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Implementation and Validation of a Three-dimensional Cardiac Motion Estimation Network.

Manuel A Morales¹, David Izquierdo-Garcia¹, Iman Aganj¹

¹Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 149 13th St, Charlestown, MA 02129 (M.A.M., D.I.G., I.A., J.K.C., B.R.R., C.C.); Harvard-MIT Division of Health Sciences and Technology (M.A.M.) and Computer Science and Artificial Intelligence Laboratory (I.A.), Massachusetts Institute of Technology, Cambridge, Mass.

Radiology. Artificial Intelligence

|February 21, 2020

Summary

This summary is machine-generated.

A novel deep learning network, Cardiac Motion Estimation Network (CarMEN), accurately estimates 3D cardiac motion from 2D MRI scans. This unsupervised approach outperforms existing methods in motion characterization and image registration accuracy.

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Research

Background:

Accurate estimation of cardiac motion is crucial for diagnosing cardiovascular diseases.
Current non-rigid registration methods face challenges in precision and computational efficiency.

Purpose of the Study:

To introduce an unsupervised deep learning network, Cardiac Motion Estimation Network (CarMEN), for 3D cardiac motion estimation.
To evaluate CarMEN's performance against state-of-the-art non-rigid registration techniques.

Main Methods:

CarMEN, a convolutional neural network, processes 3D volumes to output motion fields.
A smoothness constraint was applied using the Jacobian matrix's Frobenius norm.
The network was trained and validated on diverse datasets, including patient, synthetic, and pediatric data.

Main Results:

CarMEN demonstrated superior performance on synthetic datasets, achieving a median Dice Similarity Coefficient (DSC) of 0.85.
On real patient data, CarMEN achieved a median DSC of 0.73 for Automated Cardiac Diagnosis Challenge data and 0.77 for pediatric data.
The network consistently outperformed or matched other methods across various performance metrics.

Conclusions:

The proposed deep learning approach provides accurate 3D cardiac motion estimation.
CarMEN effectively balances motion characterization and image registration accuracy.
This method achieves comparable or superior accuracy to existing state-of-the-art algorithms.