Tiziano Passerini

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.

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.

Personalized Computer Simulation of Diastolic Function in Heart Failure

The search for a parameter representing left ventricular relaxation from non-invasive and invasive diagnostic tools has been extensive, since heart failure (HF) with preserved ejection fraction (HF-pEF) is a global health problem. We explore here the feasibility using patient-specific cardiac computer modeling to capture diastolic parameters in patients suffering from different degrees of systolic HF. Fifty eight patients with idiopathic dilated cardiomyopathy have undergone thorough clinical evaluation, including cardiac magnetic resonance imaging (MRI), heart catheterization, echocardiography, and cardiac biomarker assessment. A previously-introduced framework for creating multi-scale patient-specific cardiac models has been applied on all these patients. Novel parameters, such as global stiffness factor and maximum left ventricular active stress, representing cardiac active and passive tissue properties have been computed for all patients. Invasive pressure measurements from heart catheterization were then used to evaluate ventricular relaxation using the time constant of isovolumic relaxation Tau (τ). Parameters from heart catheterization and the multi-scale model have been evaluated and compared to patient clinical presentation. The model parameter global stiffness factor, representing diastolic passive tissue properties, is correlated significantly across the patient population with τ. This study shows that multi-modal cardiac models can successfully capture diastolic (dys) function, a prerequisite for future clinical trials on HF-pEF.

Comprehensive preclinical evaluation of a multi-physics model of liver tumor radiofrequency ablation.

PurposeWe aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA).MethodsThe RFA computation becomes subject specific after several levels of personalization: geometrical and biophysical (hemodynamics, heat transfer and an extended cellular necrosis model). We present a comprehensive experimental setup combining multimodal, pre- and postoperative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power.ResultsTo exploit this dataset, an efficient processing pipeline is introduced, which copes with image noise, variable resolution and anisotropy. The validation study includes twelve ablations from five healthy pig livers: a mean point-to-mesh error between predicted and actual ablation extent of 5.3 ± 3.6xa0mm is achieved.ConclusionThis enables an end-to-end preclinical validation framework that considers the available dataset.

Challenges to Validate Multi-Physics Model of Liver Tumor Radiofrequency Ablation from Pre-clinical Data

The planning and interventional guidance of liver tumor radiofrequency ablation (RFA) is difficult due to the cooling effect of large vessels and the large variability of tissue parameters. Subject-specific modeling of RFA is challenging as it requires the knowledge of model geometry and hemodynamics as well as the simulation of heat transfer and cell death mechanisms.In this paper, we propose to validate such a model from pre-operative multi-modal images and intra-operative signals (temperature and power) measured by the ablation device itself. In particular, the RFA computation becomes subject-specific after three levels of personalization: anatomical, heat transfer, and a novel cellular necrosis model. We propose an end-to-end pre-clinical validation framework that considers the most comprehensive dataset for model validation. This framework can also be used for parameter estimation and we evaluate its predictive power in order to fully assess the possibility to personalize our model in the future. Such a framework would therefore not require any necrosis information, thus better suited for clinical applications. We evaluated our approach on seven ablations from three healthy pigs.The predictive power of the model was tested: a mean point-to-mesh error between predicted and actual ablation extent of 3.5 mm was achieved.

Estimation of Local Conduction Velocity from Myocardium Activation Time: Application to Cardiac Resynchronization Therapy

As models of cardiac electrophysiology (EP) are maturing, an increasing effort is being put in their translation to the bed side, in particular for abnormal cardiac rhythm diagnosis and therapy planning. However, the parameters that govern these models need to be estimated from noisy and sparse clinical data in an efficient and precise way, which is still an unsolved challenge. Invasive cardiac mapping provides the richest EP information available today. This paper proposes a new method to estimate a local map of electrical conductivities of the bi-ventricular heart by applying the back-propagation error concept, widely used in neural networks. The method works when either endocardial or epicardial activation time maps are available, and can cope with heterogeneous cardiac tissue. The method was evaluated on synthetic data, showing significantly increased performance in goodness of fit compared to a global parameter estimation approach. The resulting predictive power of the personalized model for cardiac resynchronization therapy was then assessed on 16 swine models of left bundle branch block with rich imaging and EP data before and after CRT. With the proposed personalization, the average error in activation time post CRT was (10 pm 4.5) ms, lower than the observed pre/post-CRT difference of (26.3 pm 16.8) ms.

Patient-Specific Modeling of the Coronary Circulation

The instantaneous wave-Free Ratio (IFR) has been recently validated as a rest state pressure-derived index of coronary stenosis severity. We demonstrate that IFR and other patient-specific features of rest-state coronary hemodynamics can be quantified via a novel approach based on reduced-order computational modeling of blood flow. Blood flow is computed in image-based anatomical reconstructions of the coronary tree from Coronary Angiography (CA). A fully automatic two step parameter estimation framework ensures that the computations match the available patient-specific measurements. We evaluate a hybrid decision making strategy c-IFR–invasive FFR against an FFR-only strategy using a dataset comprising 125 lesions (64 patients). Lesions were considered functionally significant if c-IFR 0.93, while lesions with intermediate c-IFR were classified based on FFR. Of the 125 lesions, 43 were hemodynamically significant (FFR ≤ 0.8). The hybrid c-IFR–FFR strategy resulted in a diagnostic accuracy of 96% when compared to the FFR-only strategy, while requiring invasive FFR assessment in only 34% (43) of the lesions.

A Parameter Estimation Framework for Patient-Specific Assessment of Aortic Coarctation

In this chapter we introduce a method based on computational fluid dynamics for non-invasively assessing patients with aortic coarctation. While in practice the pressure gradient across the coarctation is typically measured invasively with a catheter, the proposed method determines the pressure gradient using a computational modeling approach, which relies on medical imaging data, routine non-invasive clinical measurements and physiological principles. The main components of the method are a reduced-order model coupled with a comprehensive pressure-drop formulation, and a parameter estimation method for personalizing the boundary conditions and the vessel wall parameters. The parameter estimation method is fully automated, and is based on an iterative tuning procedure to obtain a close match between the computed and the non-invasively determined quantities. A key feature is a warm-start to the optimization procedure, with better initial solution for the nonlinear system of equations, to reduce the number of iterations needed for the calibration of the geometrical multiscale models. To achieve these goals, the initial solution, computed with a lumped parameter model, is adapted before solving the parameter estimation problem for the geometrical multiscale circulation model: the resistance and the compliance of the circulation model are estimated and compensated. This feature is based on research, and is not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.

IMAGE-BASED COMPUTATION OF INSTANTANEOUS WAVE-FREE RATIO FROM ROUTINE CORONARY ANGIOGRAPHY: EVALUATION OF A HYBRID DECISION MAKING STRATEGY WITH FFR

A hybrid Instantaneous wave-Free Ratio (IFR)/Fractional Flow Reserve (FFR) decision making strategy was recently proposed for increasing the adoption of functional assessment of coronary artery stenoses. Therein IFR > 0.93 was used to defer revascularization, IFR < 0.86 to confirm treatment, whilst

Data-Driven Model Reduction for Fast, High Fidelity Atrial Electrophysiology Computations

Understanding and predicting atrial electrophysiology, for diagnosis and therapy planning purposes, calls for methods able to accurately represent the complex patterns of atrial electrical activity, and to produce very fast predictions to be suitable for use in the clinical practice. We apply a data-driven approach for the model reduction of an atrial cellular model. The reduced model predicts cellular action potentials (AP) in a simple form but is effective in capturing the physiological complexity of the original model. The model construction starts from an AP manifold learning which reduces the AP manifold dimension to 15, and continues with a regression model learning to predict the 15 components in the reduced AP manifold. The regression model has the potential to drastically improve the performance of atrial tissue-level electrophysiology (EP) modeling, enabling a 75 % reduction of the computational cost with the same time step and up to two order of magnitudes smaller computational time with larger time steps. The model is also capable of describing the restitution properties of the AP, as demonstrated in tests with varying diastolic intervals. This model has great potential use for real-time personalized atrial EP modeling, and the same modeling technique can be extended to the study of other excitable myocardial tissues.