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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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Updated: Jun 21, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Joint Brain Tumor Segmentation from Multi-magnetic Resonance Sequences through a Deep Convolutional Neural Network.

Farzaneh Dehghani¹, Alireza Karimian¹, Hossein Arabi²

¹Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.

Journal of Medical Signals and Sensors

|July 12, 2024

Summary

Automated brain tumor segmentation using deep learning showed the Fluid-attenuated inversion recovery (FLAIR) sequence yields the best single-sequence accuracy. Combining all four MRI sequences achieved the highest overall accuracy for brain tumor delineation.

Keywords:

Brain tumor deep learning magnetic resonance sequence segmentation

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Manual brain tumor segmentation is time-consuming and subjective.
Automated segmentation improves diagnostic consistency and treatment planning.
Deep learning offers a promising approach for automated brain tumor delineation.

Purpose of the Study:

To automate brain tumor segmentation using deep learning.
To evaluate the accuracy of individual and combined MRI sequences (FLAIR, T1W, T2W, T1ce) for segmentation.
To identify the optimal MRI sequence or combination for accurate brain tumor delineation.

Main Methods:

Utilized the BraTS-2020 dataset with 370 subjects and four MRI sequences.
Trained a residual neural network for segmentation.
Assessed models using single-channel (individual sequences) and multi-channel (combined sequences) inputs.

Main Results:

FLAIR sequence achieved the highest accuracy (Dice index 0.77 ± 0.10) among single sequences.
Combining FLAIR and T2W sequences improved accuracy (Dice index 0.80 ± 0.10).
Segmenting all four sequences yielded the highest overall accuracy (Dice index 0.82 ± 0.09).

Conclusions:

FLAIR is the optimal single MRI sequence for brain tumor segmentation.
Combining all four MRI sequences (FLAIR, T1W, T2W, T1ce) provides the highest accuracy for tumor delineation.