Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time.

BACKGROUND: Advances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired. AIM: The aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve. METHODS: Principal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a meta-model. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop. RESULTS: Even though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm2 . The proposed methodology was least accurate for aortic valve areas above 150 mm2 . Further reduction to a meta-model introduces an additional 3% error. CONCLUSIONS: Statistical shape modeling can be used to capture shape variation of the aortic valve. Meta-models trained by SSM-based CFD simulations can provide an estimate of the pressure-flow relationship in real-time.

Towards improving the accuracy of aortic transvalvular pressure gradients: rethinking Bernoulli.

The transvalvular pressure gradient (TPG) is commonly estimated using the Bernoulli equation. However, the method is known to be inaccurate. Therefore, an adjusted Bernoulli model for accurate TPG assessment was developed and evaluated. Numerical simulations were used to calculate TPGCFD in patient-specific geometries of aortic stenosis as ground truth. Geometries, aortic valve areas (AVA), and flow rates were derived from computed tomography scans. Simulations were divided in a training data set (135 cases) and a test data set (36 cases). The training data was used to fit an adjusted Bernoulli model as a function of AVA and flow rate. The model-predicted TPGModel was evaluated using the test data set and also compared against the common Bernoulli equation (TPGB). TPGB and TPGModel both correlated well with TPGCFD (r > 0.94), but significantly overestimated it. The average difference between TPGModel and TPGCFD was much lower: 3.3 mmHg vs. 17.3 mmHg between TPGB and TPGCFD. Also, the standard error of estimate was lower for the adjusted model: SEEModel = 5.3 mmHg vs. SEEB = 22.3 mmHg. The adjusted model's performance was more accurate than that of the conventional Bernoulli equation. The model might help to improve non-invasive assessment of TPG. Graphical abstract Processing pipeline for the definition of an adjusted Bernoulli model for the assessment of transvalvular pressure gradient. Using CT image data, the patient specific geometry of the stenosed AVs were reconstructed. Using this segmentation, the AVA as well as the volume flow rate was calculated and used for model definition. This novel model was compared against classical approaches on a test data set, which was not used for the model definition.

Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics.

Aortic valve stenosis is associated with an elevated left ventricular pressure and transaortic pressure drop. Clinicians routinely use Doppler ultrasound to quantify aortic valve stenosis severity by estimating this pressure drop from blood velocity. However, this method approximates the peak pressure drop, and is unable to quantify the partial pressure recovery distal to the valve. As pressure drops are flow dependent, it remains difficult to assess the true significance of a stenosis for low-flow low-gradient patients. Recent advances in segmentation techniques enable patient-specific Computational Fluid Dynamics (CFD) simulations of flow through the aortic valve. In this work a simulation framework is presented and used to analyze data of 18 patients. The ventricle and valve are reconstructed from 4D Computed Tomography imaging data. Ventricular motion is extracted from the medical images and used to model ventricular contraction and corresponding blood flow through the valve. Simplifications of the framework are assessed by introducing two simplified CFD models: a truncated time-dependent and a steady-state model. Model simplifications are justified for cases where the simulated pressure drop is above 10 mmHg. Furthermore, we propose a valve resistance index to quantify stenosis severity from simulation results. This index is compared to established metrics for clinical decision making, i.e. blood velocity and valve area. It is found that velocity measurements alone do not adequately reflect stenosis severity. This work demonstrates that combining 4D imaging data and CFD has the potential to provide a physiologically relevant diagnostic metric to quantify aortic valve stenosis severity.

A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database.

Quantification of cardiac deformation and strain with 3D ultrasound takes considerable research efforts. Nevertheless, a widespread use of these techniques in clinical practice is still held back due to the lack of a solid verification process to quantify and compare performance. In this context, the use of fully synthetic sequences has become an established tool for initial in silico evaluation. Nevertheless, the realism of existing simulation techniques is still too limited to represent reliable benchmarking data. Moreover, the fact that different centers typically make use of in-house developed simulation pipelines makes a fair comparison difficult. In this context, this paper introduces a novel pipeline for the generation of synthetic 3D cardiac ultrasound image sequences. State-of-the art solutions in the fields of electromechanical modeling and ultrasound simulation are combined within an original framework that exploits a real ultrasound recording to learn and simulate realistic speckle textures. The simulated images show typical artifacts that make motion tracking in ultrasound challenging. The ground-truth displacement field is available voxelwise and is fully controlled by the electromechanical model. By progressively modifying mechanical and ultrasound parameters, the sensitivity of 3D strain algorithms to pathology and image properties can be evaluated. The proposed pipeline is used to generate an initial library of 8 sequences including healthy and pathological cases, which is made freely accessible to the research community via our project web-page.

Chromosome-based vectors for gene therapy.

Currently used vectors in human gene therapy suffer from a number of limitations with respect to safety and reproducibility. There is increasing agreement that the ideal vector for gene therapy should be completely based on chromosomal elements and behave as an independent functional unit after integration into the genome or when retained as an episome. In this review we will first discuss the chromosomal elements, such as enhancers, locus control regions, boundary elements, insulators and scaffold- or matrix-attachment regions, involved in the hierarchic regulation of mammalian gene expression and replication. These elements have been used to design vectors that behave as artificial domains when integrating into the genome. We then discuss recent progress in the use of mammalian artificial chromosomes and small circular non-viral vectors for their use as expression systems in mammalian cells.

Targeting properties of an anti-CD16/anti-CD30 bispecific antibody in an in vivo system.

Bispecific antibodies are currently being used in clinical trials in increasing numbers in the areas of breast cancer, prostate cancer, non-Hodgkin's lymphoma and Hodgkin's lymphoma. We have previously performed two clinical trials in patients with Hodgkin's disease with an anti-CD30/anti-CD16 bispecific antibody and demonstrated a 30% response rate in a cohort of patients otherwise resistant to standard therapeutic modalities. However, no surrogate marker could be defined in these trials indicative of optimal antibody dosing/scheduling or predictive for favorable response. In order to evaluate accurately the potential biodistribution properties of bispecific antibody in patients, we have performed a detailed analysis of the binding properties and animal model in vivo characteristics of these constructs. For this purpose, the parental antibodies (anti-CD30 and anti-CD16) and the bispecific antibody (anti-CD30/anti-CD16) were radiolabeled with either 125I or 111In. Antibody integrity and binding properties after labeling were confirmed by Scatchard plot and Lindmo analysis. 111In-labeled antibodies revealed superior targeting properties in a standard SCID mouse tumor model. Both the bivalent parental anti-CD30 monoclonal antibody and the monovalent anti-CD30/anti-CD16 bispecific antibody showed excellent uptake in CD30+ tumors which did not differ significantly between the two (maximum uptake 16.5%+/-4.2% vs. 18.4%+/-3.8% injected dose/gram tissue). The equivalent targeting properties of the bispecific antibody compared with the parental anti-CD30 antibody encourages the further clinical development of this bispecific antibody, and might help to explain the clinical responses seen with this antibody so far in patients suffering from Hodgkin's disease.

Faculty mentoring programme--ways of reducing anonymity.

Increasing anonymity and the lack of personal contact between professor and student due to high enrolment is one of the major problems at German universities. Therefore, the Faculty of Medicine of the University of the Saarland started a pilot project in 1995. The intention was to reduce anonymity within the faculty by introducing a faculty mentoring programme (Studientutorium). A group of up to 12 students from different years is allocated a faculty member on a personal basis. The member responds to questions and problems concerning such areas as the curriculum, career planning or doctoral thesis as well as to personal problems. After 2 years participants were interviewed about the programme for the first time and the acceptance turned out to be high.