Deep Learning for Post-Contrast T1-Weighted Brain MRI Synthesis

Master thesis (2023)

Authors

R.C. van Oosterhoudt Mechanical Engineering

Contributors

F.M. Vos ImPhys/Computational Imaging - AS (supervisor 1)
Sebastian R. van der Voort Erasmus MC (supervisor 2)
Antonio Hernández Erasmus MC (supervisor 2)
Jifke Veenland Erasmus MC (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2023 Ruben van Oosterhoudt
Deep Learning MRI U-Net Enhancement Glioma Post-contrast T1 Synthetic GBCAs
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Published Date

27-02-2023

Language

English

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Faculty

Mechanical Engineering

Abstract

Introduction: Magnetic Resonance Imaging is a commonly used technique for the initial diagnosis of gliomas. T1, T2, T2-FLAIR, and post-contrast T1 with gadolinium-based contrast agents (GBCAs) can show tumor characteristics. However, using this contrast agent poses a risk to patients with kidney failures, has environmental impact, and increases cost. To address these issues, we aimed to evaluate the potential of deep learning in generating synthetic post-contrast T1 images without using contrast agents.

Method: The project investigates the potential of using deep learning (DL) to generate synthetic post-contrast T1 images based on T1, T2, and T2-FLAIR provided by the Erasmus Glioma Database. Exploring different model architectures, loss functions, and input sizes to discover the optimal approach.

Results: Results show that individual loss functions, input size, and model complexity slightly impact the accuracy of synthetic post-contrast T1 images. Combining loss functions, however, was the most promising approach for image generation. Models trained with ℒm could generate low detail enhancement. Resulting in 0.0478±0.0076, 0.0139±0.0036, and 0.879±0.024 for MAE, MSE, and SSIM, respectively.

Conclusion: The study's findings indicate that DL is promising for generating synthetic post-contrast T1 images without using GBCAs. However, further research is required to generate realistic synthetic post-contrast T1 images. The study, however, provides a basis for future work and highlights the importance of reducing the use of GBCAs in clinical practice.