We present a modeling framework designed for patient-specific computational hemodynamics to be performed in the context of large-scale studies. The framework takes advantage of the integration of image processing, geometric analysis and mesh generation techniques, with an accent on full automation and high-level interaction. Image segmentation is performed using implicit deformable models taking advantage of a novel approach for selective initialization of vascular branches, as well as of a strategy for the segmentation of small vessels. A robust definition of centerlines provides objective geometric criteria for the automation of surface editing and mesh generation. The framework is available as part of an open-source effort, the Vascular Modeling Toolkit, a first step towards the sharing of tools and data which will be necessary for computational hemodynamics to play a role in evidence-based medicine.

There is well-documented evidence that vascular geometry has a major impact in blood flow dynamics and consequently in the development of vascular diseases, like atherosclerosis and cerebral aneurysmal disease. The study of vascular geometry and the identification of geometric features associated with a specific pathological condition can therefore shed light into the mechanisms involved in the pathogenesis and progression of the disease. Although the development of medical imaging technologies is providing increasing amounts of data on the three-dimensional morphology of the in vivo vasculature, robust and objective tools for quantitative analysis of vascular geometry are still lacking. In this paper, we present a framework for the geometric analysis of vascular structures, in particular for the quantification of the geometric relationships between the elements of a vascular network based on the definition of centerlines. The framework is founded upon solid computational geometry criteria, which confer robustness of the analysis with respect to the high variability of in vivo vascular geometry. The techniques presented are readily available as part of the VMTK, an open source framework for image segmentation, geometric characterization, mesh generation and computational hemodynamics specifically developed for the analysis of vascular structures. As part of the Aneurisk project, we present the application of the present framework to the characterization of the geometric relationships between cerebral aneurysms and their parent vasculature.

Today angiotensin II inhibition is primarily used to slow the rate of progression of kidney diseases. There is evidence that these therapies can induce a partial regression of glomerular lesions. However, we do not know yet the extent of sclerotic lesion regression and whether new glomerular tissue is formed to help support the renal function. We used male Munich Wistar Fromter (MWF) rats, an experimental model for progressive kidney disease, to quantify kidney structural lesions upon angiotensin-converting enzyme (ACE) inhibition therapy. Animals were studied at 50 weeks of age, when renal function and structure are severely altered, and after a 10-week observation period, without or with treatment with lisinopril (80 mg/l in drinking water). A group of untreated Wistar rats was used as controls. With age, proteinuria, and serum creatinine worsen, but lisinopril almost normalized proteinuria and stabilized serum creatinine. Serial section analysis of whole glomerular tufts showed that at baseline, glomerulosclerosis affected the entire glomerular population, and that these changes further increased with age. Lisinopril significantly reduced incidence and extent of glomerulosclerosis, with the presence of glomerular tufts not affected by sclerosis (23% of glomeruli). Glomerular volume was not significantly affected by treatment, and glomerular mass spared from sclerosis increased from 46.9 to 65.5% upon treatment, indicating consistent regeneration of glomerular tissue. Lisinopril normalized baseline glomerular transforming growth factor-beta and alpha-smooth muscle actin overexpression, and prevented worsening of interstitial changes. Hence, ACE inhibition, which is widely used in human kidney disease, may not only halt the progression of renal failure, but also actually induce the regeneration of new renal tissue.

Human studies of haemodynamic factors in the pathogenesis of cerebral aneurysms require knowledge of the pre-aneurysmal vasculature. This paper presents an objective and automated technique to digitally remove an aneurysm and reconstruct the parent artery, based on lumen geometries segmented from angiographic images. Relying on robust computational geometry concepts, notably Voronoi diagrams of the digitised lumen surface, the aneurysm attachment region is first defined objectively using lumen centrelines. Centrelines within this region are replaced by smooth interpolations, which then guide the interpolation of Voronoi points within the attachment region. Combined with Voronoi points from outside the attachment region, the parent artery lumen, without the aneurysm, can be reconstructed. Plausible reconstructions were obtained, automatically, for a set of 10 side-wall or terminal aneurysms, of various sizes and shapes, from the ANEURISK project data set. Application of image-based computational fluid dynamics analysis to a five side-wall aneurysm cases data set revealed an association between the recently proposed gradient oscillatory number (GON) and the site of aneurysm formation in four of five cases; however, elevated GON was also evident at non-aneurysmal sites. A potential application to the automated delineation of aneurysms for morphological characterisations is also suggested. The proposed approach may serve as a broad platform for investigating haemodynamic and morphological factors in aneurysm initiation, rupture and therapy in a way amenable to large-scale clinical studies or routine clinical use. Nevertheless, while the parent artery reconstructions are plausible, it remains to be proven that they are faithful representations of the pre-aneurysmal artery.

The development of new technologies for acquiring measures and images in order to investigate cardiovascular diseases raises new challenges in scientific computing. These data can be in fact merged with the numerical simulations for improving the accuracy and reliability of the computational tools. Assimilation of measured data and numerical models is well established in meteorology, whilst it is relatively new in computational hemodynamics. Different approaches are possible for the mathematical setting of this problem. Among them, we follow here a variational formulation, based on the minimization of the mismatch between data and numerical results by acting on a suitable set of control variables. Several modeling and methodological problems related to this strategy are open, such as the analysis of the impact of the noise affecting the data, and the design of effective numerical solvers. In this chapter we present three examples where a mathematically sound (variational) assimilation of data can significantly improve the reliability of the numerical models. Accuracy and reliability of computational models are increasingly important features in view of the progressive adoption of numerical tools in the design of new therapies and, more in general, in the decision making process of medical doctors.