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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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Related Experiment Video

Updated: May 15, 2025

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

Published on: May 7, 2017

Physics-driven Learned Deconvolution of Multi-spectral Cellular MRI with Radial Sampling.

Jiawen Chen¹, Eric T Ahrens², Piya Pal¹

¹Department of Electrical and Computer Engineering, University of California San Diego, USA.

Conference Record. Asilomar Conference on Signals, Systems & Computers

|April 10, 2025

Summary

This summary is machine-generated.

This study introduces radial spectral deconvolution for fluorine-19 (¹⁹F) MRI, improving multi-target cell detection by modeling and removing spectral artifacts. The new methods enhance weak signal detection in low SNR conditions.

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Last Updated: May 15, 2025

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Area of Science:

Medical Imaging
Magnetic Resonance Imaging
Spectroscopy

Background:

Fluorine-19 (¹⁹F) cellular MRI requires unmixing images from multiple cell targets with distinct chemical shifts.
Conventional Cartesian sampling causes well-defined chemical shift artifacts ('ghost images').
Radial sampling introduces complex, non-linear smearing artifacts due to frequency offsets.

Purpose of the Study:

To develop radial spectral deconvolution methods for ¹⁹F MRI.
To model radial chemical shift artifacts using Radon transform.
To design sensing operators for multi-spectral imaging tasks and improve artifact removal.

Main Methods:

Modeling radial chemical shift artifacts via Radon transform.
Developing physics-informed learning-based unrolling strategies.
Joint design of acquisition schemes and sampling patterns for artifact deconvolution.

Main Results:

Successfully modeled radial chemical shift artifacts.
Enabled simultaneous artifact removal and weak signal detection.
Improved spectral unmixing for multi-target ¹⁹F MRI.

Conclusions:

Radial spectral deconvolution is effective for ¹⁹F MRI artifact management.
Physics-informed learning enhances artifact removal and signal detection in low SNR.
The developed methods are crucial for advanced multi-spectral ¹⁹F cellular imaging.