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Generative Adversarial Networks for Brain MR Image Synthesis and Its Clinical Validation on Multiple Sclerosis

In: Generative Machine Learning Models in Medical Image Computing

Author

Listed:
  • Hongwei Bran Li

    (Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School)

  • Bene Wiestler

    (Technical University of Munich, AI for Image-Guided Diagnosis and Therapy)

Abstract

This chapter explores the application of generative adversarial networks (GANs) for synthesizing brain magnetic resonance imaging sequences in the context of multiple sclerosis (MS). It presents advanced MRI synthesis methods, including lesion-focused loss functions for improved lesion appearance and uncertainty quantification in synthetic images. It details the technical aspects of GANs, including their architecture, training, and optimization, and discusses their clinical applications from diagnostic enhancements to their integration into multi-center studies. Furthermore, the chapter assesses the validation of these models in clinical settings, showcasing their ability to enhance diagnostic accuracy, detecting and monitoring MS. Through extensive experiments and reader studies with experienced radiologists, it was demonstrated that synthetic images achieve high-quality clinical utility. Finally, the chapter discusses the limitations and future directions of generative MRI synthesis in MS, highlighting its potential to impact clinical practice and patient care.

Suggested Citation

  • Hongwei Bran Li & Bene Wiestler, 2025. "Generative Adversarial Networks for Brain MR Image Synthesis and Its Clinical Validation on Multiple Sclerosis," Springer Books, in: Le Zhang & Chen Chen & Zeju Li & Greg Slabaugh (ed.), Generative Machine Learning Models in Medical Image Computing, chapter 0, pages 165-180, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-80965-1_9
    DOI: 10.1007/978-3-031-80965-1_9
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