Aortic stiffness has proven to be an important diagnostic and prognostic factor of many cardiovascular diseases, as well as an estimate of overall cardiovascular health. Pulse wave velocity (PWV) represents a good measure of the aortic stiffness, while the aortic distensibility is used as an aortic elasticity index. Obtaining the PWV and the aortic distensibility from magnetic resonance imaging (MRI) data requires diverse segmentation tasks, namely the extraction of the aortic center line and the segmentation of aortic regions, combined with signal processing methods for the analysis of the pulse wave. In our study non-contrasted MRI images of abdomen were used in healthy volunteers (22 data sets) for the sake of non-invasive analysis and contrasted magnetic resonance (MR) images were used for the aortic examination of Marfan syndrome patients (8 data sets). In this research we present a novel robust segmentation technique for the PWV and aortic distensibility calculation as a complete image processing toolbox. We introduce a novel graph-based method for the centerline extraction of a thoraco-abdominal aorta for the length calculation from 3-D MRI data, robust to artifacts and noise. Moreover, we design a new projection-based segmentation method for transverse aortic region delineation in cardiac magnetic resonance (CMR) images which is robust to high presence of artifacts. Finally, we propose a novel method for analysis of velocity curves in order to obtain pulse wave propagation times. In order to validate the proposed method we compare the obtained results with manually determined aortic centerlines and a region segmentation by an expert, while the results of the PWV measurement were compared to a validated software (LUMC, Leiden, the Netherlands). The obtained results show high correctness and effectiveness of our method for the aortic PWV and distensibility calculation.
Keywords: Image segmentation; Multiscale segmentation; Pulse wave velocity.
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