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Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection
Ovidijus Grigas1, Robertas Damaševičius1,2, Rytis Maskeliūnas1
1Faculty of Informatics, Kaunas University of Technology, 50254 Kaunas, Lithuania.
Brain Sciences
|April 27, 2024
Summary
Super-resolution of structural MRI images enhances mild cognitive impairment (MCI) detection. Optimized deep learning models improve diagnostic accuracy by refining image quality and reducing overfitting.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neuroscience
Background:
- Mild cognitive impairment (MCI) detection is crucial for early intervention.
- Structural magnetic resonance imaging (MRI) is a key diagnostic tool.
- Improving the resolution and quality of MRI scans can enhance diagnostic accuracy.
Purpose of the Study:
- To enhance the detection of mild cognitive impairment (MCI) using super-resolved structural MRI.
- To improve the perceptual quality of super-resolved 2D structural MRI images.
- To optimize deep learning models for enhanced MCI detection.
Main Methods:
- Employed advanced loss functions and generator modifications for super-resolution.
- Utilized generative adversarial training with various discriminators.
- Implemented hyperparameter optimization via Pareto optimal Markov blanket (POMB) to reduce overfitting.
Main Results:
- Super-resolution significantly improved MCI detection performance across multiple classification models.
- The proposed methodology effectively addressed perceptual quality challenges, including checkerboard artifacts.
- Hyperparameter optimization enhanced model generalizability.
Conclusions:
- Super-resolution is a valuable preprocessing step for improving MRI-based MCI detection.
- Careful selection of discriminators and hyperparameter tuning are critical for optimal performance.
- This approach offers a promising avenue for earlier and more accurate MCI diagnosis.
