Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation.

Area of Science:

• Medical imaging analysis
• Computational cardiovascular dynamics
• Machine learning in healthcare

Background:

• Cardiovascular diseases (CVDs) are a leading cause of mortality, necessitating accurate assessment of cardiac dynamics.
• Cardiac motion estimation is crucial for clinical tasks and understanding cardiac cycle variations.
• Existing image registration methods face challenges in accurately capturing complex cardiac deformations.

Purpose of the Study:

• To propose a probabilistic framework for cardiac motion estimation using compact support radial basis functions (CSRBFs).
• To develop deep learning models for learning probabilistic coefficients of CSRBFs for image deformation.
• To evaluate the framework's performance in cardiac motion estimation and myocardial strain calculation.

Main Methods:

• A generative model using variational inference and convolutional neural networks (CNNs) to learn CSRBF coefficients.
• Two CNN architectures: one for fixed control points and another for drifting control points.
• Derivation of bending energy (BE) for regularization of coefficients within a variational bound.

Main Results:

• The proposed framework demonstrated superior performance compared to state-of-the-art methods in cardiac motion estimation.
• Achieved enhanced deformation smoothness and registration accuracy in experiments.
• Validated on 1409 cardiac MR image slice pairs from public datasets.

Conclusions:

• The probabilistic framework effectively estimates cardiac motion and myocardial strain.
• The deep learning-based approach offers improved accuracy and smoothness in cardiac image registration.
• This method holds potential for advancing cardiovascular disease diagnosis and monitoring.