Abstract
Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data provides a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wider clinical use. We designed and validated a fast, fully-automatic deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data consisting of segmentation and motion estimation convolutional neural networks. The final motion network design, loss function, and associated hyperparameters are the result of a thorough ad hoc implementation that we carefully planned specific for strain quantification, tested, and compared to other potential alternatives. The optimal configuration was trained using healthy and cardiovascular disease (CVD) subjects (n = 150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD test subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 ± 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 10 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was good to excellent (>0.75) for all global measures and most polar map segments. In conclusion, we developed and evaluated the first end-to-end learning-based workflow for automated strain analysis from cine-MRI data to quantitatively characterize cardiac mechanics of healthy and CVD subjects.
Highlights
Cardiac mechanics reflects the precise interplay between myocardial architecture and loading conditions that is essential for sustaining the blood pumping function of the heart
This paper describes a carefully designed strain quantification-specific 3D convolutional neural networks (CNN) that handles challenges associated with the anisotropic resolution of cineMRI data
Training with a multi-class Dice coefficient (MDC) loss function resulted in slightly more accurate segmentations compared to categorical cross-entropy (CCE) (Supplementary Table 2), the MDC-trained model was used for all remaining analyses
Summary
Cardiac mechanics reflects the precise interplay between myocardial architecture and loading conditions that is essential for sustaining the blood pumping function of the heart. Automatic delineation approaches have been implemented within computational pipelines [9], but other factors related to motion tracking algorithms influence strain assessment, including the appropriate selection of tuneable parameters whose optimal values can differ between patient cohorts and acquisition protocols [e.g., the size of the search region in block-matching methods [10]]. These algorithms often make assumptions about the properties of the myocardial tissue [e.g., incompressible and elastic [11, 12]], or use registration methods to drive the solution toward an expected geometry. Modality-related image quality could complicate interpretation of abnormal strain values since these could reflect either real dysfunction or artifact-related inaccuracies, leading to some degree of subjectivity or non-conclusive results [3]
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