Comparison of rhinomanometric and computational fluid dynamic assessment of nasal resistance with respect to measurement accuracy.

PURPOSE: Computational fluid dynamics (CFD)-based calculation of intranasal airflow became an important method in rhinologic research. Current evidence shows weak to moderate correlation as well as a systematic underprediction of nasal resistance by numerical simulations. In this study, we investigate whether these differences can be explained by measurement uncertainties caused by rhinomanometric devices and procedures. Furthermore, preliminary findings regarding the impact of tissue movements are reported. METHODS: A retrospective sample of 17 patients, who reported impaired nasal breathing and for which rhinomanometric (RMM) measurements using two different devices as well as computed tomography scans were available, was investigated in this study. Three patients also exhibited a marked collapse of the nasal valve. Agreement between both rhinomanometric measurements as well as between rhinomanometry and CFD-based calculations was assessed using linear correlation and Bland-Altman analyses. These analyses were performed for the volume flow rates measured at trans-nasal pressure differences of 75 and 150 Pa during inspiration and expiration. RESULTS: The correlation between volume flow rates measured using both RMM devices was good (R2 > 0.72 for all breathing states), and no relevant differences in measured flow rates was observed (21.6 ml/s and 14.8 ml/s for 75 and 150 Pa, respectively). In contrast, correlation between RMM and CFD was poor (R2 < 0.5) and CFD systematically overpredicted RMM-based flow rate measurements (231.8 ml/s and 328.3 ml/s). No differences between patients with and without nasal valve collapse nor between inspiration and expiration were observed. CONCLUSION: Biases introduced during RMM measurements, by either the chosen device, the operator or other aspects as for example the nasal cycle, are not strong enough to explain the gross differences commonly reported between RMM- and CFD-based measurement of nasal resistance. Additionally, tissue movement during breathing is most likely also no sufficient explanation for these differences.

CARDIOKIN1: Computational Assessment of Myocardial Metabolic Capability in Healthy Controls and Patients With Valve Diseases.

BACKGROUND: Many heart diseases can result in reduced pumping capacity of the heart muscle. A mismatch between ATP demand and ATP production of cardiomyocytes is one of the possible causes. Assessment of the relation between myocardial ATP production (MVATP) and cardiac workload is important for better understanding disease development and choice of nutritional or pharmacologic treatment strategies. Because there is no method for measuring MVATP in vivo, the use of physiology-based metabolic models in conjunction with protein abundance data is an attractive approach. METHOD: We developed a comprehensive kinetic model of cardiac energy metabolism (CARDIOKIN1) that recapitulates numerous experimental findings on cardiac metabolism obtained with isolated cardiomyocytes, perfused animal hearts, and in vivo studies with humans. We used the model to assess the energy status of the left ventricle of healthy participants and patients with aortic stenosis and mitral valve insufficiency. Maximal enzyme activities were individually scaled by means of protein abundances in left ventricle tissue samples. The energy status of the left ventricle was quantified by the ATP consumption at rest (MVATP[rest]), at maximal workload (MVATP[max]), and by the myocardial ATP production reserve, representing the span between MVATP(rest) and MVATP(max). RESULTS: Compared with controls, in both groups of patients, MVATP(rest) was increased and MVATP(max) was decreased, resulting in a decreased myocardial ATP production reserve, although all patients had preserved ejection fraction. The variance of the energetic status was high, ranging from decreased to normal values. In both patient groups, the energetic status was tightly associated with mechanic energy demand. A decrease of MVATP(max) was associated with a decrease of the cardiac output, indicating that cardiac functionality and energetic performance of the ventricle are closely coupled. CONCLUSIONS: Our analysis suggests that the ATP-producing capacity of the left ventricle of patients with valvular dysfunction is generally diminished and correlates positively with mechanical energy demand and cardiac output. However, large differences exist in the energetic state of the myocardium even in patients with similar clinical or image-based markers of hypertrophy and pump function. Registration: URL: https://www.clinicaltrials.gov; Unique identifiers: NCT03172338 and NCT04068740.

Abnormal aortic flow profiles persist after aortic valve replacement in the majority of patients with aortic valve disease: how model-based personalized therapy planning could improve results. A pilot study approach.

OBJECTIVES: Complex blood flow profiles in the aorta are known to contribute to vessel dilatation. We studied flow profiles in the aorta in patients with aortic valve disease before and after surgical aortic valve replacement (AVR). METHODS: Thirty-four patients with aortic valve disease underwent 4-dimensional velocity-encoded magnetic resonance imaging before and after AVR (biological valve = 27, mechanical valve = 7). Seven healthy volunteers served as controls. Eccentricity (ES) and complex flow scores (CFS) were determined from the degree of helicity, vorticity and eccentricity of flow profiles in the aorta. Model-based therapy planning was used in 4 cases to improve in silico postoperative flow profiles by personalized adjustment of size, rotation and angulation of the valve as well as aorta diameter. RESULTS: Patients with aortic valve disease showed more complex flow than controls [median ES 2.5 (interquartile range (IQR) 2.3-2.7) vs 1.0 (IQR 1.0-1.0), P < 0.001, median CFS 4.7 (IQR 4.3-4.8) vs 1.0 (IQR 1.0-2.0), P < 0.001]. After surgery, flow complexity in the total patient cohort was reduced, but remained significantly higher compared to controls [median ES 2.3 (IQR 1.9-2.3) vs 1.0 (IQR 1.0-1.0), P < 0.001, median CFS 3.8 (IQR 3.0-4.3) vs 1.0 (IQR 1.0-2.0), P < 0.001]. In patients after mechanical AVR, flow complexity fell substantially and showed no difference from controls [median ES 1.0 (IQR 1.0-2.3) vs 1.0 (IQR 1.0-1.0), P = 0.46, median CFS 1.0 (IQR 1.0-3.3) vs 1.0 (IQR 1.0-2.0), P = 0.71]. In all 4 selected cases (biological, n = 2; mechanical, n = 2), model-based therapy planning reduced in silico complexity of flow profiles compared to the existing post-surgical findings [median ES 1.7 (IQR 1.4-1.7) vs 2.3 (IQR 2.3-2.3); CFS 1.7 (IQR 1.4-2.5) vs 3.8 (IQR 3.3-4.3)]. CONCLUSIONS: Abnormal flow profiles in the aorta more frequently persist after surgical AVR. Model-based therapy planning might have the potential to optimize treatment for best possible individual outcome. CLINICAL TRIAL REGISTRATION NUMBER: clinicaltrials.gov NCT03172338, 1 June 2017, retrospectively registered; NCT02591940, 30 October 2015, retrospectively registered.

Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease.

Computational modelling has made significant progress towards clinical application in recent years. In addition to providing detailed diagnostic data, these methods have the potential to simulate patient-specific interventions and to predict their outcome. Our objective was to evaluate to which extent patient-specific modelling influences treatment decisions in coarctation of the aorta (CoA), a common congenital heart disease. We selected three cases with CoA, two of which had borderline indications for intervention according to current clinical guidelines. The third case was not indicated for intervention according to guidelines. For each case, we generated two separate datasets. First dataset included conventional diagnostic parameters (echocardiography and magnetic resonance imaging). In the second, we added modelled parameters (pressure fields). For the two cases with borderline indications for intervention, the second dataset also included pressure fields after virtual stenting simulations. All parameters were computed by modelling methods that were previously validated. In an online-administered, invitation-only survey, we randomized 178 paediatric cardiologists to view either conventional (control) or add-on modelling (experimental) datasets. Primary endpoint was the proportion of participants recommending different therapeutic options: (1) surgery or catheter lab (collectively, "intervention") or (2) no intervention (follow-up with or without medication). Availability of data from computational predictive modelling influenced therapeutic decision making in two of three cases. There was a statistically significant association between group assignment and the recommendation of an intervention for one borderline case and one non-borderline case: 94.3% vs. 72.2% (RR: 1.31, 95% CI: 1.14-1.50, p = 0.00) and 18.8% vs. 5.1% (RR: 3.09, 95% CI: 1.17-8.18, p = 0.01) of participants in the experimental and control groups respectively recommended an intervention. For the remaining case, there was no difference between the experimental and control group and the majority of participants recommended intervention. In sub-group analyses, findings were not affected by the experience level of participating cardiologists. Despite existing clinical guidelines, the therapy recommendations of the participating physicians were heterogeneous. Validated patient-specific computational modelling has the potential to influence treatment decisions. Future studies in broader areas are needed to evaluate whether differences in decisions result in improved outcomes (Trial Registration: NCT02700737).

User-dependent variability in mitral valve segmentation and its impact on CFD-computed hemodynamic parameters.

PURPOSE: While novel tools for segmentation of the mitral valve are often based on automatic image processing, they mostly require manual interaction by a proficient user. Those segmentations are essential for numerical support of mitral valve treatment using computational fluid dynamics, where the reconstructed geometry is incorporated into a simulation domain. To quantify the uncertainty and reliability of hemodynamic simulations, it is crucial to examine the influence of user-dependent variability in valve segmentation. METHODS: Previously, the inter-user variability of landmarks in mitral valve segmentation was investigated. Here, the inter-user variability of geometric parameters of the mitral valve, projected orifice area (OA) and projected annulus area (AA), is investigated for 10 mitral valve geometries, each segmented by three users. Furthermore, the propagation of those variations into numerically calculated hemodynamics, i.e., the blood flow velocity, was investigated. RESULTS: Among the three geometric valve parameters, AA was least user-dependent. Almost all deviations to the mean were below 10%. Larger variations were observed for OA. Variations observed for the numerically calculated hemodynamics were in the same order of magnitude as those of geometric parameters. No correlation between variation of geometric parameters and variation of calculated hemodynamic parameters was found. CONCLUSION: Errors introduced due to the user-dependency were of the same size as the variations of calculated hemodynamics. The variation was thereby of the same scale as deviations in clinical measurements of blood flow velocity using Doppler echocardiography. Since no correlation between geometric and hemodynamic uncertainty was found, further investigation of the complex relationship between anatomy, leaflet shape and flow is necessary.

Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH)-phase II: rupture risk assessment.

PURPOSE: Assessing the rupture probability of intracranial aneurysms (IAs) remains challenging. Therefore, hemodynamic simulations are increasingly applied toward supporting physicians during treatment planning. However, due to several assumptions, the clinical acceptance of these methods remains limited. METHODS: To provide an overview of state-of-the-art blood flow simulation capabilities, the Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH) was conducted. Seventeen research groups from all over the world performed segmentations and hemodynamic simulations to identify the ruptured aneurysm in a patient harboring five IAs. Although simulation setups revealed good similarity, clear differences exist with respect to the analysis of aneurysm shape and blood flow results. Most groups (12/71%) included morphological and hemodynamic parameters in their analysis, with aspect ratio and wall shear stress as the most popular candidates, respectively. RESULTS: The majority of groups (7/41%) selected the largest aneurysm as being the ruptured one. Four (24%) of the participating groups were able to correctly select the ruptured aneurysm, while three groups (18%) ranked the ruptured aneurysm as the second most probable. Successful selections were based on the integration of clinically relevant information such as the aneurysm site, as well as advanced rupture probability models considering multiple parameters. Additionally, flow characteristics such as the quantification of inflow jets and the identification of multiple vortices led to correct predictions. CONCLUSIONS: MATCH compares state-of-the-art image-based blood flow simulation approaches to assess the rupture risk of IAs. Furthermore, this challenge highlights the importance of multivariate analyses by combining clinically relevant metadata with advanced morphological and hemodynamic quantification.

Digital Analysis of Nasal Airflow Facilitating Decision Support in Rhinosurgery.

Successful functional surgery on the nasal framework requires reliable and comprehensive diagnosis. In this regard, the authors introduce a new methodology: Digital Analysis of Nasal Airflow (diANA). It is based on computational fluid dynamics, a statistical shape model of the healthy nasal cavity and rhinologic expertise. diANA necessitates an anonymized tomographic dataset of the paranasal sinuses including the complete nasal cavity and, when available, clinical information. The principle of diANA is to compare the morphology and the respective airflow of an individual nose with those of a reference. This enables morphometric aberrations and consecutive flow field anomalies to localize and quantify within a patient's nasal cavity. Finally, an elaborated expert opinion with instructive visualizations is provided. Using diANA might support surgeons in decision-making, avoiding unnecessary surgery, gaining more precision, and target-orientation for indicated operations.

The Healthy Nasal Cavity-Characteristics of Morphology and Related Airflow Based on a Statistical Shape Model Viewed from a Surgeon's Perspective.

Functional surgery on the nasal framework requires referential criteria to objectively assess nasal breathing for indication and follow-up. This motivated us to generate a mean geometry of the nasal cavity based on a statistical shape model. In this study, the authors could demonstrate that the introduced nasal cavity's mean geometry features characteristics of the inner shape and airflow, which are commonly observed in symptom-free subjects. Therefore, the mean geometry might serve as a reference-like model when one considers qualitative aspects. However, to facilitate quantitative considerations and statistical inference, further research is necessary. Additionally, the authors were able to obtain details about the importance of the isthmus nasi and the inferior turbinate for the intranasal airstream.

Virtual downsizing for decision support in mitral valve repair.

PURPOSE: Various options are available for the treatment of mitral valve insufficiency, including reconstructive approaches such as annulus correction through ring implants. The correct choice of general therapy and implant is relevant for an optimal outcome. Additional to guidelines, decision support systems (DSS) can provide decision aid by means of virtual intervention planning and predictive simulations. Our approach on virtual downsizing is one of the virtual intervention tools that are part of the DSS workflow. It allows for emulating a ring implantation based on patient-specific lumen geometry and vendor-specific implants. METHODS: Our approach is fully automatic and relies on a lumen mask and an annulus contour as inputs. Both are acquired from previous DSS workflow steps. A virtual surface- and contour-based model of a vendor-specific ring design (26-40 mm) is generated. For each case, the ring geometry is positioned with respect to the original, patient-specific annulus and additional anatomical landmarks. The lumen mesh is parameterized to allow for a vertex-based deformation with respect to the user-defined annulus. Derived from post-interventional observations, specific deformation schemes are applied to atrium and ventricle and the lumen mesh is altered with respect to the ring location. RESULTS: For quantitative evaluation, the surface distance between the deformed lumen mesh and segmented post-operative echo lumen close to the annulus was computed for 11 datasets. The results indicate a good agreement. An arbitrary subset of six datasets was used for a qualitative evaluation of the complete lumen. Two domain experts compared the deformed lumen mesh with post-interventional echo images. All deformations were deemed plausible. CONCLUSION: Our approach on virtual downsizing allows for an automatic creation of plausible lumen deformations. As it takes only a few seconds to generate results, it can be added to a virtual intervention toolset without unnecessarily increasing the pipeline complexity.

Extraction of open-state mitral valve geometry from CT volumes.

PURPOSE: The importance of mitral valve therapies is rising due to an aging population. Visualization and quantification of the valve anatomy from image acquisitions is an essential component of surgical and interventional planning. The segmentation of the mitral valve from computed tomography (CT) acquisitions is challenging due to high variation in appearance and visibility across subjects. We present a novel semi-automatic approach to segment the open-state valve in 3D CT volumes that combines user-defined landmarks to an initial valve model which is automatically adapted to the image information, even if the image data provide only partial visibility of the valve. METHODS: Context information and automatic view initialization are derived from segmentation of the left heart lumina, which incorporates topological, shape and regional information. The valve model is initialized with user-defined landmarks in views generated from the context segmentation and then adapted to the image data in an active surface approach guided by landmarks derived from sheetness analysis. The resulting model is refined by user landmarks. RESULTS: For evaluation, three clinicians segmented the open valve in 10 CT volumes of patients with mitral valve insufficiency. Despite notable differences in landmark definition, the resulting valve meshes were overall similar in appearance, with a mean surface distance of [Formula: see text] mm. Each volume could be segmented in 5-22 min. CONCLUSIONS: Our approach enables an expert user to easily segment the open mitral valve in CT data, even when image noise or low contrast limits the visibility of the valve.

Development of a modeling pipeline for the prediction of hemodynamic outcome after virtual mitral valve repair using image-based CFD.

PURPOSE: Severe mitral valve regurgitation can either be treated by a replacement or a repair of the valve. The latter is recommended due to lower perioperative mortality and better long-term survival. On the other hand, recurrence rates after mitral valve repair are high compared to those after replacements and the repair intervention can cause induced mitral valve stenosis. So far, there are no methods to predict the hemodynamic outcome of a chosen treatment or to compare different treatment options in advance. To overcome this, diastolic mitral valve hemodynamics are simulated using computational fluid dynamics after different virtual treatments of the valve. METHODS: The left ventricular geometry of one patient was reconstructed using trans-esophageal echocardiography and computed tomography data. Pre-op hemodynamics are simulated using a referenced wall model to avoid expansive modeling of wall motion. Subsequently, the flow structures are compared to in vivo measurements. After manipulating the patient-specific geometry in order to mimic a restrictive mitral annuloplasty as well as a MitraClip intervention, hemodynamics results are calculated. RESULTS: Good agreements exist between calculated pre-op hemodynamics and in vivo measurements. The virtual annuloplasty did not result in any remarkable change of hemodynamics. Neither the pressure drop nor the velocity field showed strong differences. In contrast, the virtual MitraClip intervention led to a complete change in blood flow structures as well as an elevated pressure drop across the valve. CONCLUSION: The presented approach allows fast simulation of the diastolic hemodynamic situation before and after treatment of a mitral valve insufficiency. However, this approach is limited to the early diastolic phase of the cardiac cycle and needs to be validated using a larger sample size.

Simulation, identification and statistical variation in cardiovascular analysis (SISCA) - A software framework for multi-compartment lumped modeling.

It has not yet been possible to obtain modeling approaches suitable for covering a wide range of real world scenarios in cardiovascular physiology because many of the system parameters are uncertain or even unknown. Natural variability and statistical variation of cardiovascular system parameters in healthy and diseased conditions are characteristic features for understanding cardiovascular diseases in more detail. This paper presents SISCA, a novel software framework for cardiovascular system modeling and its MATLAB implementation. The framework defines a multi-model statistical ensemble approach for dimension reduced, multi-compartment models and focuses on statistical variation, system identification and patient-specific simulation based on clinical data. We also discuss a data-driven modeling scenario as a use case example. The regarded dataset originated from routine clinical examinations and comprised typical pre and post surgery clinical data from a patient diagnosed with coarctation of aorta. We conducted patient and disease specific pre/post surgery modeling by adapting a validated nominal multi-compartment model with respect to structure and parametrization using metadata and MRI geometry. In both models, the simulation reproduced measured pressures and flows fairly well with respect to stenosis and stent treatment and by pre-treatment cross stenosis phase shift of the pulse wave. However, with post-treatment data showing unrealistic phase shifts and other more obvious inconsistencies within the dataset, the methods and results we present suggest that conditioning and uncertainty management of routine clinical data sets needs significantly more attention to obtain reasonable results in patient-specific cardiovascular modeling.

Interactive virtual stent planning for the treatment of coarctation of the aorta.

PURPOSE: The coarctation of the aorta (CoA), a local narrowing of the aortic arch, accounts for 7 % of all congenital heart defects. Stenting is a recommended therapy to reduce the pressure gradient. This procedure is associated with complications such as the development of adverse flow conditions. A computer-aided treatment planning based on flow simulations can help to predict possible complications. The virtual stent planning is an important, intermediate step in the treatment planning pipeline. We present a novel approach that automatically suggests a stent setup and provides a set of intuitive parameters that allow for an interactive adaption of the suggested stent placement and induced deformation. METHODS: A high-quality mesh and a centerline are automatically generated. The stent-induced deformation is realized through a deformation of the centerline and a vertex displacement with respect to the deformed centerline and additional stent parameters. The parameterization is automatically derived from the underlying data and can be optionally altered through a condensed set of clinically sound parameters. RESULTS: The automatic deformation can be generated in about 25 s on a consumer system. The interactive adaption can be performed in real time. Compared with manual expert reconstructions of the stented vessel section, the mean difference of vessel path and diameter is below 1 mm. CONCLUSION: Our approach enables a medical user to easily generate a plausibly deformed vessel mesh which is necessary as input for a simulation-based treatment planning of CoA.

Hemodynamic and energetic aspects of the left ventricle in patients with mitral regurgitation before and after mitral valve surgery.

BACKGROUND: The role of intracardiac blood flow behavior within the context of manifestation and interventional success in patients with mitral regurgitation is unknown to date. The present study aims to assess left ventricular blood flow behavior characterized by kinetic energy (KE) in patients with mitral regurgitation before and after mitral valve surgery. METHODS: Patients with mitral regurgitation (mean age 56 ± 9 years) and the necessity for mitral valve repair (n = 6) or biological valve replacement (n = 4) received cardiac magnetic resonance before and after surgery and were compared with a group of healthy volunteers (n = 7; mean age 27 ± 7 years). Volumetric data and KE of the left ventricle were obtained for all subjects. KE normalized and nonnormalized to volume was calculated from four-dimensional flow magnetic resonance imaging. Mean KE and KE peaks (systolic, early-diastolic and late diastolic), and end-systolic phase duration were considered. RESULTS: End-diastolic, end-systolic and stroke volume were significantly higher in patients with mitral regurgitation than in healthy volunteers (P = 0.00, 0.01, and 0.00, respectively) and decreased significantly after surgery (P = 0.00, 0.01, and 0.00, respectively). A significant postoperative decrease of mean KE, systolic and early-diastolic KE peaks was observed (P = 0.01, 0.02, and 0.01, respectively). Late-diastolic KE peak remained high in postoperative patients (P = 0.58). CONCLUSION: Intracardiac blood flow as characterized by measurements of KE is altered in patients with mitral regurgitation. Physiological flow conditions appear to not fully be restored with mitral valve surgery.

The concept of rhinorespiratory homeostasis--a new approach to nasal breathing.

The suggested concept of rhinorespiratory homeostasis is a new theoretical model for the discussion of physiologic and physical principles of nasal breathing. This model is based on a comprehensive view of nasal functions that takes comparative animal physiology into account. Consequently, it has a universal cross-species character and emphasizes the central role of nasal secretion. In contrast to the established view, the focus is transferred from the inspired air to the nasal wall. This concept considers the parietal effect of airflow represented by wall shear stress with special regard to the epithelial lining fluid. It delivers one possible mechanism of an inherent triggering of the nasal cycle. Furthermore, the issue of biological fluid-structure interaction is introduced. This article presents a rethinking of nasal breathing that was inspired by clinical experience and results of flow field investigations through computational fluid dynamics.

Evaluation of the intranasal flow field through computational fluid dynamics.

A reliable and comprehensive assessment of nasal breathing is problematic and still a common issue in rhinosurgery. Impairments of nasal breathing need an objective approach. In this regard, currently rhinomanometry is the only standard diagnostic tool available but has various limitations. However, in the last decade, computational fluid dynamics (CFD) has become a promising method in facing the challenge of qualifying nasal breathing. This article presents use of CFD with a symptom-free subject and a symptomatic patient. Thereby, certain flow field features and changes before and after surgery were investigated. Moreover, the study outlines suggestions for concrete rhinologic CFD applications.