Immersive Visualization for Enhanced Computational Fluid Dynamics Analysis

Modern biomedical computer simulations produce spatiotemporal results that are often viewed at a single point in time on standard 2D displays. An immersive visualization environment (IVE) with 3D stereoscopic capability can mitigate some shortcomings of 2D displays via improved depth cues and active movement to further appreciate the spatial localization of imaging data with temporal computational fluid dynamics (CFD) results. We present a semi-automatic workflow for the import, processing, rendering, and stereoscopic visualization of high resolution, patient-specific imaging data, and CFD results in an IVE. Versatility of the workflow is highlighted with current clinical sequelae known to be influenced by adverse hemodynamics to illustrate potential clinical utility

Patient-specific analysis of the hemodynamic performance of surgical and transcatheter aortic valve replacements

Aortic valve (AV) diseases are life-threatening conditions which affect millions of people worldwide and, if left untreated, can lead to death a few years after symptom onset. Patients affected by AV diseases are commonly referred to surgical AV replacement (SAVR). However, more than 30% of patients are not suitable for SAVR. For this reason, transcatheter aortic valve implantation (TAVI) has been attracting growing interest. Several clinical studies compared the outcomes of these techniques, showing that TAVI could be a valid alternative to SAVR. However, there is a lack of detailed knowledge about changes in the aortic hemodynamic conditions following these procedures. The main aim of this thesis is to develop efficient and robust methodologies to study and compare the influences of different AV replacement procedures on aortic hemodynamics. An image-based patient-specific computational model has been developed, which uses magnetic resonance images (MRI) acquired from patients to obtain realistic geometry and boundary conditions (BCs) for computational fluid dynamics (CFD) analysis. The implemented physiological BCs were compared with the most commonly used inlet and outlet BCs, and showed the best agreement with in vivo data. The model was then applied to study and compare SAVR, TAVI and aortic root replacement using a variety of prostheses. In addition, an experimental set-up was designed to further study TAVI hemodynamics by combining 3D-printing, 4D flow MRI and CFD. Finally, a preliminary analysis of valve leaflet thrombosis was conducted. It has been shown that both TAVI and SAVR are able to greatly improve the aortic hemodynamics, but this often deviates from conditions in healthy volunteers, with the extent of abnormalities strongly dependent on the type of prostheses or valve disease. The work also demonstrated the feasibility of predicting valve leaflet thrombosis using a shear-driven model for thrombus formation and growth.Open Acces

Use of 3D rotational angiography to perform computational fluid dynamics and virtual interventions in aortic coarctation

Computational fluid dynamics (CFD) can be used to analyze blood flow and to predict hemodynamic outcomes after interventions for coarctation of the aorta and other cardiovascular diseases. We report the first use of cardiac 3‐dimensional rotational angiography for CFD and show not only feasibility but also validation of its hemodynamic computations with catheter‐based measurements in three patients.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154333/1/ccd28507.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154333/2/ccd28507_am.pd

Fast interactive CFD evaluation of hemodynamics assisted by RBF mesh morphing and reduced order models: the case of aTAA modelling

AbstractThe medical digital twin is emerging as a viable opportunity to provide patient-specific information useful for treatment, prevention and surgical planning. A bottleneck toward its effective use when computational fluid dynamics (CFD) techniques and tools are adopted for the high fidelity prediction of blood flow, is the significant computing cost required. Reduced order models (ROM) looks to be a promising solution for facing the aforementioned limit. In fact, once ROM data processing is accomplished, the consumption stage can be performed outside the computer-aided engineering software adopted for simulation and, in addition, it could be also implemented on interactive software visualization interfaces that are commonly employed in the medical context. In this paper we demonstrate the soundness of such a concept by numerically investigating the effect of the bulge shape for the ascending thoracic aorta aneurysm case. Radial basis functions (RBF) based mesh morphing enables the implementation of a parametric shape, which is used to build up the ROM framework and data. The final result is an inspection tool capable to visualize, interactively and almost in real-time, the effect of shape parameters on the entire flow field. The approach is first verified considering a morphing action representing the progression from an average healthy patient to an average aneurismatic one (Capellini et al. in Proceedings VII Meeting Italian Chapter of the European Society of Biomechanics (ESB-ITA 2017), 2017; Capellini et al. in J. Biomech. Eng. 140(11):111007-1–111007-10, 2018). Then, a set of shape parameters, suitable to consistently represent a widespread number of possible bulge configurations, are defined and accordingly generated. The concept is showcased taking into account the steady flow field at systolic peak conditions, using ANSYS®Fluent®and its ROM environment for CFD and ROM calculations respectively, and the RBF MorphTM software for shape parametrization

Automatic segmentation of optical coherence tomography pullbacks of coronary arteries treated with bioresorbable vascular scaffolds: Application to hemodynamics modeling

Automatic algorithms for stent struts segmentation in optical coherence tomography (OCT) images of coronary arteries have been developed over the years, particularly with application on metallic stents. The aim of this study is three-fold: (1) to develop and to validate a segmentation algorithm for the detection of both lumen contours and polymeric bioresorbable scaffold struts from 8-bit OCT images, (2) to develop a method for automatic OCT pullback quality assessment, and (3) to demonstrate the applicability of the segmentation algorithm for the creation of patient-specific stented coronary artery for local hemodynamics analysis

Semi-Automatic Reconstruction of Patient-Specific Stented Coronaries based on Data Assimilation and Computer Aided Design

Purpose The interplay between geometry and hemodynamics is a significant factor in the development of cardiovascular diseases. This is particularly true for stented coronary arteries. To elucidate this factor, an accurate patient-specific analysis requires the reconstruction of the geometry following the stent deployment for a computational fluid dynamics (CFD) investigation. The image-based reconstruction is troublesome for the different possible positions of the stent struts in the lumen and the coronary wall. However, the accurate inclusion of the stent footprint in the hemodynamic analysis is critical for detecting abnormal stress conditions and flow disturbances, particularly for thick struts like in bioresorbable scaffolds. Here, we present a novel reconstruction methodology that relies on Data Assimilation and Computer Aided Design. Methods The combination of the geometrical model of the undeployed stent and image-based data assimilated by a variational approach allows the highly automated reconstruction of the skeleton of the stent. A novel approach based on computational mechanics defines the map between the intravascular frame of reference (called L-view) and the 3D geometry retrieved from angiographies. Finally, the volumetric expansion of the stent skeleton needs to be self-intersection free for the successive CFD studies; this is obtained by using implicit representations based on the definition of Nef-polyhedra. Results We assessed our approach on a vessel phantom, with less than 10% difference (properly measured) vs. a customized manual (and longer) procedure previously published, yet with a significant higher level of automation and a shorter turnaround time. Computational hemodynamics results were even closer. We tested the approach on two patient-specific cases as well. Conclusions The method presented here has a high level of automation and excellent accuracy performances, so it can be used for larger studies involving patient-specific geometries