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NMR Spectrometers: Resolution and Error Correction

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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...

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Related Experiment Video

Updated: May 14, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Multipeak fat-corrected complex R2* relaxometry: theory, optimization, and clinical validation.

Diego Hernando¹, J Harald Kramer, Scott B Reeder

¹Departments of Radiology, University of Wisconsin, Madison, Wisconsin, USA.

Magnetic Resonance in Medicine

|January 30, 2013

Summary

This summary is machine-generated.

Developing accurate R2* mapping requires correcting for confounding factors like noise and fat. Fat-corrected R2* mapping improves robustness and accuracy, establishing it as a reliable liver imaging biomarker.

Keywords:

Cramer-Rao bound R2* relaxometry iron overload quantitative imaging biomarkers

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Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol

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Published on: September 7, 2018

Area of Science:

Magnetic Resonance Imaging
Quantitative Imaging
Biomarker Development

Background:

Conventional R2* mapping is susceptible to noise-related bias and fat presence in tissues.
Noise floor effects particularly bias magnitude-based reconstructions at high R2* values.
Uncorrected fat presence introduces significant, protocol-dependent bias in R2* measurements.

Purpose of the Study:

To develop R2* mapping techniques with corrected confounding factors.
To optimize R2* mapping for improved noise performance.
To establish R2* as a quantitative imaging biomarker in the liver.

Main Methods:

Characterized bias/noise properties of R2* reconstructions (magnitude, complex-fitting, fat-uncorrected, fat-corrected) using Cramer-Rao Bound analysis, simulations, and in vivo data.
Developed a framework for optimizing echo time selection.
Evaluated the robustness of liver R2* mapping in 28 subjects with varying fat content.

Main Results:

Fat-corrected R2* mapping effectively removes fat-related bias without compromising noise performance across a wide R2* range.
Complex nonlinear least-squares fitting with fat correction provides robust R2* estimates with low bias and optimized noise.
Optimized echo time combinations enhance R2* estimation accuracy.

Conclusions:

Complex fitting and fat-correction are crucial for improving R2* measurement robustness, noise performance, and accuracy.
These advancements are necessary for establishing R2* as a reliable quantitative imaging biomarker in liver applications.
The developed techniques offer enhanced precision for liver R2* quantification.