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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

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SegMorph: Concurrent Motion Estimation and Segmentation for Cardiac MRI Sequences.

Ning Bi, Arezoo Zakeri, Yan Xia

IEEE Transactions on Medical Imaging

|August 5, 2024

Summary

This summary is machine-generated.

We developed SegMorph, a new recurrent variational network for simultaneous segmentation and motion estimation in cardiac MRI. This advanced model improves accuracy for both tasks in cine-MRI sequences.

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Research

Background:

Cardiac cine magnetic resonance imaging (CMR) is crucial for diagnosing heart conditions.
Accurate segmentation and motion estimation are vital for quantitative analysis of cardiac function.
Existing methods often struggle with concurrent, precise estimation of both segmentation and motion from dynamic CMR sequences.

Purpose of the Study:

To introduce SegMorph, a novel recurrent variational network designed for simultaneous segmentation and motion estimation in CMR sequences.
To leverage a recurrent latent space for capturing spatiotemporal features for multitask inference.
To enhance the performance of both segmentation and motion estimation through their synergistic interaction.

Main Methods:

Developed SegMorph, a recurrent variational network based on a variational auto-encoder framework.
Utilized a recurrent latent space with a learned prior from temporal inputs to capture spatiotemporal dynamics.
Employed a multi-branch decoder for concurrent bi-ventricular segmentation and motion estimation.
Integrated motion estimation as pseudo-ground truth for segmentation and used segmentation to predict deformation vector fields (DVFs) for motion estimation.

Main Results:

SegMorph outperformed state-of-the-art methods in both segmentation and motion estimation tasks, achieving superior qualitative and quantitative results.
Achieved an average Dice Similarity Coefficient (DSC) of 81% and a Hausdorff distance of less than 3.5 mm for segmentation.
Obtained a motion estimation DSC exceeding 79% with minimal negative Jacobian determinants (approx. 0.14%) in estimated DVFs.

Conclusions:

SegMorph effectively performs concurrent segmentation and motion estimation on CMR sequences.
The recurrent latent space and multitask learning approach significantly improve performance.
The proposed method offers a robust and accurate solution for quantitative analysis of cardiac function from cine-MRI.