Automatic extraction of three‐dimensional thoracic aorta geometric model from phase contrast MRI for morphometric and hemodynamic characterization

Abstract

Our method was found effective on PCMRI data to provide a 3D geometric model of the TA, to support morphometric and hemodynamic characterization of the aorta.

Uncertainty propagation of phase contrast-MRI derived inlet boundary conditions in computational hemodynamics models of thoracic aorta

“…The MRI data sequence was extensively detailed in several previous studies (Morbiducci et al 2009;Morbiducci et al 2011a;Tresoldi et al 2014;Volonghi et al 2016), and reported in other computational hemodynamics studies (Gallo et al 2012;Morbiducci et al 2013Morbiducci et al , 2015. Briefly, 4D MRI images of an ostensibly healthy human aorta were acquired using a 1.5 T scanner (Achieva, Philips Healthcare, The Netherlands).…”

Skeleton-based cerebrovascular quantitative analysis

“…Studies by Grisan et al and Shelton et al concerning vessel tortuosity measurements used retinal blood vessels to calculate the ratio of arc length to chord length and curvature [17, 18]. However, a few studies investigated tortuosity at other vascular sites or lesions, such as tumors, the cerebral microvasculature, coronary arteries and thoracic aorta [19–23]. Studies investigating the tortuosity of the CoW are very rare, particularly quantitative studies using 3D graphics rather than slice data.…”

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline

“…Accordingly, significant efforts have been placed on the development of efficient computer-aided methods for vessel wall extraction. In previous work, approaches for automatic segmentation of carotid and aorta employ classical deformable models (eg, active contour model, 15 active shape model, 16 discrete dynamic contour model, 17 ellipse model, 18 cylinder model; 19,20 deformable model implemented using levelset; [21][22][23] graph-cuts; 24,25 or intensity probability density function matching. 26 In these approaches, some reported methods 19,22,23 demonstrated only the segmentation of inner border, while other methods [15][16][17][18]20,21,[24][25][26] are able to segment both inner and outer vessel walls.…”

“…In general, the requirements range from one interaction for every slice [15][16][17][18][19]24 to one single interaction to start the complete segmentation. [20][21][22][23]25,26 Additionally, the methods presented are optimized to a specific type of vessel (eg, carotid [15][16][17][18][19][20]23,25,26 or aorta 21,22,24 ) and no generally applicable method has been described. While semiautomated segmentation methods are generally less time-consuming and more reproducible compared to fully manual delineation, they still remain labor-intensive and therefore are not optimally suited for cohort studies where large volumes of data need to be processed.…”