StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE

Purpose: To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI.Materials and Methods: In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocar-dial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examina-tions with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circum-ferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models.Results: The study included 161 patients (110 men; mean age, 61 years +/- 14 [SD]), 99 healthy adults (44 men; mean age, 35 years +/- 15), and 45 healthy children and adolescents (21 males; mean age, 12 years +/- 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm +/- 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc.Conclusion: StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.

Automated summary

This study developed a three-dimensional convolutional neural network model for displacement and strain analysis of cardiac MRI. The model, trained with DENSE data, showed excellent performance in predicting intramyocardial displacement. Comparison with commercial feature tracking method (FT) demonstrated that StrainNet outperformed in global and segmental strain analysis.