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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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3D Cine Magnetic Resonance Imaging of Respiratory Motion in Mechanically Ventilated Mice and Rats

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Automated motion estimation for 2-D cine DENSE MRI.

Andrew D Gilliam¹, Frederick H Epstein

¹A D Gilliam Consulting, Providence, RI 02908, USA. drew@adgilliam.com

IEEE Transactions on Medical Imaging

|May 12, 2012

Summary

This study introduces the first fully automated method for analyzing cardiac motion and strain using cine displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging. The new automated approach shows good agreement with manual methods, improving efficiency and data interpretation.

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

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Area of Science:

Biomedical Engineering
Medical Imaging
Cardiovascular Research

Background:

Cine displacement encoding with stimulated echoes (DENSE) is a magnetic resonance (MR) technique for measuring tissue displacement.
Current analysis of cine DENSE data relies on manual delineation, limiting throughput and data interpretation.
Automated analysis of cardiac mechanics from DENSE imaging is needed to improve efficiency and access real-time physiological information.

Purpose of the Study:

To present the first fully automated solution for estimating tissue motion and strain from 2-D cine DENSE data.
To validate the performance of the automated algorithm against standard semi-manual analysis methods.

Main Methods:

Development of a novel, fully automated algorithm for motion and strain estimation from 2-D cine DENSE MR images.
Utilized simulated and human cardiac cine DENSE datasets for algorithm testing and validation.

Main Results:

The automated algorithm demonstrated good agreement with the established semi-manual analysis method for cine DENSE data.
Successful estimation of tissue motion and strain was achieved using the automated approach.

Conclusions:

The developed automated solution effectively analyzes cardiac motion and strain from 2-D cine DENSE MR images.
This automation promises to enhance measurement throughput, simplify data interpretation, and potentially reveal new physiological insights during MR exams.