Literature DB >> 29699822

Finite element modeling of the human kidney for probabilistic occupant models: Statistical shape analysis and mesh morphing.

Keegan M Yates1, Costin D Untaroiu2.   

Abstract

Statistical shape analysis was conducted on 15 pairs (left and right) of human kidneys. It was shown that the left and right kidney were significantly different in size and shape. In addition, several common modes of kidney variation were identified using statistical shape analysis. Semi-automatic mesh morphing techniques have been developed to efficiently create subject specific meshes from a template mesh with a similar geometry. Subject specific meshes as well as probabilistic kidney meshes were created from a template mesh. Mesh quality remained about the same as the template mesh while only taking a fraction of the time to create the mesh from scratch or morph with manually identified landmarks. This technique can help enhance the quality of information gathered from experimental testing with subject specific meshes as well as help to more efficiently predict injury by creating models with the mean shape as well as models at the extremes for each principal component.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Human kidney; Mean and boundary models; Mesh morphing; Principal component analysis; Statistical shape analysis

Mesh:

Year:  2018        PMID: 29699822     DOI: 10.1016/j.jbiomech.2018.04.016

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  2 in total

1.  Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time.

Authors:  M J M M Hoeijmakers; I Waechter-Stehle; J Weese; F N Van de Vosse
Journal:  Int J Numer Method Biomed Eng       Date:  2020-09-13       Impact factor: 2.747

2.  The impact of shape uncertainty on aortic-valve pressure-drop computations.

Authors:  M J M M Hoeijmakers; W Huberts; M C M Rutten; F N van de Vosse
Journal:  Int J Numer Method Biomed Eng       Date:  2021-08-23       Impact factor: 2.648

  2 in total

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