MRI-based computational hemodynamics in patients with aortic coarctation using the lattice Boltzmann methods : Clinical validation study

PURPOSE: To introduce a scheme based on a recent technique in computational hemodynamics, known as the lattice Boltzmann methods (LBM), to noninvasively measure pressure gradients in patients with a coarctation of the aorta (CoA). To provide evidence on the accuracy of the proposed scheme, the computed pressure drop values are compared against those obtained using the reference standard method of catheterization. MATERIALS AND METHODS: Pre‐ and posttreatment LBM‐based pressure gradients for 12 patients with CoA were simulated for the time point of peak systole using the open source library OpenLB. Four‐dimensional (4D) flow‐sensitive phase‐contrast MRI at 1.5 Tesla was used to acquire flow and to setup the simulation. The vascular geometry was reconstructed using 3D whole‐heart MRI. Patients underwent pre‐ and postinterventional pressure catheterization as a reference standard. RESULTS: There is a significant linear correlation between the pretreatment catheter pressure drops and those computed based on the LBM simulation, [Formula: see text] , [Formula: see text]. The bias was ‐0.58 ± 4.1 mmHg and was not significant ( [Formula: see text] with a 95% confidence interval (CI) of ‐3.22 to 2.06. For the posttreatment results, the bias was larger and at ‐2.54 ± 3.53 mmHg with a 95% CI of ‐0.17 to ‐4.91 mmHg. CONCLUSION: The results indicate a reasonable agreement between the simulation results and the catheter measurements. LBM‐based computational hemodynamics can be considered as an alternative to more traditional computational fluid dynamics schemes for noninvasive pressure calculations and can assist in diagnosis and therapy planning. Level of Evidence: 3 J. Magn. Reson. Imaging 2017;45:139–146

Translating computational modelling tools for clinical practice in congenital heart disease

Increasingly large numbers of medical centres worldwide are equipped with the means to acquire 3D images of patients by utilising magnetic resonance (MR) or computed tomography (CT) scanners. The interpretation of patient 3D image data has significant implications on clinical decision-making and treatment planning. In their raw form, MR and CT images have become critical in routine practice. However, in congenital heart disease (CHD), lesions are often anatomically and physiologically complex. In many cases, 3D imaging alone can fail to provide conclusive information for the clinical team. In the past 20-30 years, several image-derived modelling applications have shown major advancements. Tools such as computational fluid dynamics (CFD) and virtual reality (VR) have successfully demonstrated valuable uses in the management of CHD. However, due to current software limitations, these applications have remained largely isolated to research settings, and have yet to become part of clinical practice. The overall aim of this project was to explore new routes for making conventional computational modelling software more accessible for CHD clinics. The first objective was to create an automatic and fast pipeline for performing vascular CFD simulations. By leveraging machine learning, a solution was built using synthetically generated aortic anatomies, and was seen to be able to predict 3D aortic pressure and velocity flow fields with comparable accuracy to conventional CFD. The second objective was to design a virtual reality (VR) application tailored for supporting the surgical planning and teaching of CHD. The solution was a Unity-based application which included numerous specialised tools, such as mesh-editing features and online networking for group learning. Overall, the outcomes of this ongoing project showed strong indications that the integration of VR and CFD into clinical settings is possible, and has potential for extending 3D imaging and supporting the diagnosis, management and teaching of CHD

Aortic dissection: simulation tools for disease management and understanding

Aortic dissection is a severe cardiovascular pathology in which a tear in the intimal layer of the aortic wall allows blood to flow between the vessel wall layers, forming a 'false lumen'. In type-B aortic dissections, those involving only the descending aorta, the decision to medically manage or surgically intervene is not clear and is highly dependent on the patient. In addition to clinical imaging data, clinicians would benefit greatly from additional physiological data to inform their decision-making process. Computational fluid dynamics methods show promise for providing data on haemodynamic parameters in cardiovascular diseases, which cannot otherwise be predicted or safely measured. The assumptions made in the development of such models have a considerable impact on the accuracy of the results, and thus require careful investigation. Application of appropriate boundary conditions is a challenging but critical component of such models. In the present study, imaging data and invasive pressure measurements from a patient with a type-B aortic dissection were used to assist numerical modelling of the haemodynamics in a dissected aorta. A technique for tuning parameters for coupled Windkessel models was developed and evaluated. Two virtual treatments were modelled and analysed using the developed dynamic boundary conditions. Finally, the influence of wall motion was considered, of which the intimal flap that separates the false lumen from the true lumen, is of particular interest. The present results indicate that dynamic boundary conditions are necessary in order to achieve physiologically meaningful flows and pressures at the boundaries, and hence within the dissected aorta. Additionally, wall motion is of particular importance in the closed regions of the false lumen, wherein rigid wall simulations fail to capture the motion of the fluid due to the elasticity of the vessel wall and intimal flap

Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie - klinische Anwendung bildbasierter in silico Modellierung

Die richtige Therapie zum richtigen Zeitpunkt, nichtinvasiv und patientenindividuell zu identifizieren, ist das Ziel der Präzisionsmedizin. Durch den stetigen Fortschritt sowohl im Bereich der Bildgebung als auch in mathematischen Modellierungstechniken sowie einer zunehmenden Verfügbarkeit von leistungsstarker Informationstechnologie, gewinnen in silico (angelehnt an das Lateinische „in silicio“, also „in silicium“ bzw. im übertragenden Sinne im Computer ablaufende) Modellierungsverfahren eine immer größere Bedeutung auch im Bereich der kardiovaskulären Medizin. Die bildbasierte in silico Modellierung von Hämodynamik und Funktion des Herzens kann dabei einerseits helfen, die diagnostische Aussagekraft unterschiedlicher Bildgebungsmodalitäten zu erweitern, andererseits aber auch, verschiedene Parameter der postinterventionellen bzw. postoperativen Funktion vorherzusagen und so das geeignetste patientenindividuelle Therapieverfahren zu identifizieren. Im Bereich der pädiatrischen Kardiologie, insbesondere bei Patient*innen mit komplexen angeborenen Herzfehlern, ist eine individualisierte Therapieplanung zudem von ganz besonderer Bedeutung. Da die Anatomie des kardiovaskulären Systems in diesem Patientenkollektiv hoch individuell ist, gibt es häufig keine für das jeweilige Krankheitsbild einheitliche Therapie. Die virtuelle Behandlungsplanung bietet hier ein großes Potential für die multimodale Therapiefindung. Die Translation solcher Modellierungsansätze in die Klinik stellt jedoch eine große Hürde dar. Einerseits muss die Genauigkeit der jeweiligen Simulationsmethode quantifiziert und die Methode selbst validiert werden. Dafür benötigt es in der Regel eine hohe Anzahl an Patientendaten, die insbesondere in der Kinderkardiologie, aber auch aufgrund zunehmend strengerer Datenschutzrichtlinien häufig nicht zur Verfügung stehen. Andererseits sind die Simulationsverfahren sehr komplex und verlangen neben einer hohen technischen Expertise auch beachtliche Rechenkapazitäten und -laufzeiten, wodurch sich ihr routinemäßiger Einsatz in der Klinik ebenfalls verkompliziert. Das Problem der hohen Komplexität könnte durch den Einsatz künstlicher Intelligenz (KI) überwunden werden. Fehlende klinische Daten wiederum könnten mittels synthetischer Patientenkohorten augmentiert werden, sodass sowohl für mögliche Validierungsstudien als auch zum Trainieren des maschinellen Algorithmus‘ ein ausreichend großer Datensatz zur Verfügung stünde. In der vorliegenden Habilitationsschrift werden die Inhalte von fünf wissenschaftlichen Arbeiten zum Thema Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie auf Grundlage bildbasierter in silico Modellierung vorgestellt. Dabei wird in Form einer Proof of Concept Studie die prinzipielle Durchführbarkeit der bildbasierten in silico Modellierung am Beispiel verschiedener Parameter der aortalen Hämodynamik gezeigt sowie die Validierung der Methodik gegen den klinischen Goldstandard des Herzkatheters präsentiert. An komplexen Patient*innen aus dem Bereich der Kinderkardiologie wird die bildbasierte in silico Modellierung für eine konkrete klinische Fragestellung angewandt. Zuletzt werden zwei Optimierungsansätze vorgestellt, die einerseits den komplexen Arbeitsablauf der bildbasierten in silico Modellierung mittels KI vereinfachen sowie andererseits das Problem der existierenden klinischen Datenlücken überwinden sollen

Computational fluid dynamics modelling in cardiovascular medicine

This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length-And time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, populationaveraged data. Model integration is progressively moving towards 'digital patient' or 'virtual physiological human' representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education-And service-related challenges

Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine

Introduction: The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS). Methods: A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods. Results: ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice