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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

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.

Andrew Mao¹, Sebastian Flassbeck¹, Cem Gultekin²

¹Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016.

IEEE Transactions on Bio-Medical Engineering

|August 20, 2024

Summary

This study introduces a new quantitative MRI method that preserves signal energy and the Cramér-Rao bound (CRB) for more accurate biophysical parameter mapping. This improves precision in quantitative MRI, benefiting advanced techniques like diffusion imaging.

Keywords:

Cramér-Rao bound magnetic resonance fingerprinting magnetization transfer quantitative MRI singular value decomposition subspace reconstruction

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Last Updated: Jun 16, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

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3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

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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:

Magnetic Resonance Imaging
Biophysical Modeling
Quantitative Imaging

Background:

Traditional quantitative MRI methods focus on maximizing signal energy preservation during subspace estimation.
This can lead to suboptimal accuracy and precision in estimated biophysical parameters.

Purpose of the Study:

To extend the traditional framework for quantitative MRI subspace estimation.
To simultaneously preserve signal energy and the Cramér-Rao bound (CRB) of biophysical parameters.
To enhance accuracy and precision in quantitative MRI maps.

Main Methods:

Introduction of an approximate compressed CRB using orthogonalized signal derivatives.
Application of singular value decomposition (SVD) for minimizing CRB and signal loss during compression.
Development of a subspace reconstruction method utilizing a compact basis.

Main Results:

The proposed method demonstrates superior CRB preservation across biophysical parameters compared to traditional SVD.
Minimal compromise in preserved signal energy was observed.
Reduced bias and variance in parameter estimates were confirmed through simulations and in vivo neuroimaging applications.

Conclusions:

The novel approach enables subspace reconstruction with more compact bases, leading to significant computational savings.
Efficient subspace reconstruction supports the validation and translation of advanced quantitative MRI techniques.
Improved accuracy and precision in quantitative neuroimaging are achievable.