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Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

Hao Gao1, Kushsairy Kadir2, Alexander R Payne3, John Soraghan4 and Colin Berry5*

Author Affiliations

1 School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QW, UK

2 Centre for Excellence in Signal and Image Processing, Department of Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, UK

3 BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK

4 Centre for Excellence in Signal and Image Processing, Department of Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, UK

5 BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK

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Journal of Cardiovascular Magnetic Resonance 2013, 15:28  doi:10.1186/1532-429X-15-28

Published: 30 March 2013

Abstract

Background

T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.

Methods

We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.

Results

The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.

Conclusion

Compared to standard manual approaches, the new highly automatic method for estimating myocardial oedema is accurate and straightforward. It has potential as a generic software tool for physicians to use in clinical practice.

Keywords:
Myocardial oedema; Bright blood T2-weighted CMR; Rayleigh-Gaussian mixture model; Level set