Search

Aims To present and validate a fully automated, deep learning (DL)-based branch-wise coronary artery calcium (CAC) scoring algorithm on a multi-centre dataset. Methods and results We retrospectively included 1171 patients referred for a CAC computed tomography examination. Total CAC scores for each case were manually evaluated by a human reader. Next, each dataset was fully automatically evaluated by the DL-based software solution with output of the total CAC score and sub-scores per coronary artery (CA) branch [right coronary artery (RCA), left main (LM), left anterior descending (LAD), and circumflex (CX)]. Three readers independently manually scored the CAC for all CA branches for 300 cases from a single centre and formed the consensus using a majority vote rule, serving as the reference standard. Established CAC cut-offs for the total Agatston score were used for risk group assignments. The performance of the algorithm was evaluated using metrics for risk class assignment based on total Agatston score, and unweighted Cohen’s Kappa for branch label assignment. The DL-based software solution yielded a class accuracy of 93% (1085/1171) with a sensitivity, specificity, and accuracy of detecting non-zero coronary calcium being 97%, 93%, and 95%. The overall accuracy of the algorithm for branch label classification was 94% (LM: 89%, LAD: 91%, CX: 93%, RCA: 100%) with a Cohen's kappa of k = 0.91. Conclusion Our results demonstrate that fully automated total and vessel-specific CAC scoring is feasible using a DL-based algorithm. There was a high agreement with the manually assessed total CAC from a multi-centre dataset and the vessel-specific scoring demonstrated consistent and reproducible results.

Background and Purpose— Therapeutic decision making for small unruptured intracranial aneurysms ( Methods— We analyzed 338 small unruptured aneurysms; 35 ruptured during the observation period, and 303 remained stable. Clinical, morphological, and hemodynamic parameters were considered. Computational fluid dynamics was used to calculate hemodynamic parameters based on computed tomography images of all aneurysms in their unruptured state. Differences between the ruptured and unruptured groups were tested by the Mann-Whitney U or Fisher exact tests. Multivariate logistic regression was applied to obtain a rupture risk model. Its predictive ability was investigated by receiver operating characteristic analysis. Results— The risk model revealed that rupture may be more likely to in younger patients (odds ratio [OR], 0.92 for each age increase of 1 year [95% CI, 0.88−0.96] P P =0.03), located at a bifurcation (OR, 5.45 [95% CI, 1.87−15.85] P =0.002), with a bleb (OR, 4.09 [95% CI, 1.42−11.79] P =0.009), larger length (OR, 1.91 for each increase of 1 mm [95% CI, 1.42−2.57] P P =0.01). The sensitivity, specificity, and area under the curve were 0.800, 0.752, and 0.826 (95% CI, 0.739−0.914) respectively. Conclusions— Younger age, presence of multiple aneurysms, location at a bifurcation, presence of a bleb, larger length, and lower pressure loss coefficient were identified as risk factors for rupture of small intracranial aneurysms. The risk model should be validated in further studies.

The measurement of coronary artery calcium scores (CACS) usually requires the manual input of a human operator to identify and mark calcified coronary lesions in each image section ([1–3][1]). Because this approach is labor intensive and time consuming, a more automated work flow is desirable to

Background Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. Methods A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen’s kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. Results Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen’s kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). Conclusion We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

Additional file 1: Table S1. Demographics of patients with and without lung nodules stratified by the AI and expert as well as expert CAC scores. Table S2. Comparison of risk factors and clinical attributes between patients with expert determined nodules, comparison of risk factors and clinical attributes between patients with AI determined nodules, and comparison of risk factors and clinical attributes between patients with CAC > 0 and CAC = 0. Table S3. Demographics and risk factors associated with pulmonary outcomes. Table S4. Demographics and risk factors associated with cardiac outcomes. Table S5. Simple logistic regression for parallel analysis of AI-volume and expert-volume for prediction of cardiac outcomes. Table S6. AUC and McFadden R2 for outcomes with and without AI components included in the model. Table S7. Summary statistics of Patients with False Positive Nodules. Figure S1. ROC curves for comparison of CAC AI-Volume and Expert-Volume for prediction of MACE. Expert and AI-Volume both excellently predict MACE. Figure S2. ROC Curves for comparison of CAC AI-Volume and Expert Volume for prediction of ACS/MI hospitalization in our study timeframe. Figure S3. ROC Curves for comparison of CAC AI-Volume and Expert Volume for prediction of percutaneous coronary intervention (coronary catheterization or stent placement) or coronary artery bypass graft operation. Figure S4. Root cause analysis of false-positive nodules. A. Logistic regression of having one false positive nodule as predicted by age. B. Logistic regression probability curve of false positive nodules as a function of age. C. True anatomic identities and relative frequencies of false positive nodule etiologies.

Purpose: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT.Methods: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications.Results: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R-2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R-2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category.Conclusion: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions.

Research on implementation of artificial intelligence (AI) in radiology workflows and its impact on reports remains scarce. In this study, we aim to assess if an AI platform would perform better than clinical radiology reports in evaluating noncontrast chest computed tomography (CT) scans.Consecutive patients who had undergone noncontrast chest CT were retrospectively identified. The radiology reports were reviewed in a binary fashion for reporting of pulmonary lesions, pulmonary emphysema, aortic dilatation, coronary artery calcifications (CAC), and vertebral compression fractures (VCF). CT scans were then processed using an AI platform. The reports' findings and the AI results were subsequently compared to a consensus read by two board-certificated radiologists as reference.A total of 100 patients (mean age: 64.2 ± 14.8 years; 57% males) were included in this study. Aortic segmentation and calcium quantification failed to be processed by AI in 2 and 3 cases, respectively. AI showed superior diagnostic performance in identifying aortic dilatation (AI: sensitivity: 96.3%, specificity: 81.4%, AUC: 0.89) vs (Reports: sensitivity: 25.9%, specificity: 100%, AUC: 0.63), p0.001; and CAC (AI: sensitivity: 89.8%, specificity: 100, AUC: 0.95) vs (Reports: sensitivity: 75.4%, specificity: 94.9%, AUC: 0.85), p = 0.005. Reports had better performance than AI in identifying pulmonary lesions (Reports: sensitivity: 97.6%, specificity: 100%, AUC: 0.99) vs (AI: sensitivity: 92.8%, specificity: 82.4%, AUC: 0.88), p = 0.024; and VCF (Reports: sensitivity:100%, specificity: 100%, AUC: 1.0) vs (AI: sensitivity: 100%, specificity: 63.7%, AUC: 0.82), p0.001. A comparable diagnostic performance was noted in identifying pulmonary emphysema on AI (sensitivity: 80.6%, specificity: 66.7%. AUC: 0.74) and reports (sensitivity: 74.2%, specificity: 97.1%, AUC: 0.86), p = 0.064.Our results demonstrate that incorporating AI support platforms into radiology workflows can provide significant added value to clinical radiology reporting.

Coarctation of Aorta (CoA) is a congenital disease consisting of a narrowing that obstructs the systemic blood flow. This proof-of-concept study aimed to develop a framework for automatically and robustly personalizing aortic hemodynamic computations for the assessment of pre- and post-intervention CoA patients from 3D rotational angiography (3DRA) data. We propose a framework that combines hemodynamic modelling and machine learning (ML) based techniques, and rely on 3DRA data for non-invasive pressure computation in CoA patients. The key features of our framework are a parameter estimation method for calibrating inlet and outlet boundary conditions, and regional mechanical wall properties, to ensure that the computational results match the patient-specific measurements, and an improved ML based pressure drop model capable of predicting the instantaneous pressure drop for a wide range of flow conditions and anatomical CoA variations. We evaluated the framework by investigating 6 patient datasets, under pre- and post-operative setting, and, since all calibration procedures converged successfully, the proposed approach is deemed robust. We compared the peak-to-peak and the cycle-averaged pressure drop computed using the reduced-order hemodynamic model with the catheter based measurements, before and after virtual and actual stenting. The mean absolute error for the peak-to-peak pressure drop, which is the most relevant measure for clinical decision making, was 2.98 mmHg for the pre- and 2.11 mmHg for the post-operative setting. Moreover, the proposed method is computationally efficient: the average execution time was of only $$2.1 \pm 0.8$$ minutes on a standard hardware configuration. The use of 3DRA for hemodynamic modelling could allow for a complete hemodynamic assessment, as well as virtual interventions or surgeries and predictive modeling. However, before such an approach can be used routinely, significant advancements are required for automating the workflow.

To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact. Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.

ObjectivesTo predict ultimate treatment intensity of COVID-19 patients using pulmonary and cardiovascular metrics fully automatically extracted from initial chest CTs.Methods All patients tested positive for SARS-CoV-2 by RT-PCR at our emergency department between March 25 and April 14, 2020 were identified (n=391). For those patients, all initial chest CTs were analyzed (n=85). Multiple pulmonary and cardiovascular metrics were extracted using deep convolutional neural networks. Three clinical treatment intensity groups were defined according to the most intensive treatment of a patient, determined six weeks later: Group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit; ICU). Univariate analyses were performed to analyze differences between groups. Subsequently, multiple metrics were combined in two binary logistic regression analyses and resulting prediction probabilities used to classify whether a patient needed hospitalization or ICU care. For analysis of discriminatory power, ROC curves were plotted and areas-under-the-curves (AUCs) calculated.ResultsThe mean interval between presentation at the emergency department and the chest CT was 1.4 days. Among others, mean percentage of lung volume affected by opacities (PO) and mean total pericardial volume (TPV) increased statistically significantly with higher treatment intensity [from group 1 to 3, standard deviation in brackets: PO: 0.8%(1.5)–11.6%(13.1)–31.6%(20.1); TPV: 733.4ml(231.7)–866.2ml(211.2)–925.7ml(125.5); both: pConclusions Metrics fully automatically extracted from initial chest CTs increase with treatment intensity of COVID-19 patients. This information can be exploited to prospectively manage allocation of healthcare resources.

Funding Acknowledgements This research has been funded by the research grant PlaqueImage, contract number 26/01.09.2016, SMIS code 103544, Project funded by the European Union Aims Coronary shear stress (CSS) has been recently recognized to play a significant role in coronary plaque progression and vulnerabilisation. However, the evolution of CSS after implantation of different types of coronary stents is still under investigation. The aim of this study was to assess the evolution of the CSS along the coronary lesions following implantation of bioabsorbable vascular scaffolds (BVS), to determine the impact of BVS on coronary flow haemodynamics. Methods and results This was a single center prospective pilot study which enrolled 15 patients (aged 58.35 +/- 7.79 years, 13 males) with coronary artery disease undergoing BVS implantation in a major epicardial vessel. In all patients, angio CT scanning (Siemens Somatom Sensation scanner, Erlangen, Germany) was performed prior to the BVS implantation and repeated after 12 months. Lumen information was extracted from the vessels of interest and coronary rest hemodynamics, including CSS, were calculated using a computational fluid dynamics solver. All shear stress calculations were performed at baseline and repeated after 1 year. Average CSS was determined proximally, distally, and at the level of the minimal lumen area (MLA). Average CSS along the stented segment significantly decreased after BVS implantation, from 2.87 +/- 1.64 Pa at baseline to 1.9 +/- 0.49 at 1 year (p = 0.0001). Maximum CSS along the segment also exhibited a significant decrease, from 11.78 +/- 10.06 Pa to 6.35 +/- 3.08 Pa (p = 0.0009). Proximally to the MLA, CSS significantly decreased after BVS implantation, from 3.39 +/- 1.93 Pa at baseline to 1.91 +/- 0.68 Pa at 1 year (p Conclusions Implantation of BVS leads to a significant decrease of CSS after 1 year, especially within coronary segments located proximally to the stenosis. This underlines the role of BVS in re-establishing a physiological pattern of coronary flow in diseased coronary vessels. The feature (mentioned herein) is not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.

In recent years, computational fluid dynamics (CFD) has become a valuable tool for investigating hemodynamics in cerebral aneurysms. CFD provides flow-related quantities, which have been shown to have a potential impact on aneurysm growth and risk of rupture. However, the adoption of CFD tools in clinical settings is currently limited by the high computational cost and the engineering expertise required for employing these tools, e.g., for mesh generation, appropriate choice of spatial and temporal resolution, and of boundary conditions. Herein, we address these challenges by introducing a practical and robust methodology, focusing on computational performance and minimizing user interaction through automated parameter selection. We propose a fully automated pipeline that covers the steps from a patient-specific anatomical model to results, based on a fast, graphics processing unit- (GPU-) accelerated CFD solver and a parameter selection methodology. We use a reduced order model to compute the initial estimates of the spatial and temporal resolutions and an iterative approach that further adjusts the resolution during the simulation without user interaction. The pipeline and the solver are validated based on previously published results, and by comparing the results obtained for 20 cerebral aneurysm cases with those generated by a state-of-the-art commercial solver (Ansys CFX, Canonsburg PA). The automatically selected spatial and temporal resolutions lead to results which closely agree with the state-of-the-art, with an average relative difference of only 2%. Due to the GPU-based parallelization, simulations are computationally efficient, with a median computation time of 40 minutes per simulation.

This chapter describes a possible implementation of a comprehensive model of heart function. The presentation follows the same organization of the previous chapter. We first illustrate how the anatomical model can be build starting from medical images, and enriched with semantic information based on shape analysis. We then discuss efficient computational techniques for the electrophysiology model, based on Lattice–Boltzmann method for the approximation of the monodomain problem and on a graph-based solution of the Eikonal equation. The computation of surface electrical potential and ECG signals on the torso is also addressed, in particular based on the boundary element method. For the solution of the electromechanics problem we consider an efficient GPU-based implementation of the TLED method, discussing how to couple the biomechanics solver with the electrophysiology solver through the active force term, computed as a function of the electrical potential. Heart function depends critically on the interaction between heart tissue and blood: we dedicate specific attention to the implementation of boundary conditions for the electromechanics problem, including the coupling with models of the valves, pericardium bag and circulatory system. Finally, a section is devoted to computational models for hemodynamics, focusing in particular on techniques to solve the coupled fluid-structure interaction problem in the cardiac chambers.

This chapter illustrates three clinical applications of the approaches presented so far in this book. The first one refers to a multi-scale, multi-physics cardiac modeling pipeline for cardiac resynchronization therapy (CRT), based solely on pre-operative, non-invasive data. Results on a small cohort of ten patients are presented and discussed. The second application introduces an AI based model for pressure drop computation in coarctation of the aorta (CoA). Since establishing a large enough patient-specific training database for AI model development would be prohibitively expensive and time consuming for this congenital pathology, a methodology for generating purely synthetic CoA anatomical models is described. The ground truth pressure drop values can be computed for the synthetic models using a three-dimensional Computational Fluid Dynamics (CFD) solver. The AI model reduces the mean absolute pressure drop prediction error more than five times compared to a previously published semi-analytical pressure drop model. Finally, the third application addresses the entire cardiovascular system, based on a lumped parameter model (LPM) of whole body circulation. A similar approach to the one employed for the CoA use case is considered, relying on a purely synthetic training database. Both time-independent, e.g. systemic resistance and compliance, and time-dependent quantities, e.g. ventricular pressure, are predicted. We show that the performance of the AI models is statistically similar to that of the LPM, while the inference time is reduced to milliseconds.

Purpose—Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. Methods—3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. Results—A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. Conclusions—Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters. Fil: Valen-Sendstad, Kristian. Simula Research Laboratory and Center for Cardiological Innovation; Noruega Fil: Bergersen, Aslak W.. University of Oslo; Noruega Fil: Shimogonya, Yuji. Nihon University; Japón Fil: Goubergrits, Leonid. Charite´ – Universita¨tsmedizin Berlin; Alemania Fil: Bruening, Jan. Charité – Universitätsmedizin Berlin; Alemania Fil: Pallares, Jordi. Universitat Rovira I Virgili; España Fil: Cito, Salvatore. Universitat Rovira I Virgili; España Fil: Piskin, Senol. University Of Texas At San Antonio.; Estados Unidos Fil: Pekkan, Kerem. Koc University; Turquía Fil: Geers, Arjan J.. Universitat Pompeu Fabra; España Fil: Larrabide, Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Rapaka, Saikiran. Siemens Medical Solutions USA Inc; Estados Unidos Fil: Mihalef, Viorel. Siemens Medical Solutions USA Inc; Estados Unidos Fil: Fu, Wenyu. Beijing Union University; China Fil: Qiao, Aike. Beijing University of Technology; China Fil: Jain, Kartik. Simula Research Laboratory and Center for Cardiological Innovation; Noruega. University of Siegen; Alemania. University of Zürich; Suiza Fil: Roller, Sabine. University of Siegen; Alemania Fil: Mardal, Kent-Andre. Simula Research Laboratory and Center for Cardiological Innovation; Noruega. University of Oslo; Noruega Fil: Kamakoti, Ramji. Dassault Systemes; Francia Fil: Spirka, Thomas. Simpleware Software Solutions; Reino Unido Fil: Ashton, Neil. University of Oxford; Reino Unido Fil: Revell, Alistair. University of Manchester; Reino Unido Fil: Aristokleous, Nicolas. University of Limerick; Irlanda Fil: Houston, J. Graeme. University of Dundee; Reino Unido Fil: Tsuji, Masanori. Mie Chuo Medical Center; Japón Fil: Ishida, Fujimaro. Mie Chuo Medical Center; Japón Fil: Menon, Prahlad G.. University of Pittsburgh; Estados Unidos Fil: Browne, Leonard D.. University of Limerick; Irlanda Fil: Broderick, Stephen. University of Limerick; Irlanda Fil: Shojima, Masaaki. University of Tokyo; Japón Fil: Koizumi, Satoshi. University of Tokyo; Japón Fil: Barbour, Michael. University of Washington; Estados Unidos Fil: Aliseda, Alberto. University of Washington; Estados Unidos Fil: Morales, Hernán G.. Medisys - Philips Research Paris; Francia Fil: Lefèvre, Thierry. Medisys - Philips Research Paris; Francia Fil: Hodis, Simona. Texas A&M University - Kingsville; Estados Unidos Fil: Al-Smadi, Yahia M.. Jordan University of Science and Technology; Jordania Fil: Tran, Justin S.. Stanford University; Estados Unidos Fil: Marsden, Alison L.. Stanford University; Estados Unidos Fil: Vaippummadhom, Sreeja. EinNel Technlogies; India Fil: Einstein, G. Albert. EinNel Technlogies; India Fil: Brown, Alistair G.. Siemens PLM Software; Estados Unidos Fil: Debus, Kristian. Siemens PLM Software; Estados Unidos Fil: Niizuma, Kuniyasu. Tohoku University; Japón Fil: Rashad, Sherif. Tohoku University; Japón Fil: Sugiyama, Shin-ichiro. Kohnan Hospital; Japón Fil: Owais Khan, M.. University of Toronto; Canadá Fil: Updegrove, Adam R.. University of California at Berkeley; Estados Unidos Fil: Shadden, Shawn C.. University of California at Berkeley; Estados Unidos Fil: Cornelissen, Bart M. W.. Academic Medical Center; Países Bajos Fil: Majoie, Charles B. L. M.. Academic Medical Center; Países Bajos Fil: Berg, Philipp. University of Magdeburg; Alemania Fil: Saalfield, Sylvia. University of Magdeburg; Alemania Fil: Kono, Kenichi. Wakayama Rosai Hospital; Japón Fil: Steinman, David A.. University of Toronto; Canadá

OBJECTIVE: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. MATERIALS AND METHODS: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. RESULTS: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). CONCLUSION: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease. Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent ( R =0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; P Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

The results demonstrate that coronary CT angiography–derived fractional flow reserve using machine learning performs equally to computational fluid dynamics modeling in detecting lesion-specific ischemia; both algorithms outperform coronary CT angiography alone and quantitative coronary angiography.

Diagnosis and treatment of dilated cardiomyopathy (DCM) is challenging due to a large variety of causes and disease stages. Computational models of cardiac electrophysiology (EP) can be used to improve the assessment and prognosis of DCM, plan therapies and predict their outcome, but require personalization. In this work, we present a data-driven approach to estimate the electrical diffusivity parameter of an EP model from standard 12-lead electrocardiograms (ECG). An efficient forward model based on a mono-domain, phenomenological Lattice-Boltzmann model of cardiac EP, and a boundary element-based mapping of potentials to the body surface is employed. The electrical diffusivity of myocardium, left ventricle and right ventricle endocardium is then estimated using polynomial regression which takes as input the QRS duration and electrical axis. After validating the forward model, we computed 9500 EP simulations on 19 different DCM patients in just under three seconds each to learn the regression model. Using this database, we quantify the intrinsic uncertainty of electrical diffusion for given ECG features and show in a leave-one-patient-out cross-validation that the regression method is able to predict myocardium diffusion within the uncertainty range. Finally, our approach is tested on the 19 cases using their clinical ECG. 84% of them could be personalized using our method, yielding mean prediction errors of 18.7 ms for the QRS duration and 6.5 ° for the electrical axis, both values being within clinical acceptability. By providing an estimate of diffusion parameters from readily available clinical data, our data-driven approach could therefore constitute a first calibration step toward a more complete personalization of cardiac EP.

Owing to its clinical importance, there has been a growing body of research on understanding the hemodynamics of cerebral aneurysms. Traditionally, this work has been performed using general-purpose, state-of-the-art commercial solvers. This has meant requiring engineering expertise for making appropriate choices on the geometric discretization, time-step selection, choice of boundary conditions etc. Recently, a CFD research prototype has been developed (Siemens Healthcare GmbH, Prototype — not for diagnostic use) for end-to-end analysis of aneurysm hemodynamics. This prototype enables anatomical model preparation, hemodynamic computations, advanced visualizations and quantitative analysis capabilities. In this study, we investigate the accuracy of the hemodynamic solver in the prototype against a commercially available CFD solver ANSYS CFX 16.0 (ANSYS Inc., Canonsburg, PA, www.ansys.com) retrospectively on a sample of twenty patient-derived aneurysm models, and show good agreement of hemodynamic parameters of interest.

We present the description of a fully coupled simulator FEHM for modeling coupled thermo-hydro-mechanical (THM) processes in geomedia. The coupled equations for fluid flow and energy transport are implemented using finite volume whereas Galerkin finite element method is used for mechanical force balance. The simulator is designed to address spatial scales on the order of tens of centimeters to tens of kilometers, and time scales on the order of hours to tens of years. The governing coupled nonlinear equations are solved using a Newton–Rapshon scheme with analytically or numerically computed Jacobians. A suite of models is available for coupling flow and mechanical deformation via permeability–deformation relationships. The coupled simulator is verified by comparing with several analytical solutions developed for this purpose. A subset of the simulator capabilities is benchmarked against commercially available simulators. We also demonstrate a good match with data from Desert Peak geothermal field in Nevada, USA. This validation required the use of a shear failure model with non-linear permeability–stress relationship. In addition, we present another application involving fluid injection into an inclined fault zone using a non-orthogonal grid with stress-dependent Young׳s modulus and permeability.

Solid voxelization represents the process of transforming a polygonal mesh into a voxel representation by associating each polygon of a mesh with the cells in the voxel grid. We introduce a novel approach for the voxelization of solid objects, designed for Graphics Processing Units (GPU). The method is based on a heuristic approach that computes an approximate distance field instead of using mesh surface normals or exact point-to-triangle distances. Two main steps are required: voxel marking and distance field computation. In the first step, each voxel is marked based on its location relative to the mesh (inside, outside of the domain or on its boundary), and, during the second step, a signed distance field is computed. Experiments focused on meshes encountered in medical imaging applications: a left ventricle and a coronary artery. The proposed method is found to be exceptionally robust as it is able to handle meshes with severe defects such as self intersections and holes. The GPU based implementation is on average 20 times faster than the multi-core CPU based implementation.