Literature DB >> 23154583

A new approach to kinematic feature extraction from the human right ventricle for classification of hypertension: a feasibility study.

Jia Wu1, Yingqian Wang, Marc A Simon, John C Brigham.   

Abstract

This work presents a novel approach to analyze the function of the human right ventricle (RV) by deriving kinematic features of the relative change in shape throughout the cardiac cycle. The approach is anatomically consistent, allows direct comparison across populations of individuals, and potentially provides new metrics to improve the diagnosis and understanding of cardiovascular diseases such as pulmonary hypertension (PH). The details of the approach are presented, which includes a variation of harmonic topological mapping and proper orthogonal decomposition techniques, with particular focus on their applicability with respect to untagged cardiac imaging data. Results are shown for the decomposition of a collection of clinically obtained human RV endocardial surfaces segmented from cardiac computed tomography imaging into the fundamental shape change features for individuals both with and without PH. The features are shown to be consistent and converging towards intrinsically physiological components for the heart, and may potentially represent a new set of features for classifying the progressive change in RV function caused by PH, particularly in comparison to traditional clinical metrics.

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Year:  2012        PMID: 23154583     DOI: 10.1088/0031-9155/57/23/7905

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  4 in total

Review 1.  Assessment and treatment of right ventricular failure.

Authors:  Marc A Simon
Journal:  Nat Rev Cardiol       Date:  2013-02-12       Impact factor: 32.419

2.  Surgical planning for living donor liver transplant using 4D flow MRI, computational fluid dynamics and in vitro experiments.

Authors:  David R Rutkowski; Scott B Reeder; Luis A Fernandez; Alejandro Roldán-Alzate
Journal:  Comput Methods Biomech Biomed Eng Imaging Vis       Date:  2017-01-18

3.  A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.

Authors:  Liang Liang; Minliang Liu; Caitlin Martin; John A Elefteriades; Wei Sun
Journal:  Biomech Model Mechanobiol       Date:  2017-04-06

4.  Investigating Cardiac Motion Patterns Using Synthetic High-Resolution 3D Cardiovascular Magnetic Resonance Images and Statistical Shape Analysis.

Authors:  Benedetta Biffi; Jan L Bruse; Maria A Zuluaga; Hopewell N Ntsinjana; Andrew M Taylor; Silvia Schievano
Journal:  Front Pediatr       Date:  2017-03-08       Impact factor: 3.418

  4 in total

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