Deep Learning for Biomedical Image Reconstruction

This book provides an in-depth review of modern theories and applications of deep neural networks for image reconstruction in various medical modalities, including X-ray, CT, MRI, and more. It covers background theory, modeling fundamentals, and real-world examples, and highlights recent clinical studies and innovative research in generative models and mathematical theory. Ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.

Format: Hardback
Length: 400 pages
Publication date: 12 October 2023
Publisher: Cambridge University Press

Discover the incredible potential of deep neural networks in image reconstruction with this comprehensive and cutting-edge review of modern theories and applications. Delving into the foundational principles of deep learning, this book takes you on a step-by-step journey to understand how these powerful algorithms can be applied across various modalities, including X-ray, CT, MRI, and more. By seamlessly integrating modeling fundamentals, it unravels the complexities of implementing deep learning in medical imaging.

Real-world examples showcased throughout the book illustrate an interdisciplinary approach to medical image reconstruction processes, showcasing a wide range of imaging applications. Furthermore, recent clinical studies and groundbreaking research in generative models and mathematical theory will ignite your imagination and propel you toward new frontiers in this field. Whether you're a graduate student in Electrical or Biomedical Engineering, Medical Physics, or related disciplines, this book is an invaluable resource for advancing your knowledge and expertise in image reconstruction.

So, unlock the secrets of deep neural networks and embark on a journey to revolutionize the field of medical imaging with this essential guide.

Weight: 856g
Dimension: 175 x 251 x 26 (mm)
ISBN-13: 9781316517512