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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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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Related Experiment Video

Updated: May 17, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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Published on: April 14, 2014

Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.

Rim Missaoui^1,2, Wided Hechkel¹, Wajdi Saadaoui³

¹Laboratory of Micro-Optoelectronics and Nanostructures (LMON), University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This summary is machine-generated.

Machine learning and deep learning significantly improve brain tumor diagnosis by analyzing medical imaging like MRI. These advanced algorithms enhance detection, segmentation, classification, and survival prediction for better patient outcomes.

Keywords:

brain tumors deep learning (DL)machine learning (ML)magnetic resonance imaging (MRI)

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Published on: January 7, 2019

Area of Science:

Neuro-oncology
Medical Imaging
Artificial Intelligence

Background:

Brain tumors are complex CNS cell growths, challenging to diagnose and treat.
Magnetic Resonance Imaging (MRI) is the preferred modality due to its safety and contrast resolution.
Accurate diagnosis is crucial for effective, personalized treatment strategies.

Purpose of the Study:

To review advances in using machine learning (ML) and deep learning (DL) with medical imaging for brain tumor diagnosis.
To explore how ML/DL algorithms improve various stages of brain tumor analysis.
To assess the impact of these technologies on diagnostic precision and treatment planning.

Main Methods:

Systematic analysis of 107 studies published between 2018 and 2024.
Focus on studies utilizing ML, DL, and hybrid models for brain tumor analysis.
Inclusion of research using public datasets like BraTS, TCIA, and Figshare.

Main Results:

ML and DL algorithms show significant improvements in brain tumor detection, segmentation, classification, and survival prediction.
Advanced algorithms accurately identify tumor characteristics, aiding diagnostic precision.
The integration of ML/DL with MRI enhances diagnostic capabilities.

Conclusions:

ML and DL, particularly with MRI, offer powerful tools for advancing brain tumor diagnosis.
These AI-driven approaches are key to enhancing diagnostic accuracy and enabling personalized therapeutic strategies.
Continued research in this area promises further improvements in neuro-oncology care.