Literature DB >> 21459612

Development of in vivo quantitative geometric mapping of the aortic arch for advanced endovascular aortic repair: feasibility and preliminary results.

Fabian Rengier1, Stefan Wörz, William J Godinez, Hardy Schumacher, Dittmar Böckler, Karl Rohr, Hans-Ulrich Kauczor, Hendrik von Tengg-Kobligk.   

Abstract

PURPOSE: To evaluate whether quantitative characterization of aortic arch geometry including its branches is feasible based on in vivo computed tomography (CT) angiography and magnetic resonance (MR) angiography data in healthy and diseased aortic arches.
MATERIALS AND METHODS: Ten healthy volunteers, 10 patients with abdominal aortic disease, and 10 patients with aortic arch disease underwent MR angiography (10 volunteers) or CT angiography (20 patients). Commercial software was used for individual segmentation of supraaortic arteries. In-house software was developed for segmentation of aortic arch landmarks based on standardized multiplanar reformations (MPRs) and for subsequent aortic arch mapping.
RESULTS: Supraaortic arteries and aortic arch landmarks were successfully segmented in all 30 subjects for CT angiography and MR angiography data. Significant tapering within the first centimeter was observed in all supraaortic arteries (P < .001). The three supraaortic arteries showed significantly different vessel diameters and areas (P < .001). The software developed in-house allowed detailed aortic arch mapping with quantitative definitions of the positional relationships between each supraaortic artery and the aorta. Distances between supraaortic arteries were less than 5 mm in 77.6% (mean 4.1 mm ± 3.8). The brachiocephalic trunk tended to be positioned on the right side of the aortic arch, and the left subclavian and left common carotid arteries tended to be positioned on the left side of the aortic arch.
CONCLUSIONS: The feasibility and application of a postprocessing method allowing quantification of geometry of supraaortic arteries and aortic arch mapping were successfully demonstrated. Validation and evaluation of clinical implications are warranted.
Copyright © 2011 SIR. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 21459612     DOI: 10.1016/j.jvir.2011.01.434

Source DB:  PubMed          Journal:  J Vasc Interv Radiol        ISSN: 1051-0443            Impact factor:   3.464


  1 in total

1.  A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection.

Authors:  Yitong Yu; Yang Gao; Jianyong Wei; Fangzhou Liao; Qianjiang Xiao; Jie Zhang; Weihua Yin; Bin Lu
Journal:  Korean J Radiol       Date:  2020-11-03       Impact factor: 3.500

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.