The aim of this study is to develop and validate a patient-specific distributed model of the systemic arterial tree. This model is built using geometric and hemodynamic data measured on a specific person and validated with noninvasive measurements of flow and pressure on the same person, providing thus a patient-specific model and validation. The systemic arterial tree geometry was obtained from MR angiographic measurements. A nonlinear viscoelastic constitutive law for the arterial wall is considered. Arterial wall distensibility is based on literature data and adapted to match the wave propagation velocity of the main arteries of the specific subject, which were estimated by pressure waves traveling time. The intimal shear stress is modeled using the Witzig-Womersley theory. Blood pressure is measured using applanation tonometry and flow rate using transcranial ultrasound and phase-contrast-MRI. The model predicts pressure and flow waveforms in good qualitative and quantitative agreement with the in vivo measurements, in terms of wave shape and specific wave features. Comparison with a generic one-dimensional model shows that the patient-specific model better predicts pressure and flow at specific arterial sites. These results obtained let us conclude that a patientspecific one-dimensional model of the arterial tree is able to predict well pressure and flow waveforms in the main systemic circulation, whereas this is not always the case for a generic one-dimensional model. wave propagation; cerebral circulation; noninvasive measurements techniques; phase-contrast-magnetic resonance imaging; Doppler AT PRESENT, one-dimensional models are best suited to study flow and pressure waveforms along the whole or extensive parts of the systemic and pulmonary circulation. They can provide insight regarding wave propagation and reflection phenomena and allow for characterization of ventricular-arterial coupling. Because of their relatively low computational cost and complexity, one-dimensional models have been extensively used in the past to study different pathologies, such as hypertension by Westerhof et al. (30,31), arteriosclerosis by Raines et al. (22), stenoses by many authors (2,5,7,10,13,17,21,25,26,32), anatomical variations of cerebral arteries, arterial occlusion by Alastruey et al. (1), or to study surgery plans by Wan et al. (28). The main characteristics of previous one-dimensional models have been published in a previous article from our laboratory (23).The primary question addressed in this article was the validity of the generic one-dimensional model predictions. The approach we followed was to compare the predictions of the generic one-dimensional model with the average pressure and flow waveforms measured noninvasively in a group of healthy young individuals. The underlying hypothesis was that although the generic model would not represent precisely a specific individual it should represent reasonably well the average of the group. Hence, the model validation was strictly qualitative. Reymond et al. 2009...

As high-resolution functional magnetic resonance imaging (fMRI) and fMRI of cortical layers become more widely used, the question how well high-resolution fMRI signals reflect the underlying neural processing, and how to interpret laminar fMRI data becomes more and more relevant. High-resolution fMRI has shown laminar differences in cerebral blood flow (CBF), volume (CBV), and neurovascular coupling. Features and processes that were previously lumped into a single voxel become spatially distinct at high resolution. These features can be vascular compartments such as veins, arteries, and capillaries, or cortical layers and columns, which can have differences in metabolism. Mesoscopic models of the blood oxygenation level dependent (BOLD) response therefore need to be expanded, for instance, to incorporate laminar differences in the coupling between neural activity, metabolism and the hemodynamic response. Here we discuss biological and methodological factors that affect the modeling and interpretation of high-resolution fMRI data. We also illustrate with examples from neuropharmacology and the negative BOLD response how combining BOLD with CBF- and CBV-based fMRI methods can provide additional information about neurovascular coupling, and can aid modeling and interpretation of high-resolution fMRI.

Mice are widely used to investigate atherogenesis, which is known to be influenced by stresses related to blood flow. However, numerical characterization of the haemodynamic environment in the commonly studied aortic arch has hitherto been based on idealizations of inflow into the aorta. Our purpose in this work was to numerically characterize the haemodynamic environment in the mouse aortic arch using measured inflow velocities, and to relate the resulting shear stress patterns to known locations of high- and low-lesion prevalence. Blood flow velocities were measured in the aortic root of C57/BL6 mice using phase-contrast MRI. Arterial geometries were obtained by micro-CT of corrosion casts. These data were used to compute blood flow and wall shear stress (WSS) patterns in the arch. WSS profiles computed using realistic and idealized aortic root velocities differed significantly. An unexpected finding was that average WSS in the high-lesion-probability region on the inner wall was actually higher than the WSS in the low-probability region on the outer wall. Future studies of mouse aortic arch haemodynamics should avoid the use of idealized inflow velocity profiles. Lesion formation does not seem to uniquely associate with low or oscillating WSS in this segment, suggesting that other factors may also play a role in lesion localization.

The aim of this study is to validate a person-specific distributed model of the main systemic arterial tree. This model is built and validated with non-invasive measurements on the same person, leading therefore to a coherent set of physiological data. One-dimensional (1D) models have been used for more than 30 years to predict or analyze pressure and flow in the arterial tree (Avolio [1], Stergiopulos et al [2], Westerhof et al [3]), demonstrating their aptitude of modeling wave propagation, however, they have never being validated using in vivo measurements. A quantitative validation was performed in vitro in an elastic tube network dimensioned to resemble the human arterial tree by Matthys et al. [4]. The results were supportive of the 1D model’s capacity to yield good predictions, however, neither the form of the waves nor the elastic properties of the in vitro tube network were matching faithfully their physiological counterparts, so the interest to quantitatively validate the 1D model in vivo remained.

Atherosclerotic lesions have a highly non-uniform distribution in regions of arterial branching and curvature, consistent with hemodynamic factors, in particular wall shear stress (WSS), controlling their development. The widespread and increasing use of the mouse as a model for studying atherosclerosis has encouraged investigation of the hemodynamics of the mouse aortic arch [1–3], in which previous studies have revealed areas of high and low lesion prevalence and variation in the expression of pro-atherogenic molecules [4]. Our previous computational simulations [1–2] did not produce distributions of WSS that explain the pattern of lesions. We are currently investigating whether incorporation of more realistic aortic root velocity measurements, obtained using phase-contrast magnetic resonance imaging (PC-MRI), into these simulations can improve the correlation with disease. Here we present velocities obtained by PC-MRI and preliminary simulations employing the data.