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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.

SUBMITTER: Tufvesson J

PROVIDER: S-EPMC4779553 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVES:Assessment of thoracic aortic dimensions with non-ECG-triggered contrast-enhanced magnetic resonance angiography (CE-MRA) is accompanied with motion artefacts and requires gadolinium. To avoid both motion artefacts and gadolinium administration, we evaluated the similarity and reproducibility of dimensions measured on ECG-triggered, balanced steady-state free precession (SSFP) MRA as alternative to CE-MRA. METHODS:All patients, with varying medical conditions, referred for thoracic aortic examination between September 2016 and March 2018, who underwent non-ECG-triggered CE-MRA and SSFP-MRA (1.5 T) were retrospectively included (n = 30). Aortic dimensions were measured after double-oblique multiplanar reconstruction by two observers at nine landmarks predefined by literature guidelines. Image quality was scored at the sinus of Valsalva, mid-ascending aorta and mid-descending aorta by semi-automatically assessing the vessel sharpness. RESULTS:Aortic dimensions showed high agreement between non-ECG-triggered CE-MRA and SSFP-MRA (r = 0.99, p < 0.05) without overestimation or underestimation of aortic dimensions in SSFP-MRA (mean difference, 0.1 mm; limits of agreement, -?1.9 mm and 1.9 mm). Intra- and inter-observer variabilities were significantly smaller with SSFP-MRA for the sinus of Valsalva and sinotubular junction. Image quality of the sinus of Valsalva was significantly better with SSFP-MRA, as fewer images were of impaired quality (3/30) than in CE-MRA (21/30). Reproducibility of dimensions was significantly better in images scored as good quality compared to impaired quality in both sequences. CONCLUSIONS:Thoracic aortic dimensions measured on SSFP-MRA and non-ECG-triggered CE-MRA were similar. As expected, SSFP-MRA showed better reproducibility close to the aortic root because of lesser motion artefacts, making it a feasible non-contrast imaging alternative. KEY POINTS:• SSFP-MRA provides similar dimensions as non-ECG-triggered CE-MRA. • Intra- and inter-observer reproducibilities improve for the sinus of Valsalva and sinotubular junction with SSFP-MRA. • ECG-triggered SSFP-MRA shows better image quality for landmarks close to the aortic root in the absence of cardiac motion.

Project description:Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement. Methods: A total of 392 consecutive patients undergoing 99mTc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared. Results: Bland-Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, -5 to 7 mm) and AC rest images (bias, 1; 95% CI, -7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, -8 to 8 mm) and AC rest images (bias, 0; 95% CI, -10 to 10 mm) (P < 0.01). Bland-Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, -4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, -7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, -6 to 5 mm) and non-AC rest images (bias, -1; 95% CI, -9 to 7 mm) (P was not significant [NS]). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 [95% CI, 0.7-0.87]; AUC, 0.81 [95% CI, 0.73-0.89]) and the SVM (0.82 [0.74-0.9]) for AC data were the same (P = NS) and were higher than those for the unadjusted VP (0.63 [0.53-0.73]) (P < 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69-0.89]; AUC, 0.8 [95% CI, 0.72-0.88]) and the SVM (0.79 [0.71-0.87]) were the same (P = NS) and were higher than those for the unadjusted VP (0.65 [0.56-0.75]) (P < 0.01). Conclusion: Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification.

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

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