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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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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine

Álvaro Planchuelo-Gómez¹, Maxime Descoteaux², Hugo Larochelle³

¹Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; Imaging Processing Laboratory, Universidad de Valladolid, Valladolid, Spain.

Medical Image Analysis

|March 12, 2024

Summary

This summary is machine-generated.

This study introduces a physics-informed machine learning framework to optimize diffusion-relaxation MRI protocols. The method significantly shortens scan times while maintaining accurate quantitative parameter estimation for brain tissue characterization.

Keywords:

Brain Diffusion-relaxation Machine learning Quantitative MRI

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Area of Science:

Medical Imaging
Biophysics
Machine Learning

Background:

Diffusion-relaxation MRI provides quantitative microstructural tissue properties.
Current methods require long acquisition times, limiting clinical utility.
Optimizing measurement subsets is crucial for efficient MRI protocols.

Purpose of the Study:

To develop a physics-informed learning framework for selecting optimal diffusion-relaxation MRI measurements.
To enable shorter acquisition times through intelligent subset selection.
To predict non-measured signals and accurately estimate quantitative parameters.

Main Methods:

Implemented a physics-informed machine learning framework using in vivo and synthetic brain 5D-Diffusion-T1-T2*-weighted MRI data.
Compared physics-informed approaches against data-driven methods, manual selection, and Cramér-Rao lower bound optimization.
Utilized data from six healthy subjects for training, validation, and testing.

Main Results:

Physics-informed approaches identified measurement subsets yielding more accurate parameter estimates in simulations.
Five-fold shorter protocols resulted in minimal error in estimated quantitative parameters compared to full protocols.
Selected subsets favored denser sampling of specific inversion times, echo times, and b-values.

Conclusions:

The proposed framework effectively combines machine learning and MRI physics for protocol optimization.
Shorter diffusion-relaxation MRI protocols can be developed without compromising parameter estimate and signal prediction quality.
This approach offers a promising strategy for efficient brain tissue characterization using MRI.