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Objectives: To assess the ability of four-dimensional (4D) flow MRI to measure hepatic arterial hemodynamics by determining the effects of spatial resolution and respiratory motion suppression in vitro and its applicability in vivo with comparison to two-dimensional (2D) phase-contrast MRI. Methods: A dynamic hepatic artery phantom and 20 consecutive volunteers were scanned. The accuracies of Cartesian 4D flow sequences with k-space reordering and navigator gating at four spatial resolutions (0.5- to 1-mm isotropic) and navigator acceptance windows (± 8 to ± 2 mm) and one 2D phase-contrast sequence (0.5-mm in -plane) were assessed in vitro at 3 T. Two sequences centered on gastroduodenal and hepatic artery branches were assessed in vivo for intra - and interobserver agreement and compared to 2D phase-contrast. Results: In vitro, higher spatial resolution led to a greater decrease in error than narrower navigator window (30.5 to −4.67% vs −6.64 to −4.67% for flow). In vivo, hepatic and gastroduodenal arteries were more often visualized with the higher resolution sequence (90 vs 71%). Despite similar interobserver agreement (κ = 0.660 and 0.704), the higher resolution sequence had lower variability for area (CV = 20.04 vs 30.67%), flow (CV = 34.92 vs 51.99%), and average velocity (CV = 26.47 vs 44.76%). 4D flow had lower differences between inflow and outflow at the hepatic artery bifurcation (11.03 ± 5.05% and 15.69 ± 6.14%) than 2D phase-contrast (28.77 ± 21.01%). Conclusion: High-resolution 4D flow can assess hepatic artery anatomy and hemodynamics with improved accuracy, greater vessel visibility, better interobserver reliability, and internal consistency. Key Points: • Motion-suppressed Cartesian four-dimensional (4D) flow MRI with higher spatial resolution provides more accurate measurements even when accepted respiratory motion exceeds voxel size. • 4D flow MRI with higher spatial resolution provides substantial interobserver agreement for visualization of hepatic artery branches. • Lower peak and average velocities and a trend toward better internal consistency were observed with 4D flow MRI as compared to 2D phase-contrast. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate at which thickness marginal bone becomes visible to the observer on cone-beam computed tomography (CBCT) images and how reconstruction technique and viewing mode affect assessment., Materials and Methods: Fourteen anterior teeth from six human mandibles were examined with two CBCT resolution protocols: standard- and high-resolution. Distance from the cementoenamel junction to the visible marginal bone level (MBL) was measured in three groups of reconstructed CBCT images: multiplanar reformation (MPR) with grey scale, MPR with inverted grey scale, and 3D rendering. These measurements were used to identify the bone level where marginal bone width should be measured on histological photographs of sliced teeth. Gold standards comprised measurements of bone thickness at the superior MBL on histological photographs., Results: MPR grey scale images exposed at high-resolution settings yielded highest validity: bone widths of 0.173 mm (buccal) and 0.356 mm (lingual) were necessary for visibility on a CBCT image. 3D-rendered lingual surfaces exposed with high-resolution settings had lowest validity. Intra-observer agreement for all CBCT and histological measurements was high., Conclusion: The best CBCT resolution protocol, reconstruction technique, and viewing mode for analyzing buccal and lingual surfaces of the alveolar bone margin are images exposed with a high-resolution protocol, reconstructed using MPR, and viewed in grey scale. Bone thickness required to be visualized was twice lingually compared to buccally., Clinical Relevance: The visualization of bone thickness in CBCT requires a greater thickness on the lingual side compared to the buccal side. 3D-rendered reconstructions should be avoided when evaluating thin bony structures., Competing Interests: Declarations Ethical approval This study was approved by The Ethics Review Committee at Uppsala (Daybook no. [Dnr]: 2019 − 01582). The study was carried out according to guidelines and regulations of the 1964 Declaration of Helsinki. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background and Purpose: Brainomix e-Stroke is an artificial intelligence-based decision support tool that aids the interpretation of CT imaging in the context of acute stroke. While e-Stroke has the potential to improve the speed and accuracy of diagnosis, real-world validation is essential. The aim of this study was to prospectively evaluate the performance of Brainomix e-Stroke in an unselected cohort of patients with suspected acute ischaemic stroke., Methods: The study cohort included all patients admitted to the University College London Hospital Hyperacute Stroke Unit between October 2021 and April 2022. For e-ASPECTS and e-CTA, the ground truth was determined by a neuroradiologist with access to all clinical and imaging data. For e-CTP, the values of the core infarct and ischaemic penumbra were compared with those derived from syngo.via, an alternate software used at our institution., Results: 1163 studies were performed in 551 patients admitted during the study period. Of these, 1130 (97.2%) were successfully processed by e-Stroke in an average of 4 min. For identifying acute middle cerebral artery territory ischaemia, e-ASPECTS had an accuracy of 77.0% and was more specific (83.5%) than sensitive (58.6%). The accuracy for identifying hyperdense thrombus was lower (69.1%), which was mainly due to many false positives (positive predictive value of 22.9%). Identification of acute haemorrhage was highly accurate (97.8%) with a sensitivity of 100% and a specificity of 97.6%; false positives were typically caused by areas of calcification. The accuracy of e-CTA for large vessel occlusions was 91.5%. The core infarct and ischaemic penumbra volumes provided by e-CTP strongly correlated with those provided by syngo.via (ρ=0.804-0.979)., Conclusion: Brainomix e-Stroke software provides rapid and reliable analysis of CT imaging in the acute stroke setting although, in line with the manufacturer's guidance, it should be used as an adjunct to expert interpretation rather than a standalone decision-making tool., Competing Interests: Competing interests: DHM has received consultancy fees from Brainomix for work related to algorithm development., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Coronary computed tomography angiography (CCTA) enables detailed quantification and characterization of coronary atherosclerotic plaques, offering diagnostic and prognostic value. Interscan reproducibility studies on plaque volume measurements are limited. This study aims to assess the interscan reproducibility of coronary plaque quantification and the implications of clinical and technical characteristics on interscan reproducibility., Methods: CCTA was performed twice in 101 patients with known coronary artery disease at a 1-h interval. The scans were conducted using identical CCTA acquisition protocols. Coronary plaque volumes were quantified using a semi-automated software and performed on a per-lesion, per-vessel, and per-patient level., Results: Median plaque volumes were comparable between the first and second CCTA scan. Interscan correlation was high for total plaque (TP), non-calcified plaque (NCP), and calcified plaque (CP) across all analyses (Pearson's coefficient 0.93-0.99), but lower for low-density non-calcified plaque (LD-NCP) volume measurements (Pearson's coefficient 0.74-0.77). Bland-Altman analyses demonstrated higher interscan agreement on a per-patient level compared to on per-vessel and per-lesion level. Interscan reproducibility on CP volumes was affected by CT image quality with narrower LoA in scans with the highest image quality score (p ​= ​0.003), or lowest image reconstructive iteration level (p ​< ​0.001). Limits of agreement were significantly narrower for TP, NCP, and CP volumes in LAD-lesions and vessels compared to non-LAD lesions and vessels (p ​≤ ​0.001)., Conclusion: Overall reproducibility of repeated CCTA derived plaque measurements by a semi-automated software was modest, and was influenced by image quality, image reconstruction settings, and lesion location., Competing Interests: Declaration of competing interest Bjarne L. Nørgaard has received unrestricted research grants from the Novo Nordic Research Foundation. Damini Dey received software royalties from Cedars-Sinai Medical Center and has a patent. Damini Dey is funded by National Heart, Lung, and Blood Institute, USA (1R01HL148787–01A1 and R01HL151266). The remaining authors had no disclosures to declare., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (LAA) hypercoagulability in patients with atrial fibrillation (AF), which means LAA thrombus/sludge, dense spontaneous echo contrast and slow LAA blood flow velocity (< 0.25 m/s). Based on machine learning algorithms, cardiac computed tomography angiography (CCTA) radiomics features were adopted to construct prediction models and explore a suitable approach for diagnosing LAA hypercoagulability and adjusting anticoagulation. This study included 652 patients with non-valvular AF. The univariate analysis were used to select meaningful clinical characteristics to predict LAA hypercoagulability. Then 3D Slicer software was adopted to extract radiomics features from CCTA imaging. The radiomics score was calculated using the least absolute shrinkage and selection operator logistic regression analysis to predict LAA hypercoagulability. We then combined clinical characteristics and radiomics scores to construct a nomogram model. Finally, we got prediction models based on machine learning algorithms and logistic regression separately. The area under the receiver operating characteristic curve of radiomics score was 0.8449 in the training set and 0.7998 in the validation set. The nomogram model had a concordance index of 0.838. The final machine-learning based prediction models had good performances (best f1 score = 0.85). Radiomics features of long maximum diameter and high uniformity of Hounsfield unit in left atrial were significant predictors of the hypercoagulable state in LAA, with better predictive efficacy than clinical characteristics. Our combined models based on machine learning were reliable for hypercoagulable state screening and anticoagulation adjustment., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Objective: The aim of this study was to develop a cone beam computed tomography (CBCT) radiomics-based model that differentiates between conventional and unicystic ameloblastoma (AB)., Methods: In this retrospective study, CBCT images were collected from 100 patients who had ABs that were diagnosed histopathologically as conventional or unicystic AB after surgical treatment. The patients were randomly divided into training (70) and validation (30) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into 5 models: Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, Random Forest, and XGBoost for prediction of tumor type. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA)., Results: The 20 optimal radiomics features were incorporated into the Logistic Regression (LR) model, which exhibited the best overall performance with AUC = 0.936 (95% confidence interval [CI] = 0.877-0.996) for the training cohort and AUC = 0.929 (95% CI = 0.832-1.000) for the validation cohort. The nomogram combined the clinical features and the radiomics signature and resulted in the best predictive performance., Conclusions: The LR model demonstrated the ability of radiomics and the nomogram to distinguish between the 2 types of AB and may have the potential to replace biopsies under noninvasive conditions., Competing Interests: DECLARATION OF INTEREST None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Early prediction of hematoma expansion (HE) is important for the development of therapeutic strategies for spontaneous intracerebral hemorrhage (sICH). Radiomics can help to predict early hematoma expansion in intracerebral hemorrhage. However, complex image processing procedures, especially hematoma segmentation, are time-consuming and dependent on assessor experience. We provide a fully automated hematoma segmentation method, and construct a hybrid predictive model for risk stratification of hematoma expansion., Purpose: To propose an automatic approach for predicting early hemorrhage expansion after spontaneous intracerebral hemorrhage using deep-learning and radiomics methods., Methods: A total of 258 patients with sICH were retrospectively enrolled for model construction and internal validation, while another two cohorts (n=87, 149) were employed for independent validation. For hemorrhage segmentation, an iterative segmentation procedure was performed to delineate the area using an nnU-Net framework. Radiomics models of intra-hemorrhage and multiscale peri-hemorrhage were established and evaluated, and the best discriminative-scale peri-hemorrhage radiomics model was selected for further analysis. Combining clinical factors and intra- and peri-hemorrhage radiomics signatures, a hybrid nomogram was constructed for the early HE prediction using multivariate logistic regression. For model validation, the receiver operating characteristic (ROC) curve analyses and DeLong test were used to evaluate the performances of the constructed models, and the calibration curve and decision curve analysis were performed for clinical application., Results: Our iterative auto-segmentation model showed satisfactory results for hematoma segmentation in all four cohorts. The Dice similarity coefficient of this hematoma segmentation model reached 0.90, showing an expert-level accuracy in hematoma segmentation. The consumed time of the efficient delineation was significantly decreased, from 18 min to less than 2 min, with the assistance of the auto-segmentation model. The radiomics model of 2-mm peri-hemorrhage had a preferable area under ROC curve (AUC) of 0.840 (95 % confidence interval [CI]: 0.768, 0.912) compared with the original (0-mm dilatation) model with an AUC of 0.796 (95 % CI: 0.717, 0.875). The clinical-radiomics hybrid model showed better performances for HE prediction, with AUC of 0.853, 0.852, 0.772, and 0.818 in the training, internal validation, and independent validation cohorts 1 and 2, respectively., Conclusions: The fully automatic clinical-radiomics model based on deep learning and radiomics exhibits a good ability for hematoma segmentation and a favorable performance in stratifying HE risks., Competing Interests: Declaration of competing interest All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Donor vessel fractional flow reserve (FFR) usually increases following successful chronic total occlusion (CTO) percutaneous coronary intervention, as documented by pressure wires. In this case, donor vessel physiology changes were assessed using FFR derived from coronary computed tomography angiography (CCTA) and an artificial Intelligence-guided quantitative CCTA ischemia model in combination with pressure wire-based FFR., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Objectives: Despite successful recanalization after Mechanical Thrombectomy (MT), approximately 25 % of patients with Acute Ischemic Stroke (AIS) due to Large Vessel Occlusion (LVO) show unfavorable clinical outcomes, namely Futile Recanalization (FR). We aimed to use a Machine Learning (ML) Non-Contrast brain CT (NCCT) imaging predictive model to identify FR in patients undergoing MT., Materials & Methods: Between July 2022 and December 2022, 70 consecutive patients with LVO undergoing a complete recanalization (eTICI 3) with MT within 8 h from onset at our Centre were analyzed. Two NCCT images per patient of middle cerebral artery vascular territory and patients' clinical characteristics were classified by the presence of ischemic features on 24 h NCCT after MT. Each slice was segmented with "Mazda" software ver.4.6 by placing a Region Of Interest (ROI) on the whole brain by two radiologists in consensus. A total of 381 features were extracted for each slice. The dataset was split into train and test set with a 70:30 ratio., Results: Eleven classification models were trained. An Ensemble Machine Learning (EML) model was obtained by averaging the predictions of models with accuracy on a test set >70 %, with and without patients' clinical characteristics. The EML model combined with clinical data showed an accuracy of 0.76, a sensitivity of 0.88, a specificity of 0.69 with a NPV of 0.90, a PPV of 0.64, with AUC of 0.84., Conclusion: NCCT and ML analysis shows promise in predicting FR after complete recanalization following MT in AIS patients. Larger studies are required to confirm these preliminary results., Competing Interests: Declaration of competing interest The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (Copyright © 2024 Elsevier Inc. All rights reserved.)

To preliminarily verify the feasibility of a deep-learning (DL) artificial intelligence (AI) model to localize pulmonary embolism (PE) on unenhanced chest-CT by comparison with pulmonary artery (PA) CT angiography (CTA). In a monocentric study, we retrospectively reviewed 99 oncological patients (median age in years: 64 (range: 28-92 years); percentage of female: 39.4%) who received unenhanced and contrast-enhanced chest CT examinations in one session between January 2020 and October 2022 and who were diagnosed incidentally with PE. Findings in the unenhanced images were correlated with the contrast-enhanced images, which were considered the gold standard for central, segmental and subsegmental PE. The new algorithm was trained and tested based on the 99 unenhanced chest-CT image data sets. Based on them, candidate boxes, which were output by the model, were post-processed by evaluating whether the predicted box intersects with the patient's lung segmentation at any position. The AI-based algorithm proved to have an overall sensitivity of 54.5% for central, of 81.9% for segmental and 80.0% for subsegmental PE if taking n = 20 candidate boxes into account. Depending on the localization of the pulmonary embolism, the detection rate for only one box was: 18.1% central, 34.7% segmental and 0.0% subsegmental. The median volume of the clots differed significantly between the three subgroups and was 846.5 mm 3 (IQR:591.1-964.8) in central, 201.3 mm 3 (IQR:98.3-390.9) in segmental and 110.6 mm 3 (IQR:94.3-128.0) in subsegmental PA (p < 0.05). The new algorithm proved to have high sensitivity in detecting PE in particular in segmental/subsegmental localization and may guide to decide whether a second contrast enhanced CT is necessary., Competing Interests: Declarations Competing interests Linda Vorberg, Florian Thamm and Hendrik Ditt are/were employees of Siemens Healthcare., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: CA is founder, shareholder and director of Caristo Diagnostics Ltd, a CT-image analysis company. CA is the inventor of patents US10695023B2, US11393137B2, GB2018/1818049.7, GR20180100490, and GR20180100510, these are licensed to Caristo Diagnostics. CA has a leadership role in British Atherosclerosis Society and participates in several European commission Marie Curie Panels, and has received honoraria from Amarin, Covance and Slience Therapeutics., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Paravalvular leakage (PVL) and conduction disorders that require permanent pacemaker implantation (PPI) remain clinically relevant challenges after transcatheter aortic valve implantation (TAVI). Computed tomography-based simulations may predict the risk of significant PVL and PPI., Aims: To evaluate the feasibility and accuracy of preprocedural computer simulation with FEops HEARTguide™ to predict >trace PVL and PPI after TAVI with the self-expanding supra-annular ACURATE Neo2 transcatheter heart valve., Methods: Prospective multicenter observational study that included consecutive patients undergoing TAVI with an ACURATE Neo2 valve. Computer simulations were performed before the TAVI procedure as part of the preprocedural planning. Follow-up period for PPI and PVL was 30 days., Results: Sixty-five patients were included (median age 81 years (25th-75th percentile 77-84.5)). New left bundle branch block occurred in five patients (7.7%) and PPI in two patients (3%). Contact pressure index (CPI) was similar for patients with vs without new conduction disorders. Patients with PPI had numerically higher CPI than those without PPI (median CPI 20.0% (25th-75th percentile 15.0-25.0) vs. 13.0% (25th-75th percentile 5.5-18), p = 0.27). More than trace PVL occurred in 30%. Median PVL was significantly lower in patients with none-trace PVL (3.2 mL/s [25th-75th percentile 2.2-5.0]), compared to mild PVL (5.2 mL/s [25th-75th percentile 3.2-10.3]) and moderate PVL (12.6 mL/s [25th-75th percentile 3.9-21.3])(p = 0.036). A simulated PVL-cutoff of 9.65 mL/s identified patients with >trace PVL (AUC 0.70 (95% CI 0.55-0.85), sensitivity 42%, specificity 95%)., Conclusion: In our study FEops HEARTguide™ simulations identified patients at risk for >trace PVL with ACURATE Neo2 TAVI but not for PPI., (© 2024 The Author(s). Catheterization and Cardiovascular Interventions published by Wiley Periodicals LLC.)

Background: Selecting intervention strategies for renal artery aneurysms (RAAs) is challenging especially for those located at the vessel bifurcation. The relationship between the aneurysm and renal branches could not always be accurately viewed from traditional computed tomography angiography (CTA) images., Case Presentation: This study proposed a new method to investigate the anatomy and affected vessel branches of RAAs using automated software. Two patients with RAAs located at the renal artery bifurcation underwent Cone beam CTA (CBCTA) analysis. We sequentially coupled the "two-click AVA" function of Vessel IQ Xpress (GE Healthcare) with the "vascular tree extraction" function from FlightPlan for Embolization (GE Healthcare) to evaluate the relationship among the main renal artery, vessel branches, and aneurysms. The results showed that one patient had 1 out of 3 branches affected by the aneurysm, whereas the other's branches were all affected. Endovascular repair and open surgery were performed respectively based on the image analysis. Both patients recovered well at follow-up examination., Conclusions: Based on CBCTA analysis, the combination use of the "two-click AVA" function of VesselIQ Xpress and FlightPlan for Embolization software could assist in aneurysm assessment and intervention choices for RAAs., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

To validate the accuracy of coronary artery calcium score (CACS) using photon-counting detector (PCD) CT under various scanning settings and explore the optimized scanning settings considering both the accuracy and the radiation dose. A CACS phantom containing six hollow cylindrical hydroxyapatite calcifications of two sizes with three densities and 12 patients underwent CACS scans. For PCD-CT, two scanning modes (sequence and flash [high-pitch spiral mode]) and five tube voltages (90kV, 120kV, 140kV, Sn100kV, and Sn140kV) at different image quality (IQ) levels were set for phantom, and patients were scanned with 120kV at IQ19 using flash mode. All acquisitions from PCD-CT were reconstructed at 70keV. Acquisitions in sequence mode at 120kV on an energy-integrating detector CT (EID-CT) was used as the reference. Agatston, mass, and volume scores were calculated. The CACS from PCD-CT exhibited excellent agreements with the reference (all intraclass correlation coefficient [ICC] > 0.99). The root mean square error (RMSE) between the Agatston score acquired from PCD-CT and the reference (5.4-11.5) was small. A radiation dose reduction (16-75%) from PCD-CT compared with the reference was obtained in all protocols using flash mode, albeit with IQ20 only at sequence mode (22-44%). For the patients, ICC ( all ICC > 0.98) and Bland-Altman analysis of CACS all showed high agreements between PCD-CT and the reference, without reclassifying CACS categories(P = 0.317). PCD-CT yields repeatable and accurate CACS across diverse scanning protocols according to our pilot study. Sn100kV, 90kV, and 120kV using flash mode at IQ20 are recommended for clinical applications considering both accuracy and radiation dose., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Purpose:  Preoperative evaluation of pulmonary vascular and tracheal routes and variations is of great importance to the surgeon. Three-dimensional computed tomography bronchography and angiography (3D-CTBA) has evolved in recent years with the optimization of 3D reconstruction techniques and artificial intelligence. We aim to apply CT angiography and Exoview 3D reconstruction technology to assess patients' pulmonary arterial tree and its anatomical variants and to try to summarize a set of anatomical typing of the pulmonary arterial tree that is relatively easy and conducive to promoting teaching based on surgical habits of lobectomy., Methods:  A total of 358 patients hospitalized in the Department of Thoracic Surgery of the First Affiliated Hospital of Soochow University between July 2020 and August 2021 were included in this study. We carefully analyzed the site of emanation, alignment, and number of branches of the pulmonary artery according to a uniform classification method in conjunction with the two-dimensional CT images and transformed them into 3D reconstruction models., Results:  Different types of pulmonary artery were observed in 358 cases. We evaluated the complete pulmonary artery tree and counted the number and frequency of major arteries of the pulmonary based on the surgical habits of anatomical lobectomy., Conclusion:  The 3D-CTBA technique enables us to adequately assess the anatomy of the pulmonary arteries. Moreover, we provide a practical classification scheme of pulmonary arterial anatomical patterns based on lobectomy and 3D-CTBA. Our data can be used by clinicians in the teaching of pulmonary artery anatomy and the preoperative preparation for anatomical lobectomy., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

To investigate the effect of heart rate and virtual monoenergetic image (VMI) on coronary stent imaging in dual-source photon-counting detector computed tomography (PCD-CT). A dynamic cardiac phantom was used to vary the heart rate at 50 beats per minute (bpm), 70 bpm, and 90 bpm. Five types of stents (4.0 mm, 3.5 mm, 3.0 mm, 2.75 mm, and 2.5 mm diameter) were scanned at three different locations and reconstructed VMI at 70 keV. In addition, 50% stenosis was assessed for 3.0 mm and 4.0 mm stents. To assess in-stent stenosis, 40 keV, 70 keV, and 100 keV images were compared. Measurable lumen and contrast to noise ratio (CNR) from lumen to stenosis were evaluated quantitatively. A-4-point scale was used for the qualitative image quality of in-stent stenosis. The measurable lumen had no significant differences among heart rates in patent stents (p = 0.55). In-stent stenosis, the residual lumen was significantly larger in 40 keV [27.5% (20.8-32.3%)] than in 70 keV [11.5% (10.0-23.0%), p < 0.05] and 100 keV [0% (0-5.2%), p < 0.05]. The CNR was higher in 40 keV [12.5 (7.5-18.2)] than in 70 keV [5.3 (2.9-7.7), p < 0.05] and 100 keV [1.3 (0.5-2.7), p < 0.05]. The image quality was better in 40 keV (3.4 ± 0.7) than in 70 keV [(2.6 ± 0.8), p < 0.05] and 100 keV [(1.3 ± 0.4), p < 0.05]. Dual-source PCD-CT maintains a measurable lumen even at high heart rates. Adjusting the VMI can be helpful in visualizing the in-stent stenosis., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Purpose: This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiography (CTA)., Methods: A total of 147 patients with acute or subacute TBAD who underwent proximal TEVAR at a single center were retrospectively reviewed. The boundary of aorta was manually segmented, and the point clouds of each aorta were obtained. Prediction of negative aortic remodeling or reintervention was accomplished by a convolutional neural network (CNN) and a point cloud neural network (PC-NN), respectively. The discriminatory value of the established models was mainly evaluated by the area under the receiver operating characteristic curve (AUC) in the test set., Results: The mean follow-up time was 34.0 months (range: 12-108 months). During follow-up, a total of 25 (17.0%) patients were identified as having negative aortic remodeling, and 16 (10.9%) patients received reintervention. The AUC (0.876) by PC-NN for predicting negative aortic remodeling was superior to that obtained by CNN (0.612, p=0.034) and similar to the AUC by PC-NN combined with clinical features (0.884, p=0.92). As to reintervention, the AUC by PC-NN was significantly higher than that by CNN (0.805 vs 0.579; p=0.042), and AUCs by PC-NN combined with clinical features and PC-NN alone were comparable (0.836 vs 0.805; p=0.81)., Conclusion: The CTA-based deep learning algorithms may assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD., Clinical Impact: Negative aortic remodeling is the leading cause of late reintervention after proximal thoracic endovascular aortic repair (TEVAR) for Stanford type B aortic dissection (TBAD), and possesses great challenge to endovascular repair. Early recognizing high-risk patients is of supreme importance for optimizing the follow-up interval and therapy strategy. Currently, clinicians predict the prognosis of these patients based on several imaging signs, which is subjective. The computed tomography angiography-based deep learning algorithms may incorporate abundant morphological information of aorta, provide with a definite and objective output value, and finally assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background:  Nondisappearing subsolid nodules requiring follow-up are often detected during lung cancer screening, but changes in their invasiveness can be overlooked owing to slow growth. We aimed to develop a method for automatic identification of invasive tumors among subsolid nodules during multiple health checkups using radiomics technology based on low-dose computed tomography (LD-CT) and examine its effectiveness., Methods:  We examined patients who underwent LD-CT screening from 2014 to 2019 and had lung adenocarcinomas resected after 5-year follow-ups. They were categorized into the invasive or less-invasive group; the annual growth/change rate (Δ) of the nodule voxel histogram using three-dimensional CT (e.g., tumor volume, solid volume percentage, mean CT value, variance, kurtosis, skewness, and entropy) was assessed. A discriminant model was designed through multivariate regression analysis with internal validation to compare its efficacy with that of a volume doubling time of < 400 days., Results:  The study included 47 tumors (23 invasive, 24 less invasive), with no significant difference in the initial tumor volumes. Δskewness was identified as an independent predictor of invasiveness (adjusted odds ratio, 0.021; p  = 0.043), and when combined with Δvariance, it yielded high accuracy in detecting invasive lesions (88% true-positive, 80% false-positive). The detection model indicated surgery 2 years earlier than the volume doubling time, maintaining accuracy (median 3 years vs.1 year before actual surgery, p  = 0.011)., Conclusion:  LD-CT radiomics showed promising potential in ensuring timely detection and monitoring of subsolid nodules that warrant follow-up over time., Competing Interests: None declared., (Thieme. All rights reserved.)

Objectives: The objective of this study was to enhance the visibility of soft tissues on cone-beam computed tomography (CBCT) using a CycleGAN network trained on CT images., Methods: Training and evaluation of the CycleGAN were conducted using CT and CBCT images collected from Aichi Gakuin University (α facility) and Osaka Dental University (β facility). Synthesized images (sCBCT) output by the CycleGAN network were evaluated by comparing them with the original images (oCBCT) and CT images, and assessments were made using histogram analysis and human scoring of soft-tissue anatomical structures and cystic lesions., Results: The histogram analysis showed that on sCBCT, soft-tissue anatomical structures showed significant shifts in voxel intensity toward values resembling those on CT, with the mean values for all structures approaching those of CT and the specialists' visibility scores being significantly increased. However, improvement in the visibility of cystic lesions was limited., Conclusions: Image synthesis using CycleGAN significantly improved the visibility of soft tissue on CBCT, with this improvement being particularly notable from the submandibular region to the floor of the mouth. Although the effect on the visibility of cystic lesions was limited, there is potential for further improvement through refinement of the training method., (© 2024. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Background: Machine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS)-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between patients with auTBAD based on the rate of aortic growth., Methods: Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two computed tomography (CT) scans were analyzed: (1) the baseline CT angiography (CTA) at the index admission and (2) either the most recent surveillance CTA or the most recent CTA before an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase of ≥5 mm/year) and no or slow growth (diameter increase of <5 mm/year). Deidentified images were imported into an open source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using four-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study., Results: Of 59 patients identified for inclusion, rapid growth was observed in 33 patients (56%) and no or slow growth was observed in 26 patients (44%). There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (P > .05 for all). Median duration between baseline and interval CT was 1.07 years (interquartile range [IQR], 0.38-2.57). Postdischarge aortic intervention was performed in 13 patients (22%) at a mean of 1.5 ± 1.2 years, with no difference between the groups (P > .05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9%; IQR, -6.82 to 35.1) and zone 5 (13.4%; IQR, -7.78 to 37.9). There were no differences in baseline zone volumes between groups (P > .05 for all). The average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91)., Conclusions: We describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between patients with rapid growth and patients with no or slow growth., Competing Interests: Disclosures None., (Copyright © 2024 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Background: Joint-sparing resection of periarticular bone tumors can be challenging because of complex geometry. Successful reconstruction of periarticular bone defects after tumor resection is often performed with structural allografts to allow for joint preservation. However, achieving a size-matched allograft to fill the defect can be challenging because allograft sizes vary, they do not always match a patient's anatomy, and cutting the allograft to perfectly fit the defect is demanding., Questions/purposes: (1) Is there a difference in mental workload among the freehand, patient-specific instrumentation, and surgical navigation approaches? (2) Is there a difference in conformance (quantitative measure of deviation from the ideal bone graft), elapsed time during reconstruction, and qualitative assessment of goodness-of-fit of the allograft reconstruction among the approaches?, Methods: Seven surgeons used three modalities in the same order (freehand, patient-specific instrumentation, and surgical navigation) to fashion synthetic bone to reconstruct a standardized bone defect. National Aeronautics and Space Administration (NASA) mental task load index questionnaires and procedure time were captured. Cone-beam CT images of the shaped allografts were used to measure conformance (quantitative measure of deviation from the ideal bone graft) to a computer-generated ideal bone graft model. Six additional (senior) surgeons blinded to modality scored the quality of fit of the allografts into the standardized tumor defect using a 10-point Likert scale. We measured conformance using the root-mean-square metric in mm and used ANOVA for multipaired comparisons (p < 0.05 was significant)., Results: There was no difference in mental NASA total task load scores among the freehand, patient-specific instrumentation, and surgical navigation techniques. We found no difference in conformance root-mean-square values (mean ± SD) between surgical navigation (2 ± 0 mm; mean values have been rounded to whole numbers) and patient-specific instrumentation (2 ± 1 mm), but both showed a small improvement compared with the freehand approach (3 ± 1 mm). For freehand versus surgical navigation, the mean difference was 1 mm (95% confidence interval [CI] 0.5 to 1.1; p = 0.01). For freehand versus patient-specific instrumentation, the mean difference was 1 mm (95% CI -0.1 to 0.9; p = 0.02). For patient-specific instrumentation versus surgical navigation, the mean difference was 0 mm (95% CI -0.5 to 0.2; p = 0.82). In evaluating the goodness of fit of the shaped grafts, we found no clinically important difference between surgical navigation (median [IQR] 7 [6 to 8]) and patient-specific instrumentation (median 6 [5 to 7.8]), although both techniques had higher scores than the freehand technique did (median 3 [2 to 4]). For freehand versus surgical navigation, the difference of medians was 4 (p < 0.001). For freehand versus patient-specific instrumentation, the difference of medians was 3 (p < 0.001). For patient-specific instrumentation versus surgical navigation, the difference of medians was 1 (p = 0.03). The mean ± procedural times for freehand was 16 ± 10 minutes, patient-specific instrumentation was 14 ± 9 minutes, and surgical navigation techniques was 24 ± 8 minutes. We found no differences in procedure times across three shaping modalities (freehand versus patient-specific instrumentation: mean difference 2 minutes [95% CI 0 to 7]; p = 0.92; freehand versus surgical navigation: mean difference 8 minutes [95% CI 0 to 20]; p = 0.23; patient-specific instrumentation versus surgical navigation: mean difference 10 minutes [95% CI 1 to 19]; p = 0.12)., Conclusion: Based on surgical simulation to reconstruct a standardized periarticular bone defect after tumor resection, we found a possible small advantage to surgical navigation over patient-specific instrumentation based on qualitative fit, but both techniques provided slightly better conformance of the shaped graft for fit into the standardized post-tumor resection bone defect than the freehand technique did. To determine whether these differences are clinically meaningful requires further study. The surgical navigation system presented here is a product of laboratory research development, and although not ready to be widely deployed for clinical practice, it is currently being used in a research operating room setting for patient care. This new technology is associated with a learning curve, capital costs, and potential risk. The reported preliminary results are based on a preclinical synthetic bone tumor study, which is not as realistic as actual surgical scenarios., Clinical Relevance: Surgical navigation systems are an emerging technology in orthopaedic and reconstruction surgery, and understanding their capabilities and limitations is paramount for clinical practice. Given our preliminary findings in a small cohort study with one scenario of standardized synthetic periarticular bone tumor defects, future investigations should include different surgical scenarios using allograft and cadaveric specimens in a more realistic surgical setting., Competing Interests: Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Association of Bone and Joint Surgeons.)

Background: AccuFFRct (ArteryFlow Technology) is a novel noninvasive method for calculating fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA). The accuracy of AccuFFRct has not been adequately assessed., Objectives: This study sought to evaluate the diagnostic performance of AccuFFRct in detecting lesion-specific ischemia., Methods: This prospective study enrolled 339 patients with 404 vessels. CCTA-derived FFR was calculated using an on-site computational fluid dynamics-based method and compared with invasive FFR. The performance of AccuFFRct was comprehensively analyzed in all lesions and subgroups, including "gray zone" lesions, various lesion classifications, clinical presentations, stenosis severities, and lesion locations., Results: Using FFR ≤0.80 as a reference standard, the overall diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for AccuFFRct were 90.6% (95% CI: 87.3%-93.3%), 90.9% (95% CI: 85.1%-94.9%), 90.4% (95% CI: 86.1%-93.8%), 85.3% (95% CI: 79.8%-89.5%), and 94.2% (95% CI: 90.8%-96.4%), respectively. Good correlation and agreement were found between the computed AccuFFRct and measured FFR. AccuFFRct showed superior discrimination ability to CCTA (AUC: 0.93 [95% CI: 0.89-0.95] vs 0.77 [95% CI: 0.72-0.81]; P < 0.001) and quantitative coronary angiography (AUC: 0.93 [95% CI: 0.89-0.95] vs 0.89 [95% CI: 0.85-0.92]; P = 0.048) for identifying functionally significant stenosis. Notably, AccuFFRct maintained high diagnostic accuracy across the spectrum of lesion classifications, clinical presentations, stenosis severities, lesion locations, and in the "gray zone". Furthermore, in the cohort with ≥70% stenosis, AccuFFRct could significantly reduce the rate of un-necessary invasive tests (33.1% vs 6.6%; P < 0.001)., Conclusions: The study confirms the potential of AccuFFRct as a noninvasive alternative to invasive FFR for detecting ischemia in coronary artery disease and to risk stratify patients. The results highlight AccuFFRct's robust diagnostic ability across a wide range of lesion classifications, clinical presentations, stenosis severities, lesion locations, and in the "gray zone". (Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography [ACCURATE-CT]; NCT04426396)., Competing Interests: Funding Support and Author Disclosures This work was supported by Zhejiang Provincial Public Welfare Technology Research Project (grant LGF20H020012), National Natural Science Foundation of China (grant 82170332), Zhejiang Provincial Key Research and Development Plan (grants 2020C03016 and 2024C03095), and Hangzhou Leading Innovation and Entrepreneurship Team Project (grant TD2022007). Dr Koo has received institutional research grants from HeartFlow and AiMEDIC. Dr Wang serves as Editor-in-Chief for JACC: Asia. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D)., Methods: EPIDIAB is a post hoc analysis from the AngioSafe T2D study, which is a multicentric study aimed at determining the safety of antihyperglycemic drugs on retina and including patients with T2D screened for diabetic retinopathy (DR) (n = 7200) and deeply phenotyped for MVC. Patients included who had undergone cardiac CT for CAC (Coronary Artery Calcium) scoring after inclusion (n = 1253) were tested with a validated deep learning segmentation pipeline for EAT volume quantification., Results: Median age of the study population was 61 [54;67], with a majority of men (57%) a median duration of the disease 11 years [5;18] and a mean HbA1c of7.8 ± 1.4%. EAT was significantly associated with all traditional CV risk factors. EAT volume significantly increased with chronic kidney disease (CKD vs no CKD: 87.8 [63.5;118.6] vs 82.7 mL [58.8;110.8], p = 0.008), coronary artery disease (CAD vs no CAD: 112.2 [82.7;133.3] vs 83.8 mL [59.4;112.1], p = 0.0004, peripheral arterial disease (PAD vs no PAD: 107 [76.2;141] vs 84.6 mL[59.2; 114], p = 0.0005 and elevated CAC score (> 100 vs  < 100 AU: 96.8 mL [69.1;130] vs 77.9 mL [53.8;107.7], p < 0.0001). By contrast, EAT volume was neither associated with DR, nor with peripheral neuropathy. We further evidenced a subgroup of patients with high EAT volume and a null CAC score. Interestingly, this group were more likely to be composed of young women with a high BMI, a lower duration of T2D, a lower prevalence of microvascular complications, and a higher inflammatory profile., Conclusions: Fully-automated EAT volume quantification could provide useful information about the risk of both renal and macrovascular complications in T2D patients., (© 2024. The Author(s).)

Current planning of aortic and peripheral endovascular procedures is based largely on manual measurements performed from the 3-dimensional reconstruction of preoperative computed tomography scans. Assessment of device behavior inside patient anatomy is often difficult, and available tools, such as 3-dimensional-printed models, have several limitations. Digital twin (DT) technology has been used successfully in automotive and aerospace industries and applied recently to endovascular aortic aneurysm repair. Artificial intelligence allows the treatment of large amounts of data, and its use in medicine is increasing rapidly. The aim of this review was to present the current status of DTs combined with artificial intelligence for planning endovascular procedures. Patient-specific DTs of the aorta are generated from preoperative computed tomography and integrate aorta mechanical properties using finite element analysis. The same methodology is used to generate 3-dimensional models of aortic stent-grafts and simulate their deployment. Post processing of DT models is then performed to generate multiple parameters related to stent-graft oversizing and apposition. Machine learning algorithms allow parameters to be computed into a synthetic index to predict Type 1A endoleak risk. Other planning and sizing applications include custom-made fenestrated and branched stent-grafts for complex aneurysms. DT technology is also being investigated for planning peripheral endovascular procedures, such as carotid artery stenting. DT provides detailed information on endovascular device behavior. Analysis of DT-derived parameters with machine learning algorithms may improve accuracy in predicting complications, such as Type 1A endoleaks., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: JN Albertini is co-founder of PrediSurge SAS., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Competing Interests: Declaration of competing interest DL, AF – Supported by institutional grants from Amgen and Philips. DD, DSB, PJS – Software royalties from Cedars-Sinai Medical Center. LS – Institutional grants from Amgen and Philips. Site PI for Ocean(a) trial.

Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFR AI ) to computational fluid dynamics CT-derived FFR (FFR CT ) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFR AI model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFR CT and FFR measurements in this retrospective proof of concept study. FFR AI was compared with FFR CT regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR ≤ 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFR AI in predicting FFR ≤ 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFR CT were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFR AI and FFR CT (p = 0.12). FFR AI performed similarly to FFR CT for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFR AI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses., (© 2024. The Author(s).)

Background: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization., Objectives: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA)., Methods: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort., Results: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA., Conclusions: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328)., Competing Interests: Funding Support and Author Disclosures This study received funding from HeartFlow Inc. The company performed the computational fluid dynamics, and artificial intelligence quantitative coronary plaque analysis but was not involved in the study design, collection, analysis, and interpretation of data; the writing of this paper; or the decision to submit it for publication. Dr Koo has received institutional research grants from Abbott, Philips, and HeartFlow Inc. Dr Nam has received institutional research grants from Abbott and Genoss. Dr Merkely has received direct personal payments for speaker fees or studies from Abbott, AstraZeneca, Biotronik, Boehringer Ingelheim, CSL Behring, Daiichi Sankyo, DUKE Clinical Institute, Medtronic, and Novartis and institutional grants from Abbott, AstraZeneca, Biotronik, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, CSL Behring, Daiichi Sankyo, DUKE Clinical Institute, Eli Lilly, Medtronic, Novartis, Terumo, and VIFOR Pharma. Dr de Bruyne has received institutional unrestricted research grants from Abbott, Boston Scientific, and Biotronik; has received consulting fees from Abbott, Opsens, and Boston Scientific; and is a shareholder for Siemens, GE, Bayer, Philips, HeartFlow Inc, Edwards Life Sciences, Sanofi, and Omega Pharma. Dr Ko has equity in Artrya and has received honoraria from Medtronic, Abbott Vascular, and Canon Medical. Dr Leipsic is a consultant for and holds stock options in HeartFlow Inc and modest personal core lab fees from Arineta. Dr Shah has received honoraria as a speaker for HeartFlow Inc. Dr Bax has received unrestricted research grants from Abbott and Edwards Lifesciences to the Department of Cardiology, Leiden University Medical Center, the Netherlands. Dr Shaw has received honoraria from HeartFlow Inc and Elucid Imaging. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Epicardial adipose tissue (EAT) may enhance the risk of coronary artery disease (CAD). We investigated the relationship between EAT density (a maker of local inflammation) and coronary plaque characteristics in stable CAD patients. This study included 123 individuals who underwent coronary artery calcium scan and coronary CT angiography to evaluate CAD. Plaque characteristics were analyzed by semi-automated software (QAngio, Leiden, Netherlands). Non-contrast CT scans were used to measure EAT density (HU) and volume (cc) (Philips, Cleveland, OH). Multivariate regression models were used to evaluate the association of EAT density and volume with different plaque types. The mean (SD) age was 59.4±10.1 years, 53% were male, the mean (SD) EAT density was -77.2±4.6 HU and the volume was 118.5±41.2 cc. After adjustment for cardiovascular risk factors, EAT density was associated with fibrous fatty (FF) plaque (p<0.03). A 1 unit increase in HU was associated with a 7% higher FF plaque, and lower EAT density is independently associated to FF plaque. The association between EAT density and fibrous (p=0.08), and total noncalcified (p=0.09) plaque trended toward but did not reach significance. There was no association between EAT volume and any plaque type. These results suggest that inflammatory EAT may promote coronary atherosclerosis. Therefore, non-contrast cardiac CT evaluation of EAT quality can help better assess cardiovascular risk., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: Endovascular aneurysm repair (EVAR) success depends on imaging technology both in the planning and operative phases. Endovascular repair requires intravenous contrast and radiation exposure to the patient as well as radiation exposure to the operator. Recent developments in imaging technology attempt to merge preoperative imaging with intraoperative imaging to improve the efficiency and accuracy of EVAR. The Cydar 3-dimensional (3D) imaging system combines the preoperative and intraoperative imaging during the operation. We aim to investigate the use of the Cydar 3D imaging system during EVAR compared to conventional methods., Methods: Retrospective review of all patients undergoing an EVAR at a single quaternary vascular center from 2019-2023 was collected. This cohort was divided into 2 groups: (1) repair using Cydar 3D imaging or (2) repair without Cydar 3D imaging. Overall, 138 unique patients were identified with 27 operations using Cydar 3D imaging and 111 operations without Cydar 3D imaging. We performed a 1-to-1 propensity score-matched analysis using nearest-neighbor matching for variables including age, case urgency, and if the case was performed in the operative room or interventional radiology room. A match occurred when a patient in the Cydar 3D imaging group had an estimated score within 0.01 standard deviations of a patient in the control group. From this, we paired 27 from each cohort for a total of 54 patients. Demographic data included length of stay in days, contrast volume (mL), fluoroscopy time (min), procedure length (mins), mortality, and blood loss (mL). Univariate analyses were performed and a P value less than 0.05 was considered statistically significant., Results: A total of 54 vascular patients were analyzed: 27 without the Cydar 3D imaging and 27 with the Cydar 3D imaging. In the univariate analysis, there was no statistical difference in the average length of stay (6.4 days ± 11.76 vs. 4.1 ± 6.03, P = 0.372), aneurysm size (5.9 ± 1.4 vs. 5.9 ± 1.2, P = 0.88), contrast volume in mL (91.3 ± 47.0 vs. 91.1-33.49, P = 9.88), fluoroscopy time in mins (20.2 ± 17.2 vs. 19.5 ± 19.4, P = 0.89), procedure length (299.3 ± 177.9 vs. 353 ± 191.98, P = 0.279), and blood loss in mL (513.8 ± 791 vs. 353 ± 191.98, P = 0.594). There was an increase in reintervention for endoleaks in the group with use of Cydar 3D imaging (0 vs. 6, P = 0.043). A subanalysis of patients undergoing physician-modified EVARs did show a 15% reduction in the contrast volume used., Conclusions: The use of 3D imaging technology has the potential to increase the safety of EVAR to both patients and operators. In our study, we did not find any difference in standard EVARs; however, there was a contrast use decrease in physician-modified EVARs. Further studies will need to be performed to determine the realized benefit from performing EVARs using this new technology., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Backgrounds: The impact of quantitative assessment to differentiate total occlusions (TOs) from severe stenoses on coronary computed tomography angiography (CCTA) remains unknown., Objective: This study investigated whether quantitative characteristics assessed on CCTA could help differentiate a TO from a severe stenosis on invasive coronary angiography (ICA)., Methods: This study is a sub-analysis of the FASTTRACK CABG (NCT04142021) in which both CCTA and ICA were routinely performed. Quantitative analysis was performed with semi-automated CCTA plaque-analysis software. Blinded analysts compared TOs on CCTA, defined as a complete lack of contrast opacification within the coronary occlusion, with corresponding ICA., Results: Eighty-four TOs were seen on CCTA in 59 of the 114 patients enrolled in the trial. The concordance in diagnosing a TO between ICA and CCTA was 56.0% (n ​= ​47). Compared to severe stenoses, TOs had a significantly longer lesion length (25.1 ​± ​23.0 ​mm vs 9.4 ​± ​11.2 ​mm, P ​< ​0.001). The best cut-off value to differentiate a TO from severe stenosis was a lesion length of 5.5 ​mm (area under the curve 0.77, 95% CI: 0.66-0.87), with a 91.1% sensitivity and 61.1% specificity. Dense calcium percentage atheroma volume (PAV) was significantly higher in TOs compared to severe stenoses (18.7 ​± ​19.6% vs. 6.6 ​± ​13.0%, P ​< ​0.001), whilst the opposite was seen for fibro-fatty PAV (31.3 ​± ​14.2% vs. 19.5 ​± ​10.5%, P ​< ​0.001). On a multivariable logistic regression analysis, lesion length (>5.5 ​mm) was the only parameter associated with differentiating a TO from a severe stenosis., Conclusion: In quantitative CCTA analysis, a lesion length >5.5 ​mm was the only independent predictor differentiating a TO from a severe stenosis., Nct Registration Number: NCT04142021., Competing Interests: Declaration of competing interest Dr. Masuda received a grant from TERUMO corporation outside the submitted work. Dr. Miyashita received research grants from OrbusNeich Medical K.K., outside the submitted work. Dr. Tobe received a grant from the Fukuda foundation for medical technology. Dr. Scot received consultancy fees from Biosensors. Dr. P.W.S. reported consulting fees from SMT, Novartis, Meril Life, Philips, and Xeltis. All other authors have nothing to declare., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: This study investigated whether there are differences between software programs, voxel sizes, segmentation techniques, and intraoral scanners in terms of volume measurement in incisor teeth., Study Design: Thirty extracted teeth were scanned using a 3D intraoral scanner. Physical volumes were measured using the water displacement method (WDM) as the gold standard. Cone beam computed tomography images were taken at two voxel sizes (0.3-0.4 mm voxel). The volumes of the teeth were calculated using the manual segmentation technique in the 3D Doctor and ImageJ (Fiji) Program, manual and automatic segmentation methods in the ITK-Snap program, and automatic segmentation methods in the 3D Slicer program. The data were analyzed using the Statistical Package for Social Sciences (SPSS, v 20.0)., Results: There is a significant difference between WDM and 3D Doctor volume measurements at the voxel size of 0.4 mm (P < .05). The 3D Doctor program showed more than 13% difference compared to the WDM. There is no significant difference between WDM, intraoral scanner, ITK-Snap, ImageJ (Fiji), 3D Slicer, and 3D-Doctor volume measurements at the voxel size of 0.3 mm., Conclusions: Measurements with manual segmentation in ITK-Snap and ImageJ programs give the closest results to the physical volume measurements of the teeth. The automatic segmentation method in ITK-Snap and 3D Slicer programs may be preferred due to its ease of use and less time-consuming. In the 3D Doctor software, volume measurements tend to increase with larger voxel size., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: To evaluate the performance of convolutional neural networks (CNNs) for the automated detection and classification of mandibular fractures on multislice spiral computed tomography (MSCT)., Study Design: MSCT data from 361 patients with mandibular fractures were retrospectively collected. Two experienced maxillofacial surgeons annotated the images as ground truth. Fractures were detected utilizing the following models: YOLOv3, YOLOv4, Faster R-CNN, CenterNet, and YOLOv5-TRS. Fracture sites were classified by the following models: AlexNet, GoogLeNet, ResNet50, original DenseNet-121, and modified DenseNet-121. The performance was evaluated for accuracy, sensitivity, specificity, and area under the curve (AUC). AUC values were compared using the Z-test and P values <.05 were considered to be statistically significant., Results: Of all of the detection models, YOLOv5-TRS obtained the greatest mean accuracy (96.68%). Among all of the fracture subregions, body fractures were the most reliably detected (with accuracies of 88.59%-99.01%). For classification models, the AUCs for body fractures were higher than those of condyle and angle fractures, and they were all above 0.75, with the highest AUC at 0.903. Modified DenseNet-121 had the best overall classification performance with a mean AUC of 0.814., Conclusions: The modified CNN-based models demonstrated high reliability for the diagnosis of mandibular fractures on MSCT., Competing Interests: Declarations of Interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: To assess the effect of a blooming artifact reduction (BAR) filter on cone beam computed tomography (CBCT) images in the dimensional analysis of dental implants., Study Design: Six types of implants (n = 5 for each type) composed of titanium (3 types), titanium-zirconia alloy, zirconium oxide, and titanium-aluminum-vanadium alloy, and made with 2 manufacturing processes (milled and printed) were individually installed in a bovine rib block according to the manufacturer's protocol. CBCT images were acquired with i-CAT and Carestream scanners, randomized, and analyzed without and with the e-Vol DX BAR filter (60 images for each scanner). Implant length, diameter, and thread-to-thread distance were measured by two radiologists, with a stereomicroscopic image of each implant as the reference standard for calculation of distortion in measurements. Repeated measures ANOVA with Bonferroni corrections and intraclass correlation coefficients (ICC) were applied (α = 0.05)., Results: The BAR filter significantly reduced distortion in various parameters for specific implants, aligning closely with stereomicroscopic measurements. Titanium and printed implants showed reduced dimensional distortion regardless of BAR filter use. Carestream measurements presented smaller dimensional differences than i-CAT for most implants and parameters, especially without BAR (P < .05). Interexaminer reliability was good to excellent, with ICC ranging from 0.80 to 0.95., Conclusions: The BAR filter can enhance implant dimensional analysis, although variations based on implant material and manufacturing process were observed., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology., Methods: Patients with a computed tomographic angiography head/neck between 2010 and 2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each computed tomography was used to create a three-dimensional image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with area under the receiver operating characteristic curves (AUCs). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke., Results: We created 3 models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, peripheral arterial disease, hyperlipidemia, body mass index (BMI), chronic obstructive pulmonary disease (COPD), and history of transient ischemia attack (TIA)/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue (PVAT) volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, PVAT, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC., Conclusions: Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model has a 20% chance of stroke/death. Further prospective studies are needed to validate our findings., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background/Aims A risk prediction model for the development of hepatocellular carcinoma (HCC) from indeterminate nodules detected on computed tomography (CT) (RadCT score) in patients with chronic hepatitis B (CHB)-related cirrhosis was proposed. We validated this model for indeterminate nodules on magnetic resonance imaging (MRI). Methods Between 2013 and 2016, Liver Imaging Reporting and Data System (LI-RADS) 2/3 nodules on MRI were detected in 99 patients with CHB. The RadCT score was calculated. Results The median age of the 72 male and 27 female subjects was 58 years. HCC history and liver cirrhosis were found in 47 (47.5%) and 44 (44.4%) patients, respectively. The median RadCT score was 112. The patients with HCC (n=41, 41.4%) showed significantly higher RadCT scores than those without (median, 119 vs. 107; P=0.013); the Chinese university-HCC and risk estimation for HCC in CHB (REACH-B) scores were similar (both P>0.05). Arterial enhancement, T2 hyperintensity, and diffusion restriction on MRI were not significantly different in the univariate analysis (all P>0.05); only the RadCT score significantly predicted HCC (hazard ratio [HR]=1.018; P=0.007). Multivariate analysis showed HCC history was the only independent HCC predictor (HR=2.374; P=0.012). When the subjects were stratified into three risk groups based on the RadCT score (105), the cumulative HCC incidence was not significantly different among them (all P>0.05, log-rank test). Conclusions HCC history, but not RadCT score, predicted CHB-related HCC development from LI-RADS 2/3 nodules. New risk models optimized for MRI-defined indeterminate nodules are required.

Background: This study aims to identify a morphological indicator of aortic dissection (AD) based on the geometrical characteristics of the thoracic aorta., Methods: We evaluated computed tomographic angiograms of 63 samples with AD (22 with type A AD, 41 with type B AD) and 71 healthy samples. Via centerline extraction and spatial transformation, the spatial entanglement of the aorta was minimized, and the expanded 2D aortic morphology was obtained. The 2D morphology of the thoracic aorta was fit to a circle. The applicability of the fitting circle method for identifying aortic dissection was verified by multivariable logistic regression analysis., Results: Via the 3D coordinate transformation algorithm, the optimal aortic view was obtained. On this view, the geometrical characteristics of the thoracic aortas of the healthy controls were similar to a portion of a circle (sum of residuals: 3502.45 ± 2566.71, variance: 86.23 ± 56.60), while that of AD samples had poorer similarity to the circle (sum of residuals: 5404.78 ± 3891.69, variance: 129.90 ± 90.09). This difference was significant (p < 0.001). A logistic regression model showed that increased deformation of the thoracic aorta was a significant indicator of aortic dissection (odds ratio: 1.35, p = 0.034)., Conclusions: The morphology of the healthy thoracic aorta could be fit to a circle, while that of the dissected aorta had poorer similarity to the circle. The statistics of the circle are an effective indicator of aortic deformation in AD., Trial Registration: This study is registered in the Chinese Clinical Trial Registry (ChiCTR2000029219)., (© 2024. The Author(s).)

Background: Detailed visualization and precise measurements of aortic valve dimensions are critical for the success of transcatheter aortic valve implantation and for the prevention of complications. Currently, multislice computed tomography is the gold standard for assessment of the aortic annulus and surrounding structures to determine the prosthesis size. New technologies such as virtual reality (VR) not only enable 3-dimensional (3D) visualization with the potential to improve understanding of anatomy and pathology but also allow measurements in 3D. This study aims to investigate the feasibility, accuracy, and reproducibility of VR for the visualization of the aortic valve, the surrounding structures, and its role in preprocedural sizing for transcatheter aortic valve implantation., Methods and Results: Based on the preprocedural multislice computed tomography data, 3mensio measurements and 3D visualizations and measurements using VR software were performed retrospectively on 60 consecutive patients who underwent transcatheter aortic valve implantation at our heart center. There were no significant differences but strong correlations between the VR measurements compared with those performed with the 3mensio software. Furthermore, excellent or good intra- and interobserver reliability could be demonstrated for all values. In a structured questionnaire, users reported that VR simplified anatomical understanding, improved 3D comprehension of adjacent structures, and was associated with very good self-perceived depth perception., Conclusions: The use of VR for preprocedural transcatheter aortic valve implantation sizing is feasible and has precise and reproducible measurements. In addition, 3D visualization improves anatomical understanding and orientation. To evaluate the potential benefits of 3D visualization for planning further cardiovascular interventions, research in this field is needed.

Aims: Right phrenic nerve (RPN) injury is a disabling but uncommon complication of atrial fibrillation (AF) radiofrequency ablation. Pace-mapping is widely used to infer RPN's course, for limiting the risk of palsy by avoiding ablation at capture sites. However, information is lacking regarding the distance between the endocardial sites of capture and the actual anatomic RPN location. We aimed at determining the distance between endocardial sites of capture and anatomic CT location of the RPN, depending on the capture threshold., Methods and Results: In consecutive patients undergoing AF radiofrequency ablation, we defined the course of the RPN on the electroanatomical map with high-output pacing at up to 50 mA/2 ms, and assessed RPN capture threshold (RPN-t). The true anatomic course of the RPN was delineated and segmented using CT scan, then merged with the electroanatomical map. The distance between pacing sites and the RPN was assessed. In 45 patients, 1033 pacing sites were analysed. Distances from pacing sites to RPN ranged from 7.5 ± 3.0 mm (min 1) when RPN-t was ≤10 mA to 19.2 ± 6.5 mm (min 9.4) in cases of non-capture at 50 mA. A distance to the phrenic nerve > 10 mm was predicted by RPN-t with a ROC curve area of 0.846 [0.821-0.870] (P < 0.001), with Se = 80.8% and Sp = 77.5% if RPN-t > 20 mA, Se = 68.0% and Sp = 91.6% if RPN-t > 30 mA, and Se = 42.4% and Sp = 97.6% if non-capture at 50 mA., Conclusion: These data emphasize the utility of high-output pace-mapping of the RPN. Non-capture at 50 mA/2 ms demonstrated very high specificity for predicting a distance to the RPN > 10 mm, ensuring safe radiofrequency delivery., Competing Interests: Conflict of interest: F.M.: consultant for Biosense-Webster and Abbott., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

This study focuses on identifying anatomical markers with predictive capacity for long-term myocardial infarction (MI) in focal coronary artery disease (CAD). Eighty future culprit lesions (FCL) and 108 non-culprit lesions (NCL) from 80 patients underwent 3D quantitative coronary angiography. The minimum lumen area (MLA), minimum lumen ratio (MLR), and vessel fractional flow reserve (vFFR) were evaluated. MLR was defined as the ratio between MLA and the cross-sectional area at the proximal lesion edge, with lower values indicating more abrupt luminal narrowing. Significant differences were observed between FCL and NCL in MLR (0.41 vs. 0.53, p < 0.001). MLR correlated inversely with translesional vFFR (r =  - 0.26, p = 0.0004) and was the strongest predictor of MI at 5 years (AUC = 0.75). Lesions with MLR < 0.40 had a fourfold increased MI incidence at 5 years. MLR is a robust predictor of future adverse coronary events., (© 2024. The Author(s).)

Background: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs., Objectives: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCT ISCHEMIA ) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE)., Methods: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFR CT ), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCT ISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1., Results: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCT ISCHEMIA , 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFR CT , and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCT ISCHEMIA , 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFR CT , 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCT ISCHEMIA test was associated with aHR: 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE., Conclusions: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis., Competing Interests: Funding Support and Author Disclosures This project has been supported by the Foundation “De Drie Lichten” in the Netherlands. Dr Nurmohamed has received grants from the Dutch Heart Foundation (Dekker 03-007-2023-0068) and grants from the European Atherosclerosis Society (2023); and is co-founder of Lipid Tools. Mr Wang, Dr Chan, Ms Crabtree, Ms Aquino, Dr Min, and Dr Earls are employees of Cleerly Inc. Dr Choi has received grant support from GW Heart and Vascular Institute; equity in Cleerly, Inc; and has received consulting fees from Siemens Healthineers. Dr Knaapen has received research grants from HeartFlow, Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Gallbladder carcinoma (GBC) is highly malignant, and its early diagnosis remains difficult. This study aimed to develop a deep learning model based on contrast-enhanced computed tomography (CT) images to assist radiologists in identifying GBC., Methods: We retrospectively enrolled 278 patients with gallbladder lesions (> 10 mm) who underwent contrast-enhanced CT and cholecystectomy and divided them into the training (n = 194) and validation (n = 84) datasets. The deep learning model was developed based on ResNet50 network. Radiomics and clinical models were built based on support vector machine (SVM) method. We comprehensively compared the performance of deep learning, radiomics, clinical models, and three radiologists., Results: Three radiomics features including LoG&#95;3.0 gray-level size zone matrix zone variance, HHL first-order kurtosis, and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC, and were selected for developing radiomics model. Multivariate regression analysis revealed that age ≥ 65 years [odds ratios (OR) = 4.4, 95% confidence interval (CI): 2.1-9.1, P < 0.001], lesion size (OR = 2.6, 95% CI: 1.6-4.1, P < 0.001), and CA-19-9 > 37 U/mL (OR = 4.0, 95% CI: 1.6-10.0, P = 0.003) were significant clinical risk factors of GBC. The deep learning model achieved the area under the receiver operating characteristic curve (AUC) values of 0.864 (95% CI: 0.814-0.915) and 0.857 (95% CI: 0.773-0.942) in the training and validation datasets, which were comparable with radiomics, clinical models and three radiologists. The sensitivity of deep learning model was the highest both in the training [90% (95% CI: 82%-96%)] and validation [85% (95% CI: 68%-95%)] datasets., Conclusions: The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions., (Copyright © 2023 First Affiliated Hospital, Zhejiang University School of Medicine in China. Published by Elsevier B.V. All rights reserved.)

Background: Transcatheter aortic valve replacement (TAVR) is increasingly performed for the treatment of aortic stenosis. Computed tomography (CT) analysis is essential for pre-procedural planning. Currently available software packages for TAVR planning require substantial human interaction. We describe development and validation of an artificial intelligence (AI) powered software to automatically rend anatomical measurements and other information required for TAVR planning and implantation., Methods: Automated measurements from 100 CTs were compared to measurements from three expert clinicians and TAVR operators using commercially available software packages. Correlation coefficients and mean differences were calculated to assess precision and accuracy., Results: AI-generated annular measurements had excellent agreements with manual measurements by expert operators yielding correlation coefficients of 0.97 for both perimeter and area. There was no relevant bias with a mean difference of -0.07 mm and - 1.4 mm 2 for perimeter and area, respectively. For the ascending aorta measured 5 cm above the annular plane, correlation coefficient was 0.95 and mean difference was 1.4 mm. Instruction for use-based sizing yielded agreement with the effective implant size in 87-88 % of patients for self-expanding valves (perimeter-based sizing) and in 88 % for balloon-expandable valves (area-based sizing)., Conclusions: A fully automated software enables accurate and precise anatomical segmentation and measurements required for TAVR planning without human interaction and with high reliability., Competing Interests: Declaration of competing interest Relationship with industry: ST has received honoraria from Medtronic, Boston Scientific, Biosensors, Hi-D Imaging, Abbott Vascular, Medira, Shockwave, Teleflex, atHeart Medical, Cardiac Dimensions, Polares Medical, Amarin, Sanofi, AstraZeneca, ReCor Medical, Daiichi Sankyo, has received institutional research grants from Edwards Lifesciences, Abbott Vascular, Boston Scientific, Fumedica, Novartis, Boehringer Ingelheim, Polares Medical, and holds equity in Hi-D Imaging. MCWVB is a consultant for Hi-D Imaging, Boston Scientific, Medtronic Inc., and Sanamedi Inc. FM is a consultant for Hi-D Imaging. UG holds equity in Hi-D Imaging. MW is a proctor and consultant for Biosensors and holds equity in Hi-D Imaging. WK received personal fees from Abbott, Boston Scientific Edwards Lifesciences, Meril Life Sciences, is consultant to Hi-D imaging and received institutional fees from Boston Scientific., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Competing Interests: Disclosures F.N. is employed as a Research Student at Siemens Healthineers, Erlangen.

Objective: The objective of this study was to investigate the flow effects in different degrees of thoracic aortic stent graft protrusion extension by creating bird beak effect simulations using accurate 3D geometry and a realistic, nonlinear, elastic biomechanical model using computer-aided software SolidWorks., Methods: Segmentation in 3D of an aortic arch from a computed tomography (CT) scan of a real-life patient was performed using SolidWorks. A parametric analysis of three models was performed: (A) Aortic arch with no stent, (B) 3 mm bird-beak configuration, and (C) 6.5 mm bird-beak configuration. Flow velocity, pressure, vorticity, wall shear stress (WSS), and time average WSS were assessed., Results: The flow velocity in Model A remained relatively constant and low in the area of the ostium of the brachiocephalic artery and doubled in the left subclavian artery. On the contrary, Models B and C showed a decrease in velocity of 52.3 % in the left subclavian artery. Furthermore, Model B showed a drop in velocity of 82.7% below the bird-beak area, whereas Model C showed a decline of 80.9% in this area. The pressure inside the supra-aortic branches was higher in Model B and C compared with Model A. In Model A, vorticity only appeared at the level of the descending aorta, with low to non-vorticity in the aortic arch. In contrast, Models B and C had an average vorticity of 241.4 Hz within the bird beak area. Regarding WSS, Model A, and Model B shared similar WSS in the peak systolic phase, in the aortic arch, and the bird beak area, whereas Model C had an increased WSS by 5 Pa on average at these zones., Conclusion: In the present simulations' lower velocities, higher pressures, vortices, and WSS were observed around the bird beak zone, the aortic arch, and the supra-aortic vessels., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Cardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from cardiac CT and MRI images is essential for diagnosing functional abnormalities, treatment plans and cardiovascular diseases management. Accurate segmentation and quantitative assessments are still a challenge. Manual delineation of the heart from the scan images is labour-intensive, time-consuming, and error prone as it depends on the radiologist's experience. Thus, automated techniques are highly desirable as they can significantly improve the efficiency and accuracy of image analysis., Method: This work addresses the above problems. A new, image-driven, fast, and fully automatic segmentation method was developed to segment the heart from CT images using a processing pipeline of adaptive median filter, multi-level thresholding, active contours, mathematical morphology, and the knowledge of human anatomy to delineate the regions of interest., Results: The algorithm proposed is simple to implement and validate and requires no human intervention. The method is tested on the 'Image CHD' DICOM images (multi-centre, clinically approved single-phase de-identified images), and the results obtained were validated against the ground truths provided with the dataset. The results show an average Dice score, Jaccard score, and Hausdorff distance of 0.866, 0.776, and 33.29 mm, respectively, for the segmentation of the heart's chambers, aorta, and blood vessels. The results and the ground truths were compared using Bland-Altmon plots., Conclusion: The heart was correctly segmented from the CT images using the proposed method. Further this segmentation technique can be used to develop AI based solutions for segmentation., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Purpose: To investigate the relationship between the morphological structure and hemodynamic properties of the compressed iliac vein and explore the reason for the formation of thrombosis in the compressed iliac vein., Materials and Methods: A total of 11 patients with iliac vein compression syndrome (IVCS) were included in this study, and their iliac veins were reconstructed in 3 dimensions (3D). The morphological structures of the iliac veins (confluence angle, degree of stenosis) were analyzed based on the 3D model. Variations in the hemodynamic properties of the iliac vein were investigated at 4 typical moments in one cardiac cycle, and the relationship between the different morphological configurations and the pressure difference was investigated., Results: In the region of the compressed iliac vein, the blood flow velocity is accelerated and the pressure changes abruptly accompanied by the increase in pressure difference. Higher time averaged wall shear stress (TAWSS) and lower relative residence time (RRT) appeared in stenosis regions of compressed iliac vein, while TAWSS was low and RRT was large near the stenosis position. There was a strong positive correlation between the degree of stenosis and the pressure difference (r=0.894), and a positive correlation between the confluence angle of the iliac vein and the pressure difference (r=0.638)., Conclusion: The morphological structure of the compressed iliac vein has an obvious influence on the hemodynamic surroundings; the pressure difference becomes larger when the degree of stenosis and the confluence angle increase. The iliac vein luminal areas with low TAWSS and high RRT near the compressed location can impede blood flow and lead to accumulation of blood components, which may increase the risk of thrombosis formation and should be fully considered in the treatment of IVCS., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.