Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms

Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold standard for PE diagnosis, it is still susceptible to misdiagnosis or significant diagnosis delays, which may be fatal for critical cases. Despite the recently demonstrated power of deep learning to bring a significant boost in performance in a wide range of medical imaging tasks, there are still very few published researches on automatic pulmonary embolism detection. Herein we introduce a deep learning based approach, which efficiently combines computer vision and deep neural networks for pulmonary embolism detection in CTPA. Our method brings novel contributions along three orthogonal axes: (1) automatic detection of anatomical structures; (2) anatomical aware pretraining, and (3) a dual-hop deep neural net for PE detection. We obtain state-of-the-art results on the publicly available multicenter large-scale RSNA dataset.

In-stent restenosis in acute coronary syndrome-a classic and a machine learning approach

Background

In acute coronary syndrome (ACS), a number of previous studies tried to identify the risk factors that are most likely to influence the rate of in-stent restenosis (ISR), but the contribution of these factors to ISR is not clearly defined. Thus, the need for a better way of identifying the independent predictors of ISR, which comes in the form of Machine Learning (ML).

Objectives

The aim of this study is to evaluate the relationship between ISR and risk factors associated with ACS and to develop and validate a nomogram to predict the probability of ISR through the use of ML in patients undergoing percutaneous coronary intervention (PCI).

Methods

Consecutive patients presenting with ACS who were successfully treated with PCI and who had an angiographic follow-up after at least 3 months were included in the study. ISR risk factors considered into the study were demographic, clinical and peri-procedural angiographic lesion risk factors. We explored four ML techniques (Random Forest (RF), support vector machines (SVM), simple linear logistic regression (LLR) and deep neural network (DNN)) to predict the risk of ISR. Overall, 21 features were selected as input variables for the ML algorithms, including continuous, categorical and binary variables.

Results

The total cohort of subjects included 340 subjects, in which the incidence of ISR observed was 17.68% (n = 87). The most performant model in terms of ISR prediction out of the four explored was RF, with an area under the receiver operating characteristic (ROC) curve of 0.726. Across the predictors herein considered, only three predictors were statistically significant, precisely, the number of affected arteries (≥2), stent generation and diameter.

Conclusion

ML models applied in patients after PCI can contribute to a better differentiation of the future risk of ISR.

Importance of Visual Estimation of Coronary Artery Stenoses and Use of Functional Evaluation for Appropriate Guidance of Coronary Revascularization-Multiple Operator Evaluation

Background: Visual estimation (VE) of coronary stenoses is the first step during invasive coronary angiography. The aim of this study was to evaluate the accuracy of VE together with invasive functional assessment (IFA) in defining the functional significance (FS) of coronary stenoses based on the opinion of multiple operators. Methods: Fourteen independent operators visually evaluated 133 coronary lesions which had a previous FFR measurement, indicating the degree of stenosis (DS), FS and IFA intention. We determined the accuracy of FS prediction using several scenarios combining individual and group decision, considering IFA as deemed necessary by the operator or only in intermediate lesions. Results: The accuracy of VE in predicting FS was largely variable between operators (average 66.1%); it improved significantly when IFA was used either as per operator’s opinion (86.3%; p < 0.0001) or only in intermediate DS (82.9; p < 0.0001). There was no significant difference between using IFA per observer’s opinion or only in intermediate DS lesions (p = 0.166). The poorest accuracy of VE for FS was obtained in intermediate DS lesions (59.1%). Conclusions: There are significant inter-observer differences in reporting the degree of DS, while the accuracy of VE prediction of FS is also largely dependent on the operator, and the worst performance is obtained in the evaluation of intermediate DS.

Abstract OT-39-01: Artificial intelligence supporting cancer patients across Europe - the ASCAPE project for breast cancer patients

Background Many breast cancer patients experience adverse effects of cancer or treatment, which can considerably decrease quality of life (QoL). The current strategy of supporting breast cancer patients does not meet their needs due to the limited personalized-based approach in rehabilitation plan and the lack of healthcare, financial and other resources. ASCAPE (Artificial intelligence Supporting CAncer Patients across Europe) is a collaborative research project involving 15 partners from 7 countries, including academic medical centers, small and medium-sized enterprises, research centers and universities, aiming to leverage the recent advances in Big Data and AI (Artificial Intelligence) to support cancer patients’ QoL and health status. Specifically, ASCAPE aims to provide personalized- and AI-based predictions for QoL issues in breast cancer patients as well as suggest potential interventions to their physicians.

Trial design During the first part of the project, large-scale retrospective datasets with breast cancer patients will be analyzed to develop and train AI-based models for specific QoL issues. During the second part of the project, a multicenter prospective longitudinal study is planned. Eligible patients will be followed for one year with validated questionnaires regarding different QoL issues, and wearables that will collect active monitoring data on physical activity, sleep pattern, and heart rate. The collected data will be used to further train and optimize the AI-based models and personalized-based intervention suggestions.Based on the retrospective and prospective data, an ASCAPE-integrated prototype will be developed, enabling personalized- and AI-based predictions and intervention suggestions. This approach will be evaluated at the end of the prospective study regarding patients´ and physicians´ experience as well as health economics.

Eligibility criteria Breast cancer patients planned for curative treatment with surgery with or without oncological therapy or breast cancer patients at least 1 year post-treatment (except endocrine therapy) will be eligible for the prospective study.

Specific aims 1.To develop and optimize AI-based predictions for QoL issues in breast cancer patients as well as potential intervention suggestions.2.To evaluate the AI-based follow-up approach for breast cancer survivors in terms of patients´ experience, physicians´ experience, and health economics.

Statistical methods For discrete QoL outcome variables, ASCAPE will examine the efficiency of classification-based machine learning models trained using decision tree learning algorithms, nearest-neighbors based algorithms, probabilistic learning algorithms, support vector machines and (deep) neural networks. Regressive counterparts of aforementioned methods will be analyzed for numeric QoL outcome variables including also regression specific methods (e.g., ridge regression, lasso regression and elastic net regression). The accuracy of trained models will be estimated relying on standard machine learning validation procedures such as the K-fold cross-validation and leave-one-out cross-validation.

The ASCAPE platform will utilize state-of-the-art explainability techniques to make the machine learning models’ predictions transparent and comprehensible for the patient and the physician.Present accrual and target accrual Four retrospective datasets will be used for the first part of the project including approximately 18,000 breast cancer patients. For the prospective study, it is planned to be included about 30 patients monthly during a period of 12 months.

Contact information for people with a specific interest in the trial https://ascape-project.eu/artificial-intelligence-supporting-cancer-patients-across-europe

Citation Format: Antonios Valachis, Serge Autexier, Imma Grau, Lucian Itu, Dusan Jakotevic, Thanos Kosmidis, Montserrat Muñoz, Konstantinos Perakis, Johannes Rust, Milos Savic, Paris Kosmidis. Artificial intelligence supporting cancer patients across Europe - the ASCAPE project for breast cancer patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr OT-39-01.

Deep neural networks for ECG-free cardiac phase and end-diastolic frame detection on coronary angiographies

Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive functional assessment of CAD. This task is generally performed via manual frame selection or semi-automated selection based on simultaneously acquired ECG signals - thus introducing the requirement of simultaneous ECG recordings. In this paper, we evaluate the performance of a purely image based workflow relying on deep neural networks for fully automated cardiac phase and EDF detection on coronary angiographies. A first deep neural network (DNN), trained to detect coronary arteries, is employed to preselect a subset of frames in which coronary arteries are well visible. A second DNN predicts cardiac phase labels for each frame. Only in the training and evaluation phases for the second DNN, ECG signals are used to provide ground truth labels for each angiographic frame. The networks were trained on 56,655 coronary angiographies from 6820 patients and evaluated on 20,780 coronary angiographies from 6261 patients. No exclusion criteria related to patient state (stable or acute CAD), previous interventions (PCI or CABG), or pathology were formulated. Cardiac phase detection had an accuracy of 98.8 %, a sensitivity of 99.3 % and a specificity of 97.6 % on the evaluation set. EDF prediction had a precision of 98.4 % and a recall of 97.9 %. Several sub-group analyses were performed, indicating that the cardiac phase detection performance is largely independent from acquisition angles, the heart rate of the patient, and the angiographic view (LCA / RCA). The average execution time of cardiac phase detection for one angiographic series was on average less than five seconds on a standard workstation. We conclude that the proposed image based workflow potentially obviates the need for manual frame selection and ECG acquisition, representing a relevant step towards automated CAD assessment.

P1434 Evolution of coronary wall shear stress following implantation of bioabsorbable vascular scaffolds - first results of a 1-year follow-up pilot study

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 &lt;0.0001). However, this decrease in CSS was not significant distally to the MLA (1.3 +/- 0.72 Pa vs 1.59 +/- 0.65 Pa, p = 0.9).

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.

Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium

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<0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR.

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.

Non-invasive assessment of patient-specific aortic haemodynamics from four-dimensional flow MRI data

We introduce a parameter estimation framework for automatically and robustly personalizing aortic haemodynamic computations from four-dimensional magnetic resonance imaging data. The framework is based on a reduced-order multiscale fluid-structure interaction blood flow model, and on two calibration procedures. First, Windkessel parameters of the outlet boundary conditions are personalized by solving a system of nonlinear equations. Second, the regional mechanical wall properties of the aorta are personalized by employing a nonlinear least-squares minimization method. The two calibration procedures are run sequentially and iteratively until both procedures have converged. The parameter estimation framework was successfully evaluated on 15 datasets from patients with aortic valve disease. On average, only 1.27 ± 0.96 and 7.07 ± 1.44 iterations were required to personalize the outlet boundary conditions and the regional mechanical wall properties, respectively. Overall, the computational model was in close agreement with the clinical measurements used as objectives (pressures, flow rates, cross-sectional areas), with a maximum error of less than 1%. Given its level of automation, robustness and the short execution time (6.2 ± 1.2 min on a standard hardware configuration), the framework is potentially well suited for a clinical setting.

Personalized blood flow computations: A hierarchical parameter estimation framework for tuning boundary conditions

We propose a hierarchical parameter estimation framework for performing patient-specific hemodynamic computations in arterial models, which use structured tree boundary conditions. A calibration problem is formulated at each stage of the hierarchical framework, which seeks the fixed point solution of a nonlinear system of equations. Common hemodynamic properties, like resistance and compliance, are estimated at the first stage in order to match the objectives given by clinical measurements of pressure and/or flow rate. The second stage estimates the parameters of the structured trees so as to match the values of the hemodynamic properties determined at the first stage. A key feature of the proposed method is that to ensure a large range of variation, two different structured tree parameters are personalized for each hemodynamic property. First, the second stage of the parameter estimation framework is evaluated based on the properties of the outlet boundary conditions in a full body arterial model: the calibration method converges for all structured trees in less than 10 iterations. Next, the proposed framework is successfully evaluated on a patient-specific aortic model with coarctation: only six iterations are required for the computational model to be in close agreement with the clinical measurements used as objectives, and overall, there is a good agreement between the measured and computed quantities. Copyright © 2016 John Wiley & Sons, Ltd.

A self-taught artificial agent for multi-physics computational model personalization

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.

A machine-learning approach for computation of fractional flow reserve from coronary computed tomography

Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor.

Noninvasive hemodynamic assessment, treatment outcome prediction and follow-up of aortic coarctation from MR imaging

PURPOSE

Coarctation of the aorta (CoA) is a congenital heart disease characterized by an abnormal narrowing of the proximal descending aorta. Severity of this pathology is quantified by the blood pressure drop (△P) across the stenotic coarctation lesion. In order to evaluate the physiological significance of the preoperative coarctation and to assess the postoperative results, the hemodynamic analysis is routinely performed by measuring the △P across the coarctation site via invasive cardiac catheterization. The focus of this work is to present an alternative, noninvasive measurement of blood pressure drop △P through the introduction of a fast, image-based workflow for personalized computational modeling of the CoA hemodynamics.

METHODS

The authors propose an end-to-end system comprised of shape and computational models, their personalization setup using MR imaging, and a fast, noninvasive method based on computational fluid dynamics (CFD) to estimate the pre- and postoperative hemodynamics for coarctation patients. A virtual treatment method is investigated to assess the predictive power of our approach.

RESULTS

Automatic thoracic aorta segmentation was applied on a population of 212 3D MR volumes, with mean symmetric point-to-mesh error of 3.00 ± 1.58 mm and average computation time of 8 s. Through quantitative evaluation of 6 CoA patients, good agreement between computed blood pressure drop and catheter measurements is shown: average differences are 2.38 ± 0.82 mm Hg (pre-), 1.10 ± 0.63 mm Hg (postoperative), and 4.99 ± 3.00 mm Hg (virtual stenting), respectively.

CONCLUSIONS

The complete workflow is realized in a fast, mostly-automated system that is integrable in the clinical setting. To the best of our knowledge, this is the first time that three different settings (preoperative--severity assessment, poststenting--follow-up, and virtual stenting--treatment outcome prediction) of CoA are investigated on multiple subjects. We believe that in future-given wider clinical validation-our noninvasive in-silico method could replace invasive pressure catheterization for CoA.

GPU-accelerated model for fast, three-dimensional fluid-structure interaction computations

In this paper we introduce a methodology for performing one-way Fluid-Structure interaction (FSI), i.e. where the motion of the wall boundaries is imposed. We use a Graphics Processing Unit (GPU) accelerated Lattice-Boltzmann Method (LBM) implementation and present an efficient workflow for embedding the moving geometry, given as a set of polygonal meshes, in the LBM computation. The proposed method is first validated in a synthetic experiment: a vessel which is periodically expanding and contracting. Next, the evaluation focuses on the 3D Peristaltic flow problem: a fluid flows inside a flexible tube, where a periodic wave-like deformation produces a fluid motion along the centerline of the tube. Different geometry configurations are used and results are compared against previously published solutions. The efficient approach leads to an average execution time of approx. one hour per computation, whereas 50% of it is required for the geometry update operations. Finally, we also analyse the effect of changing the Reynolds number on the flow streamlines: the flow regime is significantly affected by the Reynolds number.

Comparison of Fractional Flow Reserve Based on Computational Fluid Dynamics Modeling Using Coronary Angiographic Vessel Morphology Versus Invasively Measured Fractional Flow Reserve

Invasive fractional flow reserve (FFRinvasive), although gold standard to identify hemodynamically relevant coronary stenoses, is time consuming and potentially associated with complications. We developed and evaluated a new approach to determine lesion-specific FFR on the basis of coronary anatomy as visualized by invasive coronary angiography (FFRangio): 100 coronary lesions (50% to 90% diameter stenosis) in 73 patients (48 men, 25 women; mean age 67 ± 9 years) were studied. On the basis of coronary angiograms acquired at rest from 2 views at angulations at least 30° apart, a PC-based computational fluid dynamics modeling software used personalized boundary conditions determined from 3-dimensional reconstructed angiography, heart rate, and blood pressure to derive FFRangio. The results were compared with FFRinvasive. Interobserver variability was determined in a subset of 25 narrowings. Twenty-nine of 100 coronary lesions were hemodynamically significant (FFRinvasive ≤ 0.80). FFRangio identified these with an accuracy of 90%, sensitivity of 79%, specificity of 94%, positive predictive value of 85%, and negative predictive value of 92%. The area under the receiver operating characteristic curve was 0.93. Correlation between FFRinvasive (mean: 0.84 ± 0.11) and FFRangio (mean: 0.85 ± 0.12) was r = 0.85. Interobserver variability of FFRangio was low, with a correlation of r = 0.88. In conclusion, estimation of coronary FFR with PC-based computational fluid dynamics modeling on the basis of lesion morphology as determined by invasive angiography is possible with high diagnostic accuracy compared to invasive measurements.

Model based non-invasive estimation of PV loop from echocardiography

We introduce a model-based approach for the non-invasive estimation of patient specific, left ventricular PV loops. A lumped parameter circulation model is used, composed of the pulmonary venous circulation, left atrium, left ventricle and the systemic circulation. A fully automated parameter estimation framework is introduced for model personalization, composed of two sequential steps: first, a series of parameters are computed directly, and, next, a fully automatic optimization-based calibration method is employed to iteratively estimate the values of the remaining parameters. The proposed methodology is first evaluated for three healthy volunteers: a perfect agreement is obtained between the computed quantities and the clinical measurements. Additionally, for an initial validation of the methodology, we computed the PV loop for a patient with mild aortic valve regurgitation and compared the results against the invasively determined quantities: there is a close agreement between the time-varying LV and aortic pressures, time-varying LV volumes, and PV loops.

A novel coupling algorithm for computing blood flow in viscoelastic arterial models

We propose a novel coupling algorithm, based on the operator-splitting scheme, which implements the viscoelastic wall law at the coupling nodes of the vessels. Two different viscoelastic models are used (V1 and V2), leading to five different computational setups: elastic wall law, model V1 applied at interior and coupling grid points, model V1 applied only at the interior grid points (V1-int), model V2 applied at interior and coupling grid points, model V2 applied only at the interior grid points (V2-int). These have been tested with two arterial configurations: (i) single artery, and (ii) complete arterial tree. Models V1-int and V2-int lead to incorrect conclusions and to errors which can be of the same order as, and are at least 1/5 of, the difference between the results with the elastic and the viscoelastic laws. Both test cases demonstrate the importance of modeling the viscous component of the pressure-area relationship at all grid points, including the coupling points between vessels or at the inlet/outlet of the model.

Graphics processing unit accelerated one-dimensional blood flow computation in the human arterial tree

One-dimensional blood flow models have been used extensively for computing pressure and flow waveforms in the human arterial circulation. We propose an improved numerical implementation based on a graphics processing unit (GPU) for the acceleration of the execution time of one-dimensional model. A novel parallel hybrid CPU-GPU algorithm with compact copy operations (PHCGCC) and a parallel GPU only (PGO) algorithm are developed, which are compared against previously introduced PHCG versions, a single-threaded CPU only algorithm and a multi-threaded CPU only algorithm. Different second-order numerical schemes (Lax-Wendroff and Taylor series) are evaluated for the numerical solution of one-dimensional model, and the computational setups include physiologically motivated non-periodic (Windkessel) and periodic boundary conditions (BC) (structured tree) and elastic and viscoelastic wall laws. Both the PHCGCC and the PGO implementations improved the execution time significantly. The speed-up values over the single-threaded CPU only implementation range from 5.26 to 8.10 × , whereas the speed-up values over the multi-threaded CPU only implementation range from 1.84 to 4.02 × . The PHCGCC algorithm performs best for an elastic wall law with non-periodic BC and for viscoelastic wall laws, whereas the PGO algorithm performs best for an elastic wall law with periodic BC.

A framework for personalization of coronary flow computations during rest and hyperemia

We introduce a Computational Fluid Dynamics (CFD) based method for performing patient-specific coronary hemodynamic computations under two conditions: at rest and during drug-induced hyperemia. The proposed method is based on a novel estimation procedure for determining the boundary conditions from non-invasively acquired patient data at rest. A multi-variable feedback control framework ensures that the computed mean arterial pressure and the flow distribution matches the estimated values for an individual patient during the rest state. The boundary conditions at hyperemia are derived from the respective rest-state values via a transfer function that models the vasodilation phenomenon. Simulations are performed on a coronary tree where a 65% diameter stenosis is introduced in the left anterior descending (LAD) artery, with the boundary conditions estimated using the proposed method. The results demonstrate that the estimation of the hyperemic resistances is crucial in order to obtain accurate values for pressure and flow rates. Results from an exhaustive sensitivity analysis have been presented for analyzing the variability of trans-stenotic pressure drop and Fractional Flow Reserve (FFR) values with respect to various measurements and assumptions.