Influence of tube current and metal artifact reduction on the diagnosis of external cervical resorption in teeth adjacent to a dental implant in CBCT: an ex-vivo study.

OBJECTIVES: This ex-vivo study aimed to assess the influence of tube current (mA) and metal artifact reduction (MAR) on the diagnosis of early external cervical resorption (EECR) in cone-beam computed tomography (CBCT) in the presence of an adjacent dental implant. MATERIALS AND METHODS: Twenty-three single-rooted teeth were sectioned longitudinally and EECR was induced using a spherical drill and 5% nitric acid in 10 teeth. Each tooth was positioned in the socket of the lower right canine of a dry human mandible and CBCT scans were acquired using 90 kVp, voxel of 0.085 mm, field of view of 5 x 5 cm, and varying tube current (4, 8 or 12 mA), MAR (enabled or disabled) and implant conditions (with a zirconia implant in the socket of the lower right first premolar or without). Five oral radiologists evaluated the presence of EECR in a 5-point scale and the diagnostic values (area under the receiver operating characteristic curve - AUC, sensitivity, and specificity) were compared using multi-way Analysis of Variance (α = 0.05). Kappa test assessed intra-/inter-evaluator agreement. RESULTS: The tube current only influenced the AUC values in the presence of the implant and when MAR disabled; in this case, 8 mA showed lower values (p<0.007). MAR did not influence the diagnostic values (p>0.05). In general, the presence of an implant reduced the AUC values (p<0.0001); sensitivity values with 8 mA and MAR disabled, and specificity values with 4 mA and MAR enabled and 8 mA regardless MAR were also decreased (p<0.0001). CONCLUSIONS: Variations in tube current and MAR were unable to improve EECR detection, which was impaired by the presence of an adjacent implant. CLINICAL RELEVANCE: Increasing tube current or activating MAR tool does not improve EECR diagnosis, which is hampered by the artifacts generated by dental implants.

Removing ring artifacts in CBCT images via Transformer with unidirectional vertical gradient loss.

BACKGROUND: Cone-beam computed tomography (CBCT), an important medical modality for disease detection and diagnosis, is currently widely used in clinical practice. However, due to the inconsistent response of CBCT detectors, the lack of proper calibration often leads to the occurrence of ring artifacts in CBCT-reconstructed images. These artifacts may affect physicians' assessment and diagnosis. Therefore, effective elimination of ring artifacts in CBCT images without degrading image quality is important. PURPOSE: Given the pros and cons of existing methods for removing ring artifacts in CBCT images, this paper is devoted to designing a specific Transformer for this task, leveraging the global and local modeling ability of Transformer. METHODS: We design a loss function with dual-domain information fusion for the vanilla Transformer to correct ring artifacts in CBCT images. The method operates in image domain to predict artifact-free outputs and preserve more image details. Meanwhile, we design a tailored loss function incorporating polar domain optimization to remove ring artifacts more effectively. Specifically, an unidirectional gradient loss that constrains vertical gradients in polar domain is imposed, based on the geometric prior that in polar coordinates, ring artifacts predominately affect horizontal gradients while minimally influencing vertical gradients. RESULTS: We conduct extensive ablative and comparative experiments on CBCT/CT image set to validate the performance of the proposed method. First, four ablation experiments demonstrate the feasibility of our approach. Then, we compare our method with several classical methods and the latest state-of-the-arts, and our method achieves the highest quality of corrected images as well as the best evaluation metrics. In these experiments, 5332 CT images were used for training, and 550 CT images, and 500 real CBCT images were used for testing. The source code is available at https://github.com/shasha521/CBCT. CONCLUSIONS: Experimental results demonstrate that our method can significantly improve the effectiveness of ring artifact correction. By capitalizing on dual-domain information fusion and a customized loss function, the improved Transformer can not only effectively remove ring artifacts in CBCT images, but also preserve the details of original images quite well.

Scoping review of magnetic resonance motion imaging phantoms.

To review and analyze the currently available MRI motion phantoms. Publications were collected from the Toronto Metropolitan University Library, PubMed, and IEEE Xplore. Phantoms were categorized based on the motions they generated: linear/cartesian, cardiac-dilative, lung-dilative, rotational, deformation or rolling. Metrics were extracted from each publication to assess the motion mechanisms, construction methods, as well as phantom validation. A total of 60 publications were reviewed, identifying 48 unique motion phantoms. Translational movement was the most common movement (used in 38% of phantoms), followed by cardiac-dilative (27%) movement and rotational movement (23%). The average degrees of freedom for all phantoms were determined to be 1.42. Motion phantom publications lack quantification of their impact on signal-to-noise ratio through standardized testing. At present, there is a lack of phantoms that are designed for multi-role as many currently have few degrees of freedom.

Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index.

PURPOSE: For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design. METHODS: By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects. RESULTS: The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences. CONCLUSION: We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.

Optimized raw data selection for artifact reduction of breathing controlled four-dimensional sequence scanning.

Background and purpose: Even with most breathing-controlled four-dimensional computed tomography (4DCT) algorithms image artifacts caused by single significant longer breathing still occur, resulting in negative consequences for radiotherapy. Our study presents first phantom examinations of a new optimized raw data selection and binning algorithm, aiming to improve image quality and geometric accuracy without additional dose exposure. Materials and methods: To validate the new approach, phantom measurements were performed to assess geometric accuracy (volume fidelity, root mean square error, Dice coefficient of volume overlap) for one- and three-dimensional tumor motion trajectories with and without considering motion hysteresis effects. Scans without significantly longer breathing cycles served as references. Results: Median volume deviations between optimized approach and reference of at maximum 1% were obtained considering all movements. In comparison, standard reconstruction yielded median deviations of 9%, 21% and 12% for one-dimensional, three-dimensional, and hysteresis motion, respectively. Measurements in one- and three-dimensional directions reached a median Dice coefficient of 0.970 ± 0.013 and 0.975 ± 0.012, respectively, but only 0.918 ± 0.075 for hysteresis motions averaged over all measurements for the optimized selection. However, for the standard reconstruction median Dice coefficients were 0.845 ± 0.200, 0.868 ± 0.205 and 0.915 ± 0.075 for one- and three-dimensional as well as hysteresis motions, respectively. Median root mean square errors for the optimized algorithm were 30 ± 16 HU2 and 120 ± 90 HU2 for three-dimensional and hysteresis motions, compared to 212 ± 145 HU2 and 130 ± 131 HU2 for the standard reconstruction. Conclusions: The algorithm was proven to reduce 4DCT-related artifacts due to missing projection data without further dose exposure. An improvement in radiotherapy treatment planning due to better image quality can be expected.

Influence of CBCT filters and contrast adjustments on peri-implant buccal bone thickness measurement and blooming expression.

OBJECTIVES: To assess whether filter and contrast adjustments can improve the accuracy of CBCT in measuring the buccal bone thickness (BBT) adjacent to dental implants by reducing blooming artifacts. MATERIALS AND METHODS: Homogeneous bone blocks with peri-implant BBT of 0.3 mm, 0.5 mm, and 1 mm were scanned using the Orthophos SL system. Three dentists measured the BBT in 234 CBCT scans under different settings of contrast adjustments and 'Sharpen' filter activation. Additionally, implant diameter measurements were taken to assess blooming artifact expression. The differences between tomographic and actual measurements of BBT and implant diameter [(CBCT - actual) * 100 / actual] were subjected to Mixed ANOVA (α = 0.05). RESULTS: The group with the thinnest BBT (0.3 mm) had the greatest difference between tomographic and actual measurements (79.9% ± 29.0%). Conversely, the 0.5 mm (36.1% ± 38.4%) and 1 mm (29.4% ± 12.3%) groups exhibited lower differences (p < 0.05). 'Sharpen' filter activation reduced blooming expression since it resulted in a lower difference for implant diameter (p < 0.05), but it did not influence BBT measurements (p = 0.673). Contrast settings had no impact on BBT (p = 0.054) or implant diameter measurements (p = 0.079). CONCLUSION: Although filter activation reduced blooming artifacts, neither filter nor contrast adjustments improved the accuracy of CBCT in measuring peri-implant BBT; actual BBT influenced this task. CLINICAL RELEVANCE: When assessing the peri-implant buccal bone plate in the CBCT system studied, dental surgeons may find it beneficial to adjust contrast and apply filters according to their preferences, since such adjustments were found to have no adverse effects on the diagnostic accuracy of this task. The use of the 'Sharpen' filter may lead to improved representation of implant dimensions.

Balloon-Assisted Stent Visualization: A Simple Technique for Precise Measurement of Previously Placed Stent Diameter.

Stent-assisted coil embolization is effective for treating intracranial aneurysms, improving outcomes and reducing recurrence rates. However, accurately measuring the diameter of a previously placed stent during imaging can be challenging due to coil artifacts. This poses difficulties in determining the coil packing and size of additional stents needed during retreatment. In a reported case, the use of a balloon enabled precise assessment of stent deployment. A 50-year-old male with a history of basilar artery-left superior cerebellar artery aneurysm underwent coil embolization, direct clipping, and stent-assisted coil embolization (SAC) over a span of 14 years. However, the aneurysm showed reenlargement over time. To address the recurrence, a balloon was used to assess the previously placed Neuroform Atlas stent. Additional coils were inserted outside the stent, and a Low-profile Visualized Intraluminal Support Blue stent was added. Postoperatively, there were no new neurological issues, and a follow-up magnetic resonance imaging showed no ischemic lesions . Balloon-assisted stent visualization (BASV) may be a useful method in the retreatment of SAC. It has the potential to provide valuable information for treatment planning.

Efficacy and safety of FDG-PET for determining target volume during intensity-modulated radiotherapy for head and neck cancer involving the oral level.

PURPOSE: To determine the efficacy and safety of target volume determination by 18F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) for intensity-modulated radiation therapy (IMRT) for locally advanced head and neck squamous cell carcinoma (HNSCC) extending into the oral cavity or oropharynx. METHODS: We prospectively treated 10 consecutive consenting patients with HNSCC using IMRT, with target volumes determined by PET-CT. Gross tumor volume (GTV) and clinical target volume (CTV) at the oral level were determined by two radiation oncologists for CT, magnetic resonance imaging (MRI), and PET-CT. Differences in target volume (GTVPET, GTVCT, GTVMRI, CTVPET, CTVCT, and CTVMRI) for each modality and the interobserver variability of the target volume were evaluated using the Dice similarity coefficient and Hausdorff distance. Clinical outcomes, including acute adverse events (AEs) and local control were evaluated. RESULTS: The mean GTV was smallest for GTVPET, followed by GTVCT and GTVMRI. There was a significant difference between GTVPET and GTVMRI, but not between the other two groups. The interobserver variability of target volume with PET-CT was significantly less than that with CT or MRI for GTV and tended to be less for CTV, but there was no significant difference in CTV between the modalities. Grade ≤ 3 acute dermatitis, mucositis, and dysphagia occurred in 55%, 88%, and 22% of patients, respectively, but no grade 4 AEs were observed. There was no local recurrence at the oral level after a median follow-up period of 37 months (range, 15-55 months). CONCLUSIONS: The results suggest that the target volume determined by PET-CT could safely reduce GTV size and interobserver variability in patients with locally advanced HNSCC extending into the oral cavity or oropharynx undergoing IMRT. Trial registration UMIN, UMIN000033007. Registered 16 jun 2018, https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000037631.

Three-dimensional assessment of image distortion induced by active cardiac implants in 3.0T CMR.

CMR at 3.0T in the presence of active cardiac implants remains a challenge due to susceptibility artifacts. Beyond a signal void that cancels image information, magnetic field inhomogeneities may cause distorted appearances of anatomical structures. Understanding influencing factors and the extent of distortion are a first step towards optimizing the image quality of CMR with active implants at 3.0T. All measurements were obtained at a clinical 3.0T scanner. An in-house designed phantom with a 3D cartesian grid of water filled spheres was used to analyze the distortion caused by four representative active cardiac devices (cardiac loop recorder, pacemaker, 2 ICDs). For imaging a gradient echo (3D-TFE) sequence and a turbo spin echo (2D-TSE) sequence were used. The work defines metrics to quantify the different features of distortion such as changes in size, location and signal intensity. It introduces a specialized segmentation technique based on a reaction-diffusion-equation. The distortion features are dependent on the amount of magnetic material in the active implants and showed a significant increase when measured with the 3D TFE compared to the 2D TSE. This work presents a quantitative approach for the evaluation of image distortion at 3.0T caused by active cardiac implants and serves as foundation for both further optimization of sequences and devices but also for planning of imaging procedures.

Distinguishing motion artifacts during optical fiber-based in-vivo hemodynamics recordings from brain regions of freely moving rodents.

Significance: Motion artifacts in the signals recorded during optical fiber-based measurements can lead to misinterpretation of data. In this work, we address this problem during in-vivo rodent experiments and develop a motion artifacts correction (MAC) algorithm for single-fiber system (SFS) hemodynamics measurements from the brains of rodents. Aim: (i) To distinguish the effect of motion artifacts in the SFS signals. (ii) Develop a MAC algorithm by combining information from the experiments and simulations and validate it. Approach: Monte-Carlo (MC) simulations were performed across 450 to 790 nm to identify wavelengths where the reflectance is least sensitive to blood absorption-based changes. This wavelength region is then used to develop a quantitative metric to measure motion artifacts, termed the dissimilarity metric (DM). We used MC simulations to mimic artifacts seen during experiments. Further, we developed a mathematical model describing light intensity at various optical interfaces. Finally, an MAC algorithm was formulated and validated using simulation and experimental data. Results: We found that the 670 to 680 nm wavelength region is relatively less sensitive to blood absorption. The standard deviation of DM (σDM) can measure the relative magnitude of motion artifacts in the SFS signals. The artifacts cause rapid shifts in the reflectance data that can be modeled as transmission changes in the optical lightpath. The changes observed during the experiment were found to be in agreement to those obtained from MC simulations. The mathematical model developed to model transmission changes to represent motion artifacts was extended to an MAC algorithm. The MAC algorithm was validated using simulations and experimental data. Conclusions: We distinguished motion artifacts from SFS signals during in vivo hemodynamic monitoring experiments. From simulation and experimental data, we showed that motion artifacts can be modeled as transmission changes. The developed MAC algorithm was shown to minimize artifactual variations in both simulation and experimental data.

Image Quality Assessment Tool for Conventional and Dynamic Magnetic Resonance Imaging Acquisitions.

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification.

Review of Image Quality Assessment Methods for Compressed Images.

The compression of images for efficient storage and transmission is crucial in handling large data volumes. Lossy image compression reduces storage needs but introduces perceptible distortions affected by content, compression levels, and display environments. Each compression method generates specific visual anomalies like blocking, blurring, or color shifts. Standardizing efficient lossy compression necessitates evaluating perceptual quality. Objective measurements offer speed and cost efficiency, while subjective assessments, despite their cost and time implications, remain the gold standard. This paper delves into essential research queries to achieve visually lossless images. The paper describes the influence of compression on image quality, appropriate objective image quality metrics (IQMs), and the effectiveness of subjective assessment methods. It also provides an overview of the existing literature, surveys, and subjective and objective image quality assessment (IQA) methods. Our aim is to offer insights, identify challenges in existing methodologies, and assist researchers in selecting the most effective assessment approach for their needs.

Unusual Artifacts Encountered during Routine Studies on Positron Emission Tomography-Computed Tomography and Gamma Camera.

Artifacts in nuclear medicine imaging are not uncommon. We are aware of some of these, for which we follow necessary protocols to avoid them. However, there are some unusual and unavoidable artifacts that we come across in daily imaging, which may be of concern and need to be detected and corrected on time. Hence, sharing a few such unusual artifacts we encountered while performing routine studies on positron emission tomography-computed tomography and gamma cameras, evaluating the cause and possible precautions.

Arterial transit artifacts and carotid Plaque-RADS may predict symptoms in patients with carotid stenosis.

AIM: To analyze the correlation of carotid stenosis severity, the Plaque Reporting and Data System (RADS) score, arterial transit artifacts (ATAs), and cerebral blood flow (CBF) with clinical cerebral ischemic symptoms in patients with carotid artery stenosis (CAS). MATERIALS AND METHODS: Sixty-one patients with unilateral internal carotid artery stenosis or occlusion (≥50% stenosis) diagnosed by ultrasound, Computed Tomography(CT) angiography, or Magnetic Resonance(MR) angiography in Yichang City Central People's Hospital from January 2022 to February 2024 were retrospectively enrolled and divided into two groups according to the presence or absence of symptoms. Both groups underwent MR plaque imaging and arterial spin labeling (ASL)-based 3.0 T MRI to compare the differences in stenosis degree, Plaque-RADS score, ATA grade, and CBF between the two groups. Binary regression analysis was used to identify the parameters with statistically significant differences between the two groups and to evaluate their diagnostic efficacy using the area under the workup curve of the subjects. RESULTS: The Plaque-RADS score, ATA grade, and CBF differences in the anterior cerebral artery(ACA)blood supply region were correlated with symptoms, and the areas under the ROC curves for the CBF differences in the ACA blood supply region, Plaque-RADS score, ATA grade and a joint model that combines all three to predict symptoms in CAS patients were 0.672, 0.796, 0.788 and 0.919, respectively. CONCLUSIONS: CBF, Plaque-RADS and ATAs were identified as independent risk factors for symptoms in patients with CAS and have a certain predictive value for symptoms, and the combined predictive value is greater, potentially providing a more effective imaging modality for clinical treatment and evaluation.

Personalized Detection of Motion Artifacts for Telemonitoring Applications.

Among its main benefits, telemonitoring enables personalized management of chronic diseases by means of biomarkers extracted from signals. In these applications, a thorough quality assessment is required to ensure the reliability of the monitored parameters. Motion artifacts are a common problem in recordings with wearable devices. In this work, we propose a fully automated and personalized method to detect motion artifacts in multimodal recordings devoted to the monitoring of the Cardiac Time Intervals (CTIs). The detection of motion artifacts was carried out by using template matching with a personalized template. The method yielded a balanced accuracy of 86%. Moreover, it proved effective to decrease the variability of the estimated CTIs by at least 17%. Our preliminary results show that personalized detection of motion artifacts improves the robustness of the assessment CTIs and opens to the use in wearable systems.

Flexible Sensor for Real-Time Monitoring of Motion Artifacts in Magnetic Resonance Imaging.

In recent years, magnetic resonance imaging has been widely used in the medical field. During the scan, if the human body moves, then there will be motion artifacts on the scan image, which will interfere with the diagnosis and only be found after the end of the scan sequence, resulting in a waste of manpower and resources. However, there is a lack of technology that halts scanning once motion artifacts arise. Here, we designed a real-time monitoring sensor (RMS) to dynamically perceive the movement of the human body and to pause in time when the movement exceeds a certain amplitude. The sensor has an array structure that can accurately sense the position of the human body in real time. The selection of the RMS ensures that there is no additional interference with the scanning results. Based on this design, the RMS can achieve the monitoring function of motion artifact generation.

Improving image quality of sparse-view lung tumor CT images with U-Net.

BACKGROUND: We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence. METHODS: CT images from 41 subjects aged 62.8 ± 10.6 years (mean ± standard deviation, 23 men), 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used. RESULTS: The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images. CONCLUSIONS: Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level. RELEVANCE STATEMENT: Our reader study demonstrates the benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose. KEY POINTS: • Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists' confidence in diagnosing lung metastasis.

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography.

OBJECTIVE: To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems. METHOD: A phantom with two iodine inserts (1 and 2 mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4 cm for first generation and 8 cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50  versus 150 keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported. RESULTS: An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2 cm). With increased collimation (4 cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8 cm collimation width was comparable to a 4 cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45  and 0.33 mg/mL for the first and second-generation DLCT, respectively). CONCLUSION: The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

Photon-counting detector computed tomography for metal artifact reduction: a comparative study of different artifact reduction techniques in patients with orthopedic implants.

PURPOSE: Artifacts caused by metallic implants remain a challenge in computed tomography (CT). We investigated the impact of photon-counting detector computed tomography (PCD-CT) for artifact reduction in patients with orthopedic implants with respect to image quality and diagnostic confidence using different artifact reduction approaches. MATERIAL AND METHODS: In this prospective study, consecutive patients with orthopedic implants underwent PCD-CT imaging of the implant area. Four series were reconstructed for each patient (clinical standard reconstruction [PCD-CTStd], monoenergetic images at 140 keV [PCD-CT140keV], iterative metal artifact reduction (iMAR) corrected [PCD-CTiMAR], combination of iMAR and 140 keV monoenergetic [PCD-CT140keV+iMAR]). Subsequently, three radiologists evaluated the reconstructions in a random and blinded manner for image quality, artifact severity, anatomy delineation (adjacent and distant), and diagnostic confidence using a 5-point Likert scale (5 = excellent). In addition, the coefficient of variation [CV] and the relative quantitative artifact reduction potential were obtained as objective measures. RESULTS: We enrolled 39 patients with a mean age of 67.3 ± 13.2 years (51%; n = 20 male) and a mean BMI of 26.1 ± 4 kg/m2. All image quality measures and diagnostic confidence were significantly higher for the iMAR vs. non-iMAR reconstructions (all p < 0.001). No significant effect of the different artifact reduction approaches on CV was observed (p = 0.26). The quantitative analysis indicated the most effective artifact reduction for the iMAR reconstructions, which was higher than PCD-CT140keV (p < 0.001). CONCLUSION: PCD-CT allows for effective metal artifact reduction in patients with orthopedic implants, resulting in superior image quality and diagnostic confidence with the potential to improve patient management and clinical decision making.

Deep learning techniques to detect rail indications from ultrasonic data for automated rail monitoring and maintenance.

The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.