Abstract
BackgroundEfficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI) by determining the size of myocardium at risk (MaR) and myocardial infarction (MI), (MSI = 1-MI/MaR). Cardiovascular magnetic resonance (CMR) can be used to assess MI by late gadolinium enhancement (LGE) and MaR by either T2-weighted imaging or contrast enhanced SSFP (CE-SSFP). Automatic segmentation algorithms have been developed and validated for MI by LGE as well as for MaR by T2-weighted imaging. There are, however, no algorithms available for CE-SSFP. Therefore, the aim of this study was to develop and validate automatic segmentation of MaR in CE-SSFP.MethodsThe automatic algorithm applies surface coil intensity correction and classifies myocardial intensities by Expectation Maximization to define a MaR region based on a priori regional criteria, and infarct region from LGE. Automatic segmentation was validated against manual delineation by expert readers in 183 patients with reperfused acute MI from two multi-center randomized clinical trials (RCT) (CHILL-MI and MITOCARE) and against myocardial perfusion SPECT in an additional set (n = 16). Endocardial and epicardial borders were manually delineated at end-diastole and end-systole. Manual delineation of MaR was used as reference and inter-observer variability was assessed for both manual delineation and automatic segmentation of MaR in a subset of patients (n = 15). MaR was expressed as percent of left ventricular mass (%LVM) and analyzed by bias (mean ± standard deviation). Regional agreement was analyzed by Dice Similarity Coefficient (DSC) (mean ± standard deviation).ResultsMaR assessed by manual and automatic segmentation were 36 ± 10 % and 37 ± 11 %LVM respectively with bias 1 ± 6 %LVM and regional agreement DSC 0.85 ± 0.08 (n = 183). MaR assessed by SPECT and CE-SSFP automatic segmentation were 27 ± 10 %LVM and 29 ± 7 %LVM respectively with bias 2 ± 7 %LVM. Inter-observer variability was 0 ± 3 %LVM for manual delineation and -1 ± 2 %LVM for automatic segmentation.ConclusionsAutomatic segmentation of MaR in CE-SSFP was validated against manual delineation in multi-center, multi-vendor studies with low bias and high regional agreement. Bias and variability was similar to inter-observer variability of manual delineation and inter-observer variability was decreased by automatic segmentation. Thus, the proposed automatic segmentation can be used to reduce subjectivity in quantification of MaR in RCT.Clinical trial registrationNCT01379261.NCT01374321.Electronic supplementary materialThe online version of this article (doi:10.1186/s12880-016-0124-1) contains supplementary material, which is available to authorized users.
Highlights
Efficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI) by determining the size of myocardium at risk (MaR) and myocardial infarction (MI), (MSI = 1-MI/MaR)
In the test set (n = 183) MaR assessed by manual delineation in contrast enhanced SSFP (CE-SSFP) was 36 ± 10 % Percent of left ventricular mass (LVM) and MaR assessed by Segment MaR CE-SSFP automatic segmentation was 37 ± 11 %LVM
Bias was 1 ± 6 %LVM [-11; 14] %LVM, R = 0.83 and regional agreement Dice Similarity Coefficient (DSC) 0.85 ± 0.08 when Segment MaR CE-SSFP was compared to manual delineation (Fig. 2, Table 2)
Summary
The myocardial infarction evolves during time to treatment and if blood flow is not restored in time the whole region of MaR becomes myocardial infarction (MI) If both the size of MaR and final MI size is determined, the efficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI = 1-MI/MaR). By CMR MaR can be assessed by either T2-weighted imaging [3] or contrast enhanced steady state free precession (CE-SSFP) [4] and both have been validated against SPECT for assessment of MaR up to one week after MI [4, 5] Both T2-weighted imaging and CE-SSFP have been used to determine myocardial salvage in two multi-center cardioprotective studies, CHILL-MI [6] and MITOCARE [7]. CE-SSFP may be more suitable than T2-weighted imaging for quantification of MaR in multi-center settings
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