Abstract
PurposeTo demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac MRI–tagged images.Materials and MethodsIn this retrospective cross-sectional study, 4508 cases from the U.K. Biobank were split randomly into 3244 training cases, 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of (a) a convolutional neural network (CNN) for localization and (b) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis.ResultsWithin the test set, myocardial end-systolic circumferential Green strain errors were −0.001 ± 0.025, −0.001 ± 0.021, and 0.004 ± 0.035 in the basal, mid-, and apical slices, respectively (mean ± standard deviation of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in participants with diabetes, hypertensive participants, and participants with a previous heart attack. Typical processing time was approximately 260 frames (approximately 13 slices) per second on a GPU with 12 GB RAM compared with 6–8 minutes per slice for the manual analysis.ConclusionThe fully automated combined RNN and CNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack.Published under a CC BY 4.0 license.Supplemental material is available for this article.
Highlights
Automated whole-slice myocardial strain analysis is feasible in a high-throughput workflow by using a deep learning framework and can be used to detect impairment in disease groups with confidence intervals similar to those attained with manual analysis
We reviewed the cases with the worst intersection over union (IoU) and checked whether the cropped region of interest (ROI) was acceptable
In the combined recurrent neural network (RNN) and CNN network, we found that a leaky rectified linear unit (ReLU) [37] was a key hyperparameter in the CNN component, which allowed negative values to be updated and prevented missing spatial information that might have been useful for the RNN component
Summary
In this retrospective cross-sectional study, 4508 cases from the U.K. Biobank were split randomly into 3244 training cases, 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of (a) a convolutional neural network (CNN) for localization and (b) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. This study examined 5065 U.K. Biobank participants who underwent cardiovascular MRI as part of the pilot phase (April 2014–August 2015) of the U.K. Biobank imaging enhancement substudy [22]. Details of the image acquisition protocol have been described previously [22]. Cardiovascular MRI–tagged images comprised gradient-recalled-echo images acquired in three short-axis slices (basal, mid, and apical) with the following parameters: repetition time msec/echo time msec, 8.2/3.9; flip angle, 12°; field of view, 350 × 241 mm; acquisition matrix, 256 × 174; voxel size, 1.4 × 1.4 × 8.0 mm; prospective triggering; tag grid spacing, 6 mm; temporal resolution, 41 msec; and approximately 20 reconstructed frames
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