Estimation of Myocardial Strain and Contraction Phase From Cine MRI Using Variational Data Assimilation.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Cardiology

Background:

• Accurate assessment of left ventricle (LV) function is crucial for diagnosing cardiac diseases.
• Current methods for quantifying LV deformation and contraction may have limitations in accuracy and scope.

Purpose of the Study:

• To develop and validate a new method for estimating LV deformations and contraction parameters using variational data assimilation.
• To integrate cardiac MRI observations with a dynamic heart evolution model for improved diagnostic information.

Main Methods:

• A variational data assimilation framework combining cine MRI data with a dynamic heart model.
• Simultaneous estimation of myocardial motion and contraction/relaxation parameters.
• Application to synthetic data and 47 patient MRI datasets across multiple slice locations.

Main Results:

• The method demonstrated good agreement with a reference tag tracking software for radial and circumferential strain (ICC > 0.8).
• The dynamic model effectively handled temporal artifacts, outperforming methods relying solely on observation terms.
• A piecewise transport model improved performance over continuous models by preserving temporal variations.

Conclusions:

• The proposed method provides accurate, complementary information for diagnosing LV dysfunction.
• Estimated strain and contraction parameters show significant correlation with clinical scores, aiding in the diagnosis of hypokinesia and dyskinesia.