Literature DB >> 29747948

Automated quantification of mitral valve geometry on multi-slice computed tomography in patients with dilated cardiomyopathy - Implications for transcatheter mitral valve replacement.

Tom Banks1, Orod Razeghi2, Ioannis Ntalas3, Waqar Aziz3, Jonathan M Behar3, Rebecca Preston4, Brian Campbell3, Simon Redwood3, Bernard Prendergast3, Steven Niederer2, Ronak Rajani5.   

Abstract

OBJECTIVES: The primary aim of this study was to quantify the dimensions and geometry of the mitral valve complex in patients with dilated cardiomyopathy and significant mitral regurgitation. The secondary aim was to evaluate the validity of an automated segmentation algorithm for assessment of the mitral valve compared to manual assessment on computed tomography.
BACKGROUND: Transcatheter mitral valve replacement (TMVR) is an evolving technique which relies heavily on the lengthy evaluation of cardiac computed tomography (CT) datasets. Limited data is available on the dimensions and geometry of the mitral valve in pathological states throughout the cardiac cycle, which may have implications for TMVR device design, screening of suitable candidates and annular sizing prior to TMVR.
METHODS: A retrospective study of 15 of patients with dilated cardiomyopathy who had undergone full multiphase ECG gated cardiac CT. A comprehensive evaluation of mitral valve geometry was performed at 10 phases of the cardiac cycle using the recommended D-shaped mitral valve annulus (MA) segmentation model using manual and automated CT interpretation platforms. Mitral annular dimensions and geometries were compared between manual and automated methods.
RESULTS: Mitral valve dimensions in patients with dilated cardiomyopathy were similar to previously reported values (MAarea Diastole: 12.22 ± 1.90 cm2), with dynamic changes in size and geometry between systole and diastole of up to 5%. The distance from the centre of the MA to the left ventricular apex demonstrated moderate agreement between automated and manual methods (ρc = 0.90) with other measurements demonstrating poor agreement between the two methods (ρc = 0.75-0.86).
CONCLUSIONS: Variability of mitral valve annulus measurements are small during the cardiac cycle. Novel automated algorithms to determine cardiac cycle variations in mitral valve geometry may offer improved segmentation accuracy as well as improved CT interpretation times.
Copyright © 2018 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computed tomography; Geometry; Mitral valve; Transcatheter mitral valve replacement

Mesh:

Year:  2018        PMID: 29747948     DOI: 10.1016/j.jcct.2018.04.003

Source DB:  PubMed          Journal:  J Cardiovasc Comput Tomogr        ISSN: 1876-861X


  5 in total

1.  Individual Patient-specific Planning of Minimally Invasive Transcatheter Intervention for Heart Valve Disease.

Authors:  A De Vecchi; S Niederer; R Rajani; S Redwood; B Prendergast
Journal:  EClinicalMedicine       Date:  2019-01-23

2.  Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images.

Authors:  Orod Razeghi; Mattias Heinrich; Thomas E Fastl; Cesare Corrado; Rashed Karim; Adelaide De Vecchi; Tom Banks; Patrick Donnelly; Jonathan M Behar; Justin Gould; Ronak Rajani; Christopher A Rinaldi; Steven Niederer
Journal:  Sci Rep       Date:  2021-03-11       Impact factor: 4.379

Review 3.  Cardiac Computed Tomography in Cardio-Oncology: JACC: CardioOncology Primer.

Authors:  Juan C Lopez-Mattei; Eric H Yang; Maros Ferencik; Lauren A Baldassarre; Susan Dent; Matthew J Budoff
Journal:  JACC CardioOncol       Date:  2021-12-21

4.  Dynamic changes of mitral valve annulus geometry at preprocedural CT: relationship with functional classes of regurgitation.

Authors:  Anna Palmisano; Valeria Nicoletti; Caterina Colantoni; Caterina Beatrice Monti; Luigi Pannone; Davide Vignale; Fatemeh Darvizeh; Eustachio Agricola; Simone Schaffino; Francesco De Cobelli; Antonio Esposito
Journal:  Eur Radiol Exp       Date:  2021-08-13

5.  CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research.

Authors:  Orod Razeghi; José Alonso Solís-Lemus; Angela W C Lee; Rashed Karim; Cesare Corrado; Caroline H Roney; Adelaide de Vecchi; Steven A Niederer
Journal:  SoftwareX       Date:  2020-07-31
  5 in total

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