A framework combining multi-sequence MRI for fully automated quantitative analysis of cardiac global and regional functions

Authors:
Xiahai Zhuang;Wenzhe Shi;Simon Duckett;Haiyan Wang;Reza Razavi;David Hawkes;Daniel Rueckert;Sebastien Ourselin
Affiliations:
Centre for Medical Image Computing, University College London;Biomedical Image Analysis Group, Imperial College London;The Rayne Institute, St Thomas Hospital, King's College London;Biomedical Image Analysis Group, Imperial College London;The Rayne Institute, St Thomas Hospital, King's College London;Centre for Medical Image Computing, University College London;Biomedical Image Analysis Group, Imperial College London;Centre for Medical Image Computing, University College London
Venue:
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Year:
2011

Citing 3
Cited 0

An Atlas-Based Segmentation Propagation Framework Using Locally Affine Registration --- Application to Automatic Whole Heart Segmentation

MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Multi-sequence Registration of Cine, Tagged and Delay-Enhancement MRI with Shift Correction and Steerable Pyramid-Based Detagging

FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
LV surface reconstruction from sparse TMRI using Laplacian surface deformation and optimization

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro

Quantified Score

Hi-index	0.00

Visualization

Abstract

In current clinical settings, there are several technological challenges to perform automated functional analysis from cardiac MRI. In this work, we present a framework to automatically segment the heart anatomy, define segments of the left ventricle, and extract myocardial motions for quantitative analysis of cardiac global and regional functions. This framework makes use of the cardiac MRI sequences that are widely available in clinical practice, and improves the performance of the automated processing by combining information from multiple MRI sequences. We employed 20 pathological datasets to evaluate the proposed framework where the automatic analysis was compared with the manual intervention assisted analysis. The results showed high correlation between the two methods for the global function analysis (volume: R20.8, ejection fraction: R2=0.88), and for the regional dyssynchrony analysis (wall motion: R2=0.89; thickening: R2=0.81). We also found that the automated method could fully include apical and basal volume, resulting in consistent overestimation of the left ventricle volume (∼40mL, PP