Discover the power of deep neural networks for image reconstruction
with this state-of-the-art review of modern theories and
applications. The background theory of deep learning is introduced
step-by-step, and by incorporating modeling fundamentals this book
explains how to implement deep learning in a variety of modalities,
including X-ray, CT, MRI and others. Real-world examples
demonstrate an interdisciplinary approach to medical image
reconstruction processes, featuring numerous imaging applications.
Recent clinical studies and innovative research activity in
generative models and mathematical theory will inspire the reader
towards new frontiers. This book is ideal for graduate students in
Electrical or Biomedical Engineering or Medical Physics.
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