Severe Metal Artifacts Research Articles

Differentiation of radicular cysts and radicular granulomas via texture analysis of multi-slice computed tomography images.

This study aimed to establish a method for differentiating radicular cysts from granulomas via texture analysis (TA) of multi-slice computed tomography (CT) images. A total of 222 lesions with multi-slice computed tomography images acquired at our hospital between 2013 and 2022 that were pathologically diagnosed were included in this study. Cases of contrast-enhanced images, severe metallic artifacts, and lesions that were not sufficiently large to be analyzed were excluded. The images were chronologically divided into a training group and a validation group. The radiological characteristics were determined. Subsequently, a TA was performed. Pyradiomics software was used for the TA of three-dimensionally segmented volumes extracted from 2-mm slice thickness images with a soft-tissue algorithm. Features that differed significantly between the two lesions in the training group were extracted and used to create machine-learning models. The discriminative ability of these models was evaluated in the validation group using receiver operating characteristic curve analysis. A total of 131 lesions, comprising 28 radicular cysts and 103 granulomas, were analyzed. Forty-three texture features that exhibited significant variations were extracted. A support vector machine and decision tree model, with areas under the curves of 0.829 and 0.803, respectively, were created. These models showed high discriminative abilities, even for the validation group, with areas under the curve of 0.727 and 0.701, respectively. Both models showed superior performance compared with that of the models based on radiographic findings. Discriminatory models were established for the TA of radicular cysts and granulomas using CT images.

Image quality and metal artifact reduction in total hip arthroplasty CT: deep learning-based algorithm versus virtual monoenergetic imaging and orthopedic metal artifact reduction

BackgroundTo compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR).MethodsConventional CT and 130-keV monoenergetic images with and without O-MAR and DL-MAR images of 28 unilateral THA patients were reconstructed. Image quality, metal artifacts, and diagnostic confidence in bone, pelvic organs, and soft tissue adjacent to the prosthesis were jointly scored by two experienced musculoskeletal radiologists. Contrast-to-noise ratios (CNR) between bladder and fat and muscle and fat were measured. Wilcoxon signed-rank tests with Holm-Bonferroni correction were used.ResultsSignificantly higher image quality, higher diagnostic confidence, and less severe metal artifacts were observed on DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001 for all comparisons). Higher image quality, higher diagnostic confidence for bone and soft tissue adjacent to the prosthesis, and less severe metal artifacts were observed on DL-MAR when compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.014). CNRs were higher for DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001). Higher CNRs were observed on DL-MAR images compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.010).ConclusionsDL-MAR showed higher image quality, diagnostic confidence, and superior metal artifact reduction compared to conventional CT images and 130-keV monoenergetic images with and without O-MAR in unilateral THA patients.Relevance statementDL-MAR resulted into improved image quality, stronger reduction of metal artifacts, and improved diagnostic confidence compared to conventional and virtual monoenergetic images with and without metal artifact reduction, bringing DL-based metal artifact reduction closer to clinical application.Key points• Metal artifacts introduced by total hip arthroplasty hamper radiologic assessment on CT.• A deep-learning algorithm (DL-MAR) was compared to dual-layer CT images with O-MAR.• DL-MAR showed best image quality and diagnostic confidence.• Highest contrast-to-noise ratios were observed on the DL-MAR images.Graphical

Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography

Sparse-view computed tomography (SVCT) is regarded as a promising technique to accelerate data acquisition and reduce radiation dose. However, in the presence of metallic implants, SVCT inevitably makes the reconstructed CT images suffer from severe metal artifacts and streaking artifacts due to the lack of sufficient projection data. Previous stand-alone SVCT and metal artifact reduction (MAR) methods to solve the problem of simultaneously sparse-view and metal artifact reduction (SVMAR) are plagued by insufficient correction accuracy. To overcome this limitation, we propose a multi-domain deep unrolling network, called Mud-Net, for SVMAR. Specifically, we establish a joint sinogram, image, artifact, and coding domains deep unrolling reconstruction model to recover high-quality CT images from the under-sampled sinograms corrupted by metallic implants. To train this multi-domain network effectively, we embed multi-domain knowledge into the network training process. Comprehensive experiments demonstrate that our method is superior to both existing MAR methods in the full-view MAR task and previous SVCT methods in the SVMAR task.

Abstract 351: Novel Quantitative Imaging Biomarkers From Acute Brain CT Using Artificial Intelligence for Predicting Neurological Prognosis in Cardiac Arrest Patients: A Pilot Study

Background: Effective use of information extracted from medical images has attracted attention for decision support in primary care of patients with cardiac arrest (CA). Hypothesis and Aims: This study aimed to predict the neurological prognosis of patients with cardiac arrest by quantitative imaging biomarkers (QIBs) extracted from acute CT brain images before admission using artificial intelligence. Methods: Eighty-six CA patients (good prognosis; Glasgow-Pittsburgh cerebral performance categories [CPC]=1 or 2 at 90 days after the CA : 32 patients, poor prognosis; CPC=3, 4, or 5 at 90 days after the CA : 54 patients) treated at three hospitals between 2017 and 2019 were retrospectively analyzed. None of the CA patients caused by trauma or brain damage were included in the database. The data were divided into training and test data, and the training data were used to select informative QIBs for classification between the good and poor neurological prognosis in this analysis. Results: Seven CA patients’ data were excluded from the study due to severe metal and bone artifacts broadly spreading on the brain image, and 1170 QIBs were extracted from whole brain and regional brain interest volumes on CT images of each patient. The QIBs were composed of first-order histogram and texture features and informative QIBs for the classification were selected by feature selection algorithms using the training dataset. The feature corresponding to the gray level having a maximum gradient in the image histogram from the brain region of interest on CT images showed the strongest significant difference between the good and poor neurological prognosis of the test data set with p=0.009 and AUC=0.775 (95% CI; 0.590-0.960). Conclusions: We have shown that QIB can be used to predict neurological prognosis in patients with CA. Because this is a pilot study for methodological development and there are limitations in sample size and outcomes, new studies on a larger number of patients are required.

A new dental CBCT metal artifact reduction method based on a dual-domain processing framework

Objective. Cone beam computed tomography (CBCT) has been wildly used in clinical treatment of dental diseases. However, patients often have metallic implants in mouth, which will lead to severe metal artifacts in the reconstructed images. To reduce metal artifacts in dental CBCT images, which have a larger amount of data and a limited field of view compared to computed tomography images, a new dental CBCT metal artifact reduction method based on a projection correction and a convolutional neural network (CNN) based image post-processing model is proposed in this paper. Approach. The proposed method consists of three stages: (1) volume reconstruction and metal segmentation in the image domain, using the forward projection to get the metal masks in the projection domain; (2) linear interpolation in the projection domain and reconstruction to build a linear interpolation (LI) corrected volume; (3) take the LI corrected volume as prior and perform the prior based beam hardening correction in the projection domain, and (4) combine the constructed projection corrected volume and LI-volume slice-by-slice in the image domain by two concatenated U-Net based models (CNN1 and CNN2). Simulated and clinical dental CBCT cases are used to evaluate the proposed method. The normalized root means square difference (NRMSD) and the structural similarity index (SSIM) are used for the quantitative evaluation of the method. Main results. The proposed method outperforms the frequency domain fusion method (FS-MAR) and a state-of-art CNN based method on the simulated dataset and yields the best NRMSD and SSIM of 4.0196 and 0.9924, respectively. Visual results on both simulated and clinical images also illustrate that the proposed method can effectively reduce metal artifacts. Significance. This study demonstrated that the proposed dual-domain processing framework is suitable for metal artifact reduction in dental CBCT images.

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

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. 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. The γ passing rates (versus PDIs obtained without a phantom in the beam field (PDIair ), 2%/2mm) 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 ). 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.

Deep learning based Metal Artifact Reduction in X-ray Computed Tomography

Due to the presence of metal implants, computed tomography(CT) images of patients undergoing CT scans produce severe metal artifacts. In recent years, metal artifact reduction (MAR) algorithms have been developed at a high speed, and deep learning-based MAR algorithms have proved to be one of the very effective methods. However, based on the fact that most of the current deep learning-based solutions utilize simulated data for supervised training, these models are difficult to be directly applied in clinical settings. In addition, current MAR schemes still face considerable challenges in reduce streak artifacts and dark band artifacts from metal artifacts. In this paper, we propose an improved unsupervised network model based on CycleGAN suitable for small-sample training of medical images. In order to enhance the extraction of image features, the generator in this paper adopts the U-Net network model, which is commonly used in medical image processing, and the residual connection and attention modules are added between each layer of the U-Net network. In addition, in order to better repair the dark band artifacts, the input of the network is processed by mixing the original image and the prior image, and in order to make the output image retain as much as possible the effective information of the original image, this paper adds the correlation constraints of the prior image and the original image to the output of the network. Experimentally, it is proved that the method in this paper has very good ability to reduce metal artifacts.

Panoramic dental tomosynthesis imaging by use of CBCT projection data

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient’s dental arch from the patient’s CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image.

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.

Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting.

Metal artifacts in dental computed tomography (CT) images, caused by highly X-ray absorbing objects, such as dental implants or crowns, often more severely compromise image readability than in medical CT images. Since lower tube voltages are used for dental CTs in spite of the more frequent presence of metallic objects in the patient, metal artifacts appear more severely in dental CT images, and the artifacts often persist even after metal artifact correction. The direct sinogram correction (DSC) method, which directly corrects the sinogram using the mapping function derived by minimizing the sinogram inconsistency, works well in the case of mild metal artifacts, but it often fails to correct severe metal artifacts. We propose a modified DSC method to reduce severe metal artifacts, and we have tested it on human dental images. We first segment the metallic objects in the CT image, and then we forward-project the segmented metal mask to identify the metal traces in the projection data with computing the metal path length for the rays penetrating the metal mask. In the sinogram correction with the DSC mapping function, we apply the weighting proportional to the metal path length. We have applied the proposed method to the phantom and patient images taken at the X-ray tube voltage of 90 kVp. We observed that the proposed method outperforms the original DSC method when metal artifacts were severe. However, we need further extensive studies to verify the proposed method for various CT scan conditions with many more patient images.

Multiacquisition with variable resonance image combination T2 (MAVRIC SL T2) for postoperative cervical spine with artificial disc replacement

Anterior cervical discectomy with artificial disc replacement (ADR) is an effective treatment of cervical degenerative disc disease. However, postoperative MRI due to recurrent neck/radicular pain is limited due to severe metallic artifacts of artificial disc instrument. Multiacquisition with variable resonance image combination selective T2 (MAVRIC SL T2) has been developed as an MRI technique for metal artifact reduction but has not been evaluated for the postoperative cervical spine with ADR. In our study, we compared MAVRIC SL T2 with the fast spin echo (FSE) T2-weighted sequence (T2WI), which was an essential MR sequence for evaluation of the cervical neural structure, for metallic artifact reduction in the post-operative cervical spine with ADR. Our study revealed MAVRIC SL T2 showed smaller signal void areas, less distortion and signal pile-up, and was more clinically useful than T2WI (p < 0.05). The spinal cord, vertebral bodies, both neural foramina, and anterior paravertebral soft tissue were significantly more visible with MAVRIC SL T2 than with T2WI (p < 0.05). MAVRIC SL T2 might be a useful technique for the evaluation of postoperative cervical spine with ADR and complements T2WI in the evaluation of the spinal cord and nerve roots which were important structures for post-operative recurrent neck/radicular pain.

Effectiveness of orthopedic metal artifact reduction among patients undergoing computed tomography at a tertiary setting – A cross-sectional study

Background: In computed tomography (CT), metal implants increase the inconsistencies between the measured data and the linear assumption of the radon transform made by the analytic CT reconstruction algorithm. Aims and Objectives: The aims of this study was to evaluate the effectiveness of orthopedic metal artifact reduction (O-MAR) among patients undergoing CT at a tertiary setting. Materials and Methods: A cross-sectional observational study was conducted on 30 subjects attending the department of radiology for CT scan for 1 year from December 2018 to 2019. The CT scan was performed in 64 detector row (PHILIPS INGENUITY CORE 128 slice) scanner. The patient was asked for the presence of metallic objects/history of implants, and the topo gram confirms it. Image noise was considered as the primary outcome variable. The statistical analysis was performed using the coGuide software and P&lt;0.005. Results: All the thirty participants were analyzed finally. Image noise in plain image type was 79.5 SD of Hounsfield units, and in O-MAR image type, it was 44.01 SD of Hounsfield units denoting a higher percentage of SD in plain than O-MAR images. Conclusion: The O-MAR application helped in improving image quality of CT for patients in the aspect of metal artifact reduction and soft-tissue profile. However, it can also improve diagnostic quality in the evaluation of patients with severe metallic artifacts by decreasing the negative impacts of orthopedic metals.

Comparison of Image Quality and Radiation Dose Between Single-Energy and Dual-Energy Images for the Brain With Stereotactic Frames on Dual-Energy Cerebral CT.

Preoperative stereotactic planning of deep brain stimulation (DBS) using computed tomography (CT) imaging in patients with Parkinson's disease (PD) is of clinical interest. However, frame-induced metal artifacts are common in clinical practice, which can be challenging for neurosurgeons to visualize brain structures. To evaluate the image quality and radiation exposure of patients with stereotactic frame brain CT acquired using a dual-source CT (DSCT) system in single- and dual-energy modes. We included 60 consecutive patients with Parkinson's disease (PD) and randomized them into two groups. CT images of the brain were performed using DSCT (Group A, an 80/Sn150 kVp dual-energy mode; Group B, a 120 kVp single-energy mode). One set of single-energy images (120 kVp) and 10 sets of virtual monochromatic images (50-140 keV) were obtained. Subjective image analysis of overall image quality was performed using a five-point Likert scale. For objective image quality evaluation, CT values, image noise, signal-to-noise ratio (SNR), and contrast-to-noise (CNR) were calculated. The radiation dose was recorded for each patient. The mean effective radiation dose was reduced in the dual-energy mode (1.73 mSv ± 0.45 mSv) compared to the single-energy mode (3.16 mSv ± 0.64 mSv) (p < 0.001). Image noise was reduced by 46-52% for 120-140 keV VMI compared to 120 kVp images (both p < 0.01). CT values were higher at 100-140 keV than at 120 kVp images. At 120-140 keV, CT values of brain tissue showed significant differences at the level of the most severe metal artifacts (all p < 0.05). SNR was also higher in the dual-energy mode 90-140 keV compared to 120 kVp images, showing a significant difference between the two groups at 120-140 keV (all p < 0.01). The CNR was significantly better in Group A for 60-140 keV VMI compared to Group B (both p < 0.001). The highest subjective image scores were found in the 120 keV images, while 110-140 keV images had significantly higher scores than 120 kVp images (all p < 0.05). DSCT images using dual-energy modes provide better objective and subjective image quality for patients with PD at lower radiation doses compared to single-energy modes and facilitate brain tissue visualization with stereotactic frame DBS procedures.

A fidelity-embedded learning for metal artifact reduction in dental CBCT.

Dental cone-beam computed tomography (CBCT) has been increasingly used for dental and maxillofacial imaging. However, the presence of metallic inserts, such as implants, crowns, and dental braces, violates the CT model assumption, which leads to severe metal artifacts in the reconstructed CBCT image, resulting in the degradation of diagnostic performance. In this study, we used deep learning to reduce metalartifacts. The metal artifacts, appearing as streaks and shadows, are nonlocal and highly associated with various factors, including the geometry of metallic inserts, energy-dependent attenuation, and energy spectrum of the incident X-ray beam, making it difficult to learn their complicated structures directly. To provide a step-by-step environment in which deep learning can be trained, we propose an iterative learning approach in which the network at each iteration step learns the correction error caused by the previous network, while enforcing the data fidelity in the projection domain. To generate a realistic paired training dataset, metal-free CBCT scans were collected from patients without metallic inserts, and then simulated metal projection data were added to generate the corresponding metal-corrupted projectiondata. The feasibility of the proposed method was investigated in clinical metal-affected CBCT scans, as well as simulated metal-affected CBCT scans. The results show that the proposed method significantly reduces metal artifacts while preserving the morphological structures near metallic objects and outperforms direct image domainlearning. The proposed fidelity-embedded learning can effectively reduce metal artifacts in dental CBCT compared with direct image domainlearning.

Metal artifact reduction in 2D CT images with self-supervised cross-domain learning

The presence of metallic implants often introduces severe metal artifacts in the x-ray computed tomography (CT) images, which could adversely influence clinical diagnosis or dose calculation in radiation therapy. In this work, we present a novel deep-learning-based approach for metal artifact reduction (MAR). In order to alleviate the need for anatomically identical CT image pairs (i.e. metal artifact-corrupted CT image and metal artifact-free CT image) for network learning, we propose a self-supervised cross-domain learning framework. Specifically, we train a neural network to restore the metal trace region values in the given metal-free sinogram, where the metal trace is identified by the forward projection of metal masks. We then design a novel filtered backward projection (FBP) reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images. To preserve the fine structure details and fidelity of the final MAR image, instead of directly adopting convolutional neural network (CNN)-refined images as output, we incorporate the metal trace replacement into our framework and replace the metal-affected projections of the original sinogram with the prior sinogram generated by the forward projection of the CNN output. We then use the FBP algorithms for final MAR image reconstruction. We conduct an extensive evaluation on simulated and real artifact data to show the effectiveness of our design. Our method produces superior MAR results and outperforms other compelling methods. We also demonstrate the potential of our framework for other organ sites.

Complex 3D scan trajectories for industrial cone-beam computed tomography using a hexapod

Industrial x-ray computed tomography (CT) represents an established measurement technique in the field of nondestructive testing and dimensional metrology. However, the measurement accuracy is sometimes limited by measurement artifacts that cannot be avoided using a standard circular scan trajectory. This problem can be addressed with the aid of flexible 3D trajectories, but up to date, the application of these is mainly restricted to special CT devices using robot arms. In this paper, we present results using a hexapod as an additional positioning system in a commercial industrial CT scanner. In addition to the 360∘ rotation, task-specific tilting of the part during the scan is possible in this way. We used and adapted geometry calibration procedures based on a multi-sphere reference object to enable reconstruction with high accuracy. Using a demonstrator test fixture with high absorbing elements, we show that severe metal and truncation artifacts can be avoided for a region-of-interest scan. Furthermore, cone-beam artifacts, which are inherent to circular scan trajectories, can be reduced significantly. Using measurement objects that can be measured well with a circular trajectory, we found that applying a 3D trajectory leads to dimensional measurement deviations in the same range or even lower than those of a circular trajectory. This suggests that the pose repeatability of the hexapod is sufficient to perform complex scan paths without general loss of accuracy. The obtained results could be relevant for end users of conventional CT scanners, as upgrading existing devices is in principle possible. The presented investigations form the basis for the application of trajectory optimization algorithms.

Reduction of microwave ablation needle related metallic artifacts using virtual monoenergetic images from dual-layer detector spectral CT in a rabbit model with VX2 tumor

The purpose of the study was to investigate the application of virtual monoenergetic images (VMIs) in reducing metal artifacts in rabbit VX2 liver cancer models treated with microwave ablation (MWA) therapy. A total of 31 VX2 liver cancer models that accepted CT-guided percutaneous microwave ablation were analyzed. Conventional images (CIs) with the most severe metallic artifacts and their corresponding energy levels from 40 to 200 keV with 10 keV increment of VMIs were reconstructed for further analysis. Objective image analysis was assessed by recording the attenuation (HU) and standard deviation of the most severe hyper/hypodense artifacts as well as artifact-impaired liver parenchyma tissue. Two radiologists visually evaluated the extent of artifact reduction, assessed data obtained by a diagnostic evaluation of liver tissues, and appraised the appearance of new artifacts according to the grade score. Statistical analysis was performed to compare the difference between CIs and each energy level of VMIs. For subjective assessment, reductions in hyperdense and hypodense artifacts were observed at 170–200 keV and 160–200 keV, respectively. The outcomes of the diagnostic evaluation of adjacent liver tissue were statistically higher at 140–200 keV for VMIs than for CIs. In terms of objective evaluation results, VMIs at 90–200 keV reduced the corrected attenuation of hyperdense and of artifact-impaired liver parenchyma compared with CIs (P < 0.001). When VMIs at 80–200 keV decreased the hypodense artifacts (P < 0.001). Therefore, we concluded that VMIs at 170–200 keV can obviously decrease the microwave ablation needle-related metal artifacts objectively and subjectively in rabbit VX2 liver cancer models.

CLIMAR: classified linear interpolation based metal artifact reduction for severe metal artifact reduction in x-ray CT imaging

In x-ray CT imaging, the existence of metal in the imaging field of view deteriorates the quality of the reconstructed image. This is because rays penetrating dense metal implants are highly corrupted, causing huge inconsistency between projection data. The result appears as strong artifacts such as black and white streaks on the reconstructed image disturbing correct diagnosis. For several decades, there have been various trials to reduce metal artifacts for better image quality. As the computing power of computer processors became more powerful, more complex algorithms with improved performance have been introduced. For instance, the initially developed metal artifact reduction (MAR) algorithms based on simple sinogram interpolation were combined with computationally expensive iterative reconstruction techniques to pursue better image quality. Recently, even machine learning based techniques have been introduced, which require huge amounts of computations for training. In this paper, we introduce an image based novel MAR algorithm in which severe metal artifacts such as black shadings are detected by the proposed method in a straightforward manner based on a linear interpolation. To do that, a new concept of metal artifact classification is devised using linear interpolation in the virtual projection domain. The proposed method reduces severe artifacts very quickly and effectively and has good performance to keep the detailed body structure preserved. Results of qualitative and quantitative comparisons with other representative algorithms such as LIMAR and NMAR support the excellence of the proposed algorithm. Thanks to the nature of reducing artifacts in the image itself and its low computational cost, the proposed algorithm can function as an initial image generator for other MAR algorithms, as well as being integrated in the modalities under limited computation power such as mobile CT scanners.

MRI and dual-energy CT fusion anatomic imaging in Ru-106 ophthalmic brachytherapy

PurposeBrachytherapy with Ru-106 is widely used for the treatment of intraocular tumors, and its efficacy depends on the accuracy of radioactive plaque placement. Ru-106 plaques are MRI incompatible and create severe metal artifacts on conventional CT scans. Dual-energy CT scans (DECT) may be used to suppress such artifacts. This study examines the possibility of creating fusion images from MRI scans (preoperatively) and DECT scans (with the plaque in place) as a tool for confirming the anatomic accuracy of plaque placement. Methods and MaterialsSix patients with intraocular lesions (5 with choroidal melanoma and 1 with a retinal vasoproliferative lesion) were included. Fusion images of preoperative MRI scans and DECT scans with the plaque in place were created with the Demo version of the ImFusion suite (ImFusion GmbH, Munchen Germany). Clearance margins between the tumor and plaque edge in axial, transverse, and coronal planes as well as the elevation of the posterior plaque edge from the sclera were recorded and associated with the location of the lesion. ResultsPlaque-tumor clearance margins for transverse, sagittal, and coronal planes were higher for anteriorly located lesions (5.13 mm ± 0.11 [5.0–5.2], 5.10 mm ± 0.26 [4.9–5.4], and 5.33 mm ± 0.45 [4.9–5.8] respectively) than for posteriorly located lesions (4.16 mm ± 1.44 [2.5–5.1], 4.13 mm ± 1.42 [2.5–5.1], and 4.2 mm ± 1.21 [2.8–5.0], respectively). The elevation of the posterior plaque edge from the sclera was 0.33 mm ± 0.28 [0–0.5] and 0.63 mm ± 0.60 [0.7–1.2] for posterior and anterior lesions, respectively. ConclusionsFusion images between DECT and MRI scans may be used as a tool to confirm the accuracy of Ru-106 plaque placement in relation with the intraocular tumors in ophthalmic brachytherapy.

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.