A novel Deep Learning based method for Myocardial Strain Quantification.

Purpose: This paper introduces a deep learning method for myocardial strain analysis while also evaluating the efficacy of the method across a public and a private dataset for cardiac pathology discrimination. Methods: We measure the global and regional myocardial strain in cSAX CMR images by first identifying a ROI centered in the LV, obtaining the cardiac structures (LV, RV and Myo) and estimating the motion of the myocardii. Finally we compute the strain for the heart coordinate system and report the global and regional strain. Results: We validated our method in two public datasets (ACDC, 80 subjects and CMAC, 16 subjects) and a private dataset (SSC, 75 subjects), containing healthy and pathological cases (acute myocardial infarct, DCM and HCM). We measured the mean Dice coefficient and Haussdorff distance for segmentation accuracy, the absolute end point error for motion accuracy, and we conducted a study of the discrimination power of the strain and strain rate between populations of healthy and pathological subjects. The results demonstrated that our method effectively quantifies myocardial strain and strain rate, showing distinct patterns across different cardiac conditions achieving notable statistical significance. Results also show that the method's accuracy is on par with iterative non-parametric registration methods and is also capable of estimating regional strain values. Conclusion: Our method proves to be a powerful tool for cardiac strain analysis, achieving results comparable to other state of the art methods, and computational efficiency over traditional methods. .