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Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT.

ABSTRACT: BACKGROUND: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). 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. METHODS:The 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). RESULTS:MaR 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. CONCLUSIONS:Automatic 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 REGISTRATION:NCT01379261. NCT01374321.

PROVIDER: S-EPMC4779553 | BioStudies |

REPOSITORIES: biostudies

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