Deep Left Ventricular Motion Estimation Methods in Echocardiography: A Comparative Study.

Area of Science:

• Medical Imaging
• Cardiology
• Artificial Intelligence

Background:

• Accurate motion estimation in echocardiography is vital for assessing heart function and myocardial deformation.
• Current clinical methods for measuring strain indices face limitations in accuracy and reliability.

Purpose of the Study:

• To evaluate deep learning-based motion estimation architectures for determining left ventricular longitudinal strain in echocardiography.
• To assess the performance of different optical flow models on simulated echocardiographic data.

Main Methods:

• Three deep learning motion estimation models (PWC-Net, RAFT, FlowFormer), pretrained on optical flow datasets, were applied to simulated echocardiographic images.
• End-point error was calculated to quantify motion estimation accuracy.
• Global longitudinal strain was computed from FlowFormer outputs to evaluate strain correlation and vendor variability.

Main Results:

• FlowFormer demonstrated the lowest average end-point error (0.09 mm/frame), outperforming RAFT (0.11 mm/frame) and PWC-Net (0.20 mm/frame).
• Strain calculations from FlowFormer outputs showed potential for clinical application.
• Significant variability in strain accuracy was observed across different ultrasound vendors.

Conclusions:

• Deep learning-based optical flow methods, particularly FlowFormer, offer high accuracy for motion estimation in echocardiography.
• These advanced techniques hold promise for enhancing the clinical utility and reliability of echocardiographic strain imaging.
• Addressing vendor-specific variability is crucial for widespread adoption of AI-driven strain analysis.