaffolds being used in orthopedic applications. Microcomputed tomography (µCT) is commonly applied to evaluate the newly formed bone tissues and scaffold integration in animal studies. However, the presence of metal artifacts dramatically hinders the accuracy of µCT analysis of new bone formation. To acquire reliable and accurate µCT results that reflect new bone formation in vivo, it is crucial to lessen the impact of metal artifacts. Herein, an optimized procedure for calibrating µCT parameters using histological data was developed. In this study, the porous titanium scaffolds were fabricated by powder bed fusion based on computer-aided design. These scaffolds were implanted in femur defects created in New Zealand rabbits. After 8 weeks, tissue samples were collected to assess new bone formation using µCT analysis. Resin-embedded tissue sections were then used for further histological analysis. A series of deartifact two-dimensional (2D) µCT images were obtained by setting the erosion radius and the dilation radius in the µCT analysis software (CTan) separately. To get the µCT results closer to the real value, the 2D µCT images and corresponding parameters were subsequently selected by matching the histological images in the particular region. After applying the optimized parameters, more accurate 3D images and more realistic statistical data were obtained. The results demonstrate that the newly established method of adjusting µCT parameters can effectively reduce the influence of metal artifacts on data analysis to some extent. For further validation, other metal materials should be analyzed using the process established in this study.

Dosimetric characterization of single- and dual-port temporary tissue expanders for postmastectomy radiotherapy using Monte Carlo methods.

Purpose: The aim of this work was two-fold: a) to assess two treatment planning strategies for accounting CT artifacts introduced by temporary tissue-expanders (TTEs); b) to evaluate the dosimetric impact of two commercially available and one novel TTE. Methods: The CT artifacts were managed using two strategies. 1) Identifying the metal in the RayStation treatment planning software (TPS) using image window-level adjustments, delineate a contour enclosing the artifact, and setting the density of the surrounding voxels to unity (RS1). 2) Registering a geometry template with dimensions and materials from the TTEs (RS2). Both strategies were compared for DermaSpan, AlloX2, and AlloX2-Pro TTEs using Collapsed Cone Convolution (CCC) in RayStation TPS, Monte Carlo simulations (MC) using TOPAS, and film measurements. Wax slab phantoms with metallic ports and breast phantoms with TTEs balloons were made and irradiated with a 6 MV AP beam and partial arc, respectively. Dose values along the AP direction calculated with CCC (RS2) and TOPAS (RS1 and RS2) were compared with film measurements. The impact in dose distributions was evaluated with RS2 by comparing TOPAS simulations with and without the metal port. Results: For the wax slab phantoms, the dose differences between RS1 and RS2 were 0.5% for DermaSpan and AlloX2 but 3% for AlloX2-Pro. From TOPAS simulations of RS2, the impact in dose distributions caused by the magnet attenuation was (6.4 ± 0.4) %, (4.9 ± 0.7)%, and (2.0 ± 0.9)% for DermaSpan, AlloX2, and AlloX2-Pro, respectively. With breast phantoms, maximum differences in DVH parameters between RS1 and RS2 were as follows. For AlloX2 at the posterior region: (2.1 ± 1.0)%, (1.9 ± 1.0)% and (1.4 ± 1.0)% for D1, D10, and average dose, respectively. For AlloX2-Pro at the anterior region (-1.0 ± 1.0)%, (-0.6 ± 1.0)% and (-0.6 ± 1.0)% for D1, D10 and average dose, respectively. The impact in D10 caused by the magnet was at most (5.5 ± 1.0)% and (-0.8 ± 1.0)% for AlloX2 and AlloX2-Pro, respectively. Conclusion: Two strategies for accounting for CT artifacts from three breast TTEs were assessed using CCC, MC, and film measurements. This study showed that the highest differences with respect to measurements occurred with RS1 and can be mitigated if a template with the actual port geometry and materials is used.

Is AI the way forward for reducing metal artifacts in CT? Development of a generic deep learning-based method and initial evaluation in patients with sacroiliac joint implants.

PURPOSE: To develop a deep learning-based metal artifact reduction technique (dl-MAR) and quantitatively compare metal artifacts on dl-MAR-corrected CT-images, orthopedic metal artifact reduction (O-MAR)-corrected CT-images and uncorrected CT-images after sacroiliac (SI) joint fusion. METHODS: dl-MAR was trained on CT-images with simulated metal artifacts. Pre-surgery CT-images and uncorrected, O-MAR-corrected and dl-MAR-corrected post-surgery CT-images of twenty-five patients undergoing SI joint fusion were retrospectively obtained. Image registration was applied to align pre-surgery with post-surgery CT-images within each patient, allowing placement of regions of interest (ROIs) on the same anatomical locations. Six ROIs were placed on the metal implant and the contralateral side in bone lateral of the SI joint, the gluteus medius muscle and the iliacus muscle. Metal artifacts were quantified as the difference in Hounsfield units (HU) between pre- and post-surgery CT-values within the ROIs on the uncorrected, O-MAR-corrected and dl-MAR-corrected images. Noise was quantified as standard deviation in HU within the ROIs. Metal artifacts and noise in the post-surgery CT-images were compared using linear multilevel regression models. RESULTS: Metal artifacts were significantly reduced by O-MAR and dl-MAR in bone (p < 0.001), contralateral bone (O-MAR: p = 0.009; dl-MAR: p < 0.001), gluteus medius (p < 0.001), contralateral gluteus medius (p < 0.001), iliacus (p < 0.001) and contralateral iliacus (O-MAR: p = 0.024; dl-MAR: p < 0.001) compared to uncorrected images. Images corrected with dl-MAR resulted in stronger artifact reduction than images corrected with O-MAR in contralateral bone (p < 0.001), gluteus medius (p = 0.006), contralateral gluteus medius (p < 0.001), iliacus (p = 0.017), and contralateral iliacus (p < 0.001). Noise was reduced by O-MAR in bone (p = 0.009) and gluteus medius (p < 0.001) while noise was reduced by dl-MAR in all ROIs (p < 0.001) in comparison to uncorrected images. CONCLUSION: dl-MAR showed superior metal artifact reduction compared to O-MAR in CT-images with SI joint fusion implants.

Automatic registration of dental CT and 3D scanned model using deep split jaw and surface curvature.

BACKGROUND AND OBJECTIVES: In the medical field, various image registration applications have been studied. In dentistry, the registration of computed tomography (CT) volume data and 3D optically scanned models is essential for various clinical applications, including orthognathic surgery, implant surgical planning, and augmented reality. Our purpose was to present a fully automatic registration method of dental CT data and 3D scanned models. METHODS: We use a 2D convolutional neural network to regress a curve splitting the maxilla (i.e., upper jaw) and mandible (i.e., lower jaw) and the points specifying the front and back ends of the crown from the CT data. Using this regressed information, we extract the point cloud and vertices corresponding to the tooth crown from the CT and scanned data, respectively. We introduce a novel metric, called curvature variance of neighbor (CVN), to discriminate between highly fluctuating and smoothly varying regions of the tooth crown. The registration based on CVN enables more accurate fine registration while reducing the effects of metal artifacts. Moreover, the proposed method does not require any preprocessing such as extracting the iso-surface for the tooth crown from the CT data, thereby significantly reducing the computation time. RESULTS: We evaluated the proposed method with the comparison to several promising registration techniques. Our experimental results using three datasets demonstrated that the proposed method exhibited higher registration accuracy (i.e., 2.85, 1.92, and 7.73 times smaller distance errors for individual datasets) and smaller computation time (i.e., 4.12 times faster registration) than one of the state-of-the-art methods. Moreover, the proposed method worked considerably well for partially scanned data, whereas other methods suffered from the unbalancing of information between the CT and scanned data. CONCLUSIONS: The proposed method was able to perform fully automatic and highly accurate registration of dental CT data and 3D scanned models, even with severe metal artifacts. In addition, it could achieve fast registration because it did not require any preprocessing for iso-surface reconstruction from the CT data.

Physics-informed sinogram completion for metal artifact reduction in CT imaging.

Objective.Metal artifacts in the computed tomography (CT) imaging are unavoidably adverse to the clinical diagnosis and treatment outcomes. Most metal artifact reduction (MAR) methods easily result in the over-smoothing problem and loss of structure details near the metal implants, especially for these metal implants with irregular elongated shapes. To address this problem, we present the physics-informed sinogram completion (PISC) method for MAR in CT imaging, to reduce metal artifacts and recover more structural textures.Approach.Specifically, the original uncorrected sinogram is firstly completed by the normalized linear interpolation algorithm to reduce metal artifacts. Simultaneously, the uncorrected sinogram is also corrected based on the beam-hardening correction physical model, to recover the latent structure information in metal trajectory region by leveraging the attenuation characteristics of different materials. Both corrected sinograms are fused with the pixel-wise adaptive weights, which are manually designed according to the shape and material information of metal implants. To furtherly reduce artifacts and improve the CT image quality, a post-processing frequency split algorithm is adopted to yield the final corrected CT image after reconstructing the fused sinogram.Main results.We qualitatively and quantitatively evaluated the presented PISC method on two simulated datasets and three real datasets. All results demonstrate that the presented PISC method can effectively correct the metal implants with various shapes and materials, in terms of artifact suppression and structure preservation.Significance.We proposed a sinogram-domain MAR method to compensate for the over-smoothing problem existing in most MAR methods by taking advantage of the physical prior knowledge, which has the potential to improve the performance of the deep learning based MAR approaches.

Quantitative evaluation of the reduction of distortion and metallic artifacts in magnetic resonance images using the multiacquisition variable­resonance image combination selective sequence.

Magnetic resonance imaging (MRI) is superior to computed tomography (CT) in determining changes in tissue structure, such as those observed following inflammation and infection. However, when metal implants or other metal objects are present, MRI exhibits more distortion and artifacts compared with CT, which hinders the accurate measurement of the implants. A limited number of reports have examined whether the novel MRI sequence, multiacquisition variable-resonance image combination selective (MAVRIC SL), can accurately measure metal implants without distortion. Therefore, the present study aimed to demonstrate whether MAVRIC SL could accurately measure metal implants without distortion and whether the area around the metal implants could be well delineated without artifacts. An agar phantom containing a titanium alloy lumbar implant was used for the present study and was imaged using a 3.0 T MRI machine. A total of three imaging sequences, namely MAVRIC SL, CUBE and magnetic image compilation (MAGiC), were applied and the results were compared. Distortion was evaluated by measuring the screw diameter and distance between the screws multiple times in the phase and frequency directions by two different investigators. The artifact region around the implant was examined using a quantitative method following standardization of the phantom signal values. It was revealed that MAVRIC SL was a superior sequence compared with CUBE and MAGiC, as there was significantly less distortion, a lack of bias between the two different investigators and significantly reduced artifact regions. These results suggested the possibility of utilizing MAVRIC SL for follow-up to observe metal implant insertions.

Metal artifact reduction by virtual monoenergetic reconstructions from spectral brain CT.

Purpose: Conventional computed tomography (CT) images are severely affected by metal artifacts in patients with intracranial coils. Monoenergetic images have been suggested to reduce metal artifacts.The aim of this study was to assess metal artifacts in virtual monoenergetic images (VMIs) reconstructed from spectral brain CT. Methods: Thirty-two consecutive patients with intracranial coils examined by spectral non contrast brain CT (NCCT) at our center between November 2017 and April 2019 were included. Attenuation and standard deviations were measured in regions of interest (ROIs) at predefined areas in artifact-free and artifact-affected areas. Measurements were performed in conventional polyenergetic images (CIs) and the corresponding data for VMIs were retrieved through spectral diagrams for the each ROI. Subjective analysis was performed by visual grading of CIs and specific VMIs by two neuroradiologists, independently. Results: In artefact-affected image areas distal from the metal objects, the attenuation values decreased with higher energy level VMIs. The same effect was not seen for artefact-affected image areas close to the metal.Subjective rating of the artefact severity was significantly better in VMIs at 50 keV for one of the two reviewers compared to the CIs. Overall image quality and tissue differentiation scores were significantly higher for both reviewers in VMIs at 60 and 70 keV compared to CIs. Conclusion: Our quantitative and qualitative image analysis shown that there is a small significant reduction of intracranial coils artifacts severity by all monoenergetic reconstructions from 50 to 200 keV with preserved or increased overall subjective image quality compared to conventional images.