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Chapter 1 Magnetic resonance image re-parameterization on real data

  • Melika Abdollahi , Heidar Davoudi and Mehran Ebrahimi
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Imaging Science
This chapter is in the book Imaging Science

Abstract

Magnetic resonance (MR) image re-parameterization involves creating an MR image with a modified set of magnetic resonance imaging (MRI) scanning parameters. Adjusting these parameters produces varying contrasts between tissues, aiding in the detection of pathological changes. While multiple scans are often necessary for accurate diagnosis, obtaining them can be expensive, time-consuming and challenging for patients. As an alternative, MR image re-parameterization provides a method to predict and estimate contrast in imaging scans. In this work, we introduce an innovative model for MRI re-parameterization that combines autoencoders with cycle-generative adversarial networks. Unlike prior work that relied on synthetic data, we validate the method on Duke’s Breast Cancer MRI dataset, showing the effectiveness of the model in generating translated MR images with different acquisition parameters.

Abstract

Magnetic resonance (MR) image re-parameterization involves creating an MR image with a modified set of magnetic resonance imaging (MRI) scanning parameters. Adjusting these parameters produces varying contrasts between tissues, aiding in the detection of pathological changes. While multiple scans are often necessary for accurate diagnosis, obtaining them can be expensive, time-consuming and challenging for patients. As an alternative, MR image re-parameterization provides a method to predict and estimate contrast in imaging scans. In this work, we introduce an innovative model for MRI re-parameterization that combines autoencoders with cycle-generative adversarial networks. Unlike prior work that relied on synthetic data, we validate the method on Duke’s Breast Cancer MRI dataset, showing the effectiveness of the model in generating translated MR images with different acquisition parameters.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Section: Image processing
  5. Chapter 1 Magnetic resonance image re-parameterization on real data 1
  6. Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
  7. Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
  8. Chapter 4 Image clustering enhanced with refined image classification 59
  9. Chapter 5 AI-powered framework for objective scoring of product design innovation 89
  10. Section: Computer vision
  11. Chapter 6 Image inpainting using GAN transformerbased model 111
  12. Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
  13. Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
  14. Chapter 9 Implementation of deep learning techniques on thermal image classification 173
  15. Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
  16. Section: Pattern recognition
  17. Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
  18. Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
  19. Index 273
https://doi.org/10.1515/9783111436425-001

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Section: Image processing
  5. Chapter 1 Magnetic resonance image re-parameterization on real data 1
  6. Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
  7. Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
  8. Chapter 4 Image clustering enhanced with refined image classification 59
  9. Chapter 5 AI-powered framework for objective scoring of product design innovation 89
  10. Section: Computer vision
  11. Chapter 6 Image inpainting using GAN transformerbased model 111
  12. Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
  13. Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
  14. Chapter 9 Implementation of deep learning techniques on thermal image classification 173
  15. Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
  16. Section: Pattern recognition
  17. Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
  18. Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
  19. Index 273
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