Paper Info
Title
Characterization Of Hypertrophic Cardiomyopathy Using Left Ventricular Regional Wall Thickness Derived From Cmr Imaging
Abstract
Hypertrophic cardiomyopathy (HCM) patients present unusual myocardial mechanics due to hypertrophied ventricular wall. The objective of this study was to assess the distribution of regional wall thickness for the left ventricle (LV) based on cardiac magnetic resonance (CMR) images in HCM patients compared against control subjects.CMR scans were performed in 19 HCM patients and 9 healthy individuals. Border-delineated contours of the LV endocardial and epicardial surfaces throughout the entire cardiac cycle were used to reconstruct the 3D LV geometry for all participants using our in-house software (Cardiowerkz). The LV wall thickness is then computed for each individual vertex on the endocardial surface mesh using a ray tracing approach to the epicardial surface mesh (to obtain the perpendicular distance from the mesh vertex on the endocardial surface to the epicardial surface). The regional wall thickness is then characterized using the standard 16-segment American Heart Association (AHA) nomenclature by aggregating over all mesh vertices in the particular segment.We observed the following results: (i) regional wall thickness for all 16 segments in HCM patients were significantly higher as compared to normal controls and (ii) distribution of regional wall thickness across 3 representative HCM subtypes appear visually different. The novelty of our approach is that it facilitates the visualization of the LV wall thickness distribution across the 16 AHA segments that potentially can be used to differentiate the various HCM subtypes.
Year
DOI
Venue
2017
10.22489/CinC.2017.287-148
2017 COMPUTING IN CARDIOLOGY (CINC)
Field
DocType
Volume
Internal medicine,Cardiology,Cardiac magnetic resonance,Ventricle,Cardiac cycle,Hypertrophic cardiomyopathy,Medicine
Conference
44
ISSN
Citations 
PageRank 
2325-8861
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Soo Kng Teo1146.97
Xiaodan Zhao214.20
Ru-San Tan323922.37
Liang Zhong444.21
Y. Su52010.55