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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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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

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Published on: December 18, 2016

Quantitative performance assessments for neuromagnetic imaging systems.

Ryo Koga, Ei Hiyama, Takuya Matsumoto

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 11, 2013

Summary

This study introduces a Monte Carlo simulation to evaluate neuromagnetic imaging system performance using A-prime and spatial resolution metrics. Results show performance improves with more sensors and vary between axial and planar gradiometer systems.

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Area of Science:

Biomedical Engineering
Neuroscience
Computational Physics

Background:

Neuromagnetic imaging systems are crucial for non-invasively studying brain activity.
Assessing the performance of these systems is essential for accurate diagnostic and research applications.
Existing performance evaluation methods may not fully capture the nuances of different sensor configurations.

Purpose of the Study:

To develop and apply a Monte Carlo simulation method for evaluating neuromagnetic imaging system performance.
To quantify system performance using A-prime and spatial resolution metrics.
To compare the performance of virtual sensor systems with varying sensor counts and existing commercial MEG systems.

Main Methods:

Utilized a Monte Carlo simulation approach.
Calculated A-prime and spatial resolution metrics.
Simulated virtual sensor arrays (80, 160, 320, 640 sensors) and analyzed real-world MEG systems (MEGvision™, TRIUX™).

Main Results:

Neuromagnetic imaging system performance, measured by A-prime and spatial resolution, generally improves with an increased number of sensors.
Significant performance differences were observed between systems employing axial-gradiometer sensors and those using planar-gradiometer and magnetometer sensors.
The simulation provided a quantitative basis for comparing distinct neuromagnetic imaging system designs.

Conclusions:

The developed Monte Carlo simulation is a viable tool for assessing neuromagnetic imaging system performance.
Sensor configuration and quantity significantly impact system efficacy.
This methodology aids in understanding and optimizing the design of future neuromagnetic imaging technologies.