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Magnetic Resonance Imaging

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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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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview.

Abrar Faiyaz¹, Marvin M Doyley^1,2,3, Giovanni Schifitto^1,2,4

¹Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States.

Frontiers in Neurology

|May 8, 2023

Summary

This summary is machine-generated.

Artificial intelligence (AI) enhances diffusion MRI (dMRI) for brain microstructure analysis. This review explores AI

Keywords:

artificial intelligence biophysical model brain deep learning diffusion MRI (dMRI)machine learning microstructure neuroimaging

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Area of Science:

Neuroimaging
Biophysics
Artificial Intelligence

Background:

Diffusion magnetic resonance imaging (dMRI) and other neuroimaging techniques are increasingly utilizing Artificial Intelligence (AI).
AI applications span image reconstruction, denoising, artifact removal, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnostic support.
AI offers potential for optimizing dMRI techniques and biophysical models to improve sensitivity and inference.

Purpose of the Study:

To review and classify AI-based approaches in dMRI.
To highlight best practices and potential pitfalls in AI-driven tissue microstructure estimation.
To provide directions for future advancements in the field.

Main Methods:

Classification of AI-based dMRI approaches based on unifying characteristics.
Review of data-driven techniques for tissue microstructure estimation.
Analysis of data engineering strategies leveraging q-space geometry for enhanced inference.

Main Results:

AI significantly advances dMRI applications in neuroimaging.
Leveraging q-space geometry in data engineering can improve inference quality and pathological difference identification.
Awareness of pitfalls and adherence to best practices are crucial for AI in brain microstructure studies.

Conclusions:

AI holds transformative potential for studying the brain and understanding neurological disorders.
Optimizing AI algorithms and data engineering in dMRI is key to advancing neuroimaging.
Further research into best practices and pitfalls will accelerate AI's impact on brain microstructure analysis.