StrainNet outperformed commercial feature tracking for global circumferential strain analysis of cine MRI, demonstrating higher agreement with DENSE (ICC 0.87 vs 0.72).
Observational (n=305)
Yes
Does StrainNet improve the accuracy of global and segmental circumferential strain analysis of cine MRI compared to commercial feature tracking?
A deep learning model (StrainNet) trained on DENSE data outperforms commercial feature tracking for analyzing global and segmental circumferential strain from standard cine MRI.
Absolute Event Rate: 0.87% vs 0.72%
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 intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations 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 circumferential 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. Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023
Wang et al. (Thu,) conducted a observational in Various heart diseases and healthy controls (n=305). StrainNet (deep learning model) vs. Commercial feature tracking (FT) was evaluated on Agreement of global circumferential strain (Ecc) with DENSE reference (ICC). StrainNet outperformed commercial feature tracking for global circumferential strain analysis of cine MRI, demonstrating higher agreement with DENSE (ICC 0.87 vs 0.72).