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Updated: Jan 28, 2026
08:41
Assessment of Cardiac Function and Myocardial Morphology Using Small Animal Look-locker Inversion Recovery SALLI MRI in Rats
Published on: July 19, 2013
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Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional
Wen Wei1,2,3, Emilie Poirion2, Benedetta Bodini2
1Université Côte d'Azur, Inria, Epione Project Team, Sophia Antipolis, France.
Journal of Medical Imaging (Bellingham, Wash.)
|March 2, 2019
Summary
This study introduces a novel AI method using 3D fully convolutional neural networks to generate missing Magnetic Resonance Imaging (MRI) FLAIR sequences from other MRI types, aiding Multiple Sclerosis (MS) lesion detection.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Multiple sclerosis (MS) is a white matter (WM) disease characterized by WM lesions.
- Magnetic resonance imaging (MRI), specifically the fluid-attenuated inversion recovery (FLAIR) sequence, is crucial for detecting these WM lesions.
- Clinical and research settings may face constraints leading to missed MRI pulse sequences.
Purpose of the Study:
- To develop and evaluate a method for synthesizing FLAIR MRI sequences from other available MRI sequences.
- To address the challenge of potentially missed MRI pulse sequences in clinical practice.
Main Methods:
- Utilized three-dimensional fully convolutional neural networks (3D FCNs) to predict FLAIR sequences.
- Employed pulse sequence-specific saliency maps to assess the contribution of each input sequence.
- Tested the approach on a real-world MS image dataset.
Main Results:
- The proposed method demonstrated competitive performance in synthesizing FLAIR sequences.
- Qualitative and quantitative evaluations confirmed the method's efficacy.
- Saliency maps provided insights into the importance of input pulse sequences.
Conclusions:
- The 3D FCN approach is a viable and competitive method for FLAIR MRI synthesis.
- This technique can potentially overcome limitations caused by missed MRI sequences in MS imaging.
- The method aids in improving the detection and characterization of WM lesions in multiple sclerosis.
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