Rupture Risk of Small Unruptured Intracranial Aneurysms in Japanese Adults.

Background and Purpose- Therapeutic decision making for small unruptured intracranial aneurysms (<10 mm) is difficult. We aimed to develop a rupture risk model for small intracranial aneurysms in Japanese adults, including clinical, morphological, and hemodynamic parameters. 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<0.001) with multiple aneurysms (OR, 2.58 [95% CI, 1.07-6.19] 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<0.001), and lower pressure loss coefficient (OR, 0.33 for each decrease of 1 unit [95% CI, 0.14-0.77] 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.

Patient-specific requirements and clinical validation of MRI-based pressure mapping: A two-center study in patients with aortic coarctation.

BACKGROUND: Invasive peak-to-peak pressure gradients are the current clinical reference standard for assessing aortic coarctation. To obtain them, patients need to undergo arterial heart catheterization. Unless an intervention is performed, the procedure remains purely diagnostic, while the concomitant risks remain. PURPOSE: To validate MRI-based pressure mapping against pressure drop derived from heart catheterization and to define minimal clinical requirements. STUDY TYPE: Prospective clinical validation study. POPULATION: Twenty-seven coarctation patients with an indicated heart catheterization were enrolled at two clinical centers. MRI SEQUENCES: 1.5T including 4D velocity-encoded MRI and 3D anatomical imaging of the aorta. ASSESSMENT: Pressure drop across the stenosis was calculated by pressure mapping based on the pressure Poisson equation. Calculated pressure drops were compared with catheter measured data. Spatial and temporal resolution were analyzed using in silico phantom-based data as well as in vivo measurements. STATISTICS: Pressure drop was compared to peak-to-peak measurements. A two-sample paired mean equivalence test was used. RESULTS: In patients without imaging artifacts and a required spatial resolution ≥5 voxel/diameter, significant equivalence of pressure mapping compared to heart catheterization was found (17.5 ± 6.49 vs. 16.6 ± 6.53 mmHg, P < 0.001). DATA CONCLUSION: Pressure mapping provides equivalent accuracy to pressure drop obtained from heart catheterization in patients 1) without previous stenting and 2) with sufficient spatial image resolution (at least 5 voxels/diameter). In these patients the method can reliably be performed prior to the actual procedure, and thus allows safe noninvasive treatment planning based on MRI. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:81-89.

Personalized Pre- and Post-Operative Hemodynamic Assessment of Aortic Coarctation from 3D Rotational Angiography.

PURPOSE: 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. METHODS: 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. RESULTS: 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 [Formula: see text] minutes on a standard hardware configuration. CONCLUSION: 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.

Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks.

Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia. Acquiring high resolution electroanatomic maps is technically challenging and may require interpolation methods to obtain dense measurements. These methods, however, cannot recover activation times in the entire biventricular domain. This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements. Our method is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model. Using geometries sampled from a statistical shape model of biventricular anatomy, diverse wave dynamics are induced by randomly sampling scar and border zone distributions, locations of initial activation, and tissue conduction velocities. Once trained, the method accurately reconstructs biventricular activation times in left-out synthetic simulations with a mean absolute error of 3.9 ms ± 4.2 ms at a sampling density of one measurement sample per cm2. The total activation time is matched with a mean error of 1.4 ms ± 1.4 ms. A significant decrease in errors is observed in all heart zones with an increased number of samples. Without re-training, the network is further evaluated on two datasets: (1) an in-house dataset comprising four ischemic porcine hearts with dense endocardial activation maps; (2) the CRT-EPIGGY19 challenge data comprising endo- and epicardial measurements of 5 infarcted and 6 non-infarcted swines. In both setups the neural network recovers biventricular activation times with a mean absolute error of less than 10 ms even when providing only a subset of endocardial measurements as input. Furthermore, we present a simple approach to suggest new measurement locations in real-time based on the estimated uncertainty of the graph network predictions. The model-guided selection of measurement locations allows to reduce by 40% the number of measurements required in a random sampling strategy, while achieving the same prediction error. In all the tested scenarios, the proposed approach estimates biventricular activation times with comparable or better performance than a personalized computational model and significant runtime advantages.

An Automated Workflow for Hemodynamic Computations in Cerebral Aneurysms.

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.

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.

Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge.

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.

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

PURPOSE: We aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA). METHODS: The 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. RESULTS: To 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 ± 3.6 mm is achieved. CONCLUSION: This enables an end-to-end preclinical validation framework that considers the available dataset.

Verification of a research prototype for hemodynamic analysis of cerebral aneurysms.

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.

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.

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.

Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors.

Radiofrequency ablation (RFA) is an established treatment for liver cancer when resection is not possible. Yet, its optimal delivery is challenged by the presence of large blood vessels and the time-varying thermal conductivity of biological tissue. Incomplete treatment and an increased risk of recurrence are therefore common. A tool that would enable the accurate planning of RFA is hence necessary. This manuscript describes a new method to compute the extent of ablation required based on the Lattice Boltzmann Method (LBM) and patient-specific, pre-operative images. A detailed anatomical model of the liver is obtained from volumetric images. Then a computational model of heat diffusion, cellular necrosis, and blood flow through the vessels and liver is employed to compute the extent of ablated tissue given the probe location, ablation duration and biological parameters. The model was verified against an analytical solution, showing good fidelity. We also evaluated the predictive power of the proposed framework on ten patients who underwent RFA, for whom pre- and post-operative images were available. Comparisons between the computed ablation extent and ground truth, as observed in postoperative images, were promising (DICE index: 42%, sensitivity: 67%, positive predictive value: 38%). The importance of considering liver perfusion while simulating electrical-heating ablation was also highlighted. Implemented on graphics processing units (GPU), our method simulates 1 minute of ablation in 1.14 minutes, allowing near real-time computation.

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.

Non-invasive hemodynamic assessment of aortic coarctation: validation with in vivo measurements.

We propose a CFD-based approach for the non-invasive hemodynamic assessment of pre- and post-operative coarctation of aorta (CoA) patients. Under our approach, the pressure gradient across the coarctation is determined from computational modeling based on physiological principles, medical imaging data, and routine non-invasive clinical measurements. The main constituents of our approach are a reduced-order model for computing blood flow in patient-specific aortic geometries, a parameter estimation procedure for determining patient-specific boundary conditions and vessel wall parameters from non-invasive measurements, and a comprehensive pressure-drop formulation coupled with the overall reduced-order model. The proposed CFD-based algorithm is fully automatic, requiring no iterative tuning procedures for matching the computed results to observed patient data, and requires approximately 6-8 min of computation time on a standard personal computer (Intel Core2 Duo CPU, 3.06 GHz), thus making it feasible for use in a clinical setting. The initial validation studies for the pressure-drop computations have been performed on four patient datasets with native or recurrent coarctation, by comparing the results with the invasively measured peak pressure gradients recorded during routine cardiac catheterization procedure. The preliminary results are promising, with a mean absolute error of less than 2 mmHg in all the patients.

Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images.

Radio-frequency ablation (RFA), the most widely used minimally invasive ablative therapy of liver cancer, is challenged by a lack of patient-specific planning. In particular, the presence of blood vessels and time-varying thermal diffusivity makes the prediction of the extent of the ablated tissue difficult. This may result in incomplete treatments and increased risk of recurrence. We propose a new model of the physical mechanisms involved in RFA of abdominal tumors based on Lattice Boltzmann Method to predict the extent of ablation given the probe location and the biological parameters. Our method relies on patient images, from which level set representations of liver geometry, tumor shape and vessels are extracted. Then a computational model of heat diffusion, cellular necrosis and blood flow through vessels and liver is solved to estimate the extent of ablated tissue. After quantitative verifications against an analytical solution, we apply our framework to 5 patients datasets which include pre- and post-operative CT images, yielding promising correlation between predicted and actual ablation extent (mean point to mesh errors of 8.7 mm). Implemented on graphics processing units, our method may enable RFA planning in clinical settings as it leads to near real-time computation: 1 minute of ablation is simulated in 1.14 minutes, which is almost 60x faster than standard finite element method.

3D cardiac motion reconstruction from CT data and tagged MRI.

In this paper we present a novel method for left ventricle (LV) endocardium motion reconstruction using high resolution CT data and tagged MRI. High resolution CT data provide anatomic details on the LV endocardial surface, such as the papillary muscle and trabeculae carneae. Tagged MRI provides better time resolution. The combination of these two imaging techniques can give us better understanding on left ventricle motion. The high resolution CT images are segmented with mean shift method and generate the LV endocardium mesh. The meshless deformable model built with high resolution endocardium surface from CT data fit to the tagged MRI of the same phase. 3D deformation of the myocardium is computed with the Lagrangian dynamics and local Laplacian deformation. The segmented inner surface of left ventricle is compared with the heart inner surface picture and show high agreement. The papillary muscles are attached to the inner surface with roots. The free wall of the left ventricle inner surface is covered with trabeculae carneae. The deformation of the heart wall and the papillary muscle in the first half of the cardiac cycle is presented. The motion reconstruction results are very close to the live heart video.

A workflow for patient-individualized virtual angiogram generation based on CFD simulation.

Increasing interest is drawn on hemodynamic parameters for classifying the risk of rupture as well as treatment planning of cerebral aneurysms. A proposed method to obtain quantities such as wall shear stress, pressure, and blood flow velocity is to numerically simulate the blood flow using computational fluid dynamics (CFD) methods. For the validation of those calculated quantities, virtually generated angiograms, based on the CFD results, are increasingly used for a subsequent comparison with real, acquired angiograms. For the generation of virtual angiograms, several patient-specific parameters have to be incorporated to obtain virtual angiograms which match the acquired angiograms as best as possible. For this purpose, a workflow is presented and demonstrated involving multiple phantom and patient cases.

Hemodynamic assessment of pre- and post-operative aortic coarctation from MRI.

Coarctation of the aorta (CoA), is a congenital defect characterized by a severe narrowing of the aorta, usually distal to the aortic arch. The treatment options include surgical repair, stent implantation, and balloon angioplasty. In order to evaluate the physiological significance of the pre-operative coarctation and to assess the post-operative results, the hemodynamic analysis is usually performed by measuring the pressure gradient (deltaP) across the coarctation site via invasive cardiac catheterization. The measure of success is reduction of the (deltaP > 20 mmHg) systolic blood pressure gradient. In this paper, we propose a non-invasive method based on Computational Fluid Dynamics and MR imaging to estimate the pre- and post-operative hemodynamics for both native and recurrent coarctation patients. High correlation of our results and catheter measurements is shown on corresponding pre- and post-operative examination of 5 CoA patients.

Patient-specific modelling of whole heart anatomy, dynamics and haemodynamics from four-dimensional cardiac CT images.

There is a growing need for patient-specific and holistic modelling of the heart to support comprehensive disease assessment and intervention planning as well as prediction of therapeutic outcomes. We propose a patient-specific model of the whole human heart, which integrates morphology, dynamics and haemodynamic parameters at the organ level. The modelled cardiac structures are robustly estimated from four-dimensional cardiac computed tomography (CT), including all four chambers and valves as well as the ascending aorta and pulmonary artery. The patient-specific geometry serves as an input to a three-dimensional Navier-Stokes solver that derives realistic haemodynamics, constrained by the local anatomy, along the entire heart cycle. We evaluated our framework with various heart pathologies and the results correlate with relevant literature reports.