Key result
A highly automatic method for estimating myocardial oedema using bright blood T2-weighted CMR achieved good accuracy, with a Dice similarity coefficient of 0.74 compared to standard manual segmentation.
Why the study?
Does a highly automatic framework for quantifying myocardial oedema match the accuracy of manual segmentation in patients with acute MI?
Observational (n=25)
No
Does a highly automatic framework for quantifying myocardial oedema match the accuracy of manual segmentation in patients with acute MI?
A highly automatic method for estimating myocardial oedema on T2-weighted CMR is accurate compared to manual segmentation, offering a potential clinical tool for acute MI patients.
May support rapid oedema assessment in acute MI; hypothesis-generating and requires prospective validation before clinical use.
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.
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Gao et al. (2013) conducted an observational in Acute myocardial infarction (n=25). Highly automatic quantification of myocardial oedema vs. Manual segmentation was evaluated on Dice similarity coefficient for agreement of oedema regions. A highly automatic method for estimating myocardial oedema using bright blood T2-weighted CMR achieved good accuracy, with a Dice similarity coefficient of 0.74 compared to standard manual segmentation.
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