Machine Learning for Tomographic Imaging

Name: Machine Learning for Tomographic Imaging
Brand: IOP Publishing
Price: 154.75 AUD

By: Professor Ge Wang, Professor Yi Zhang, Professor Xuanqin Mou, Professor Xiaojing Ye

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Hardcover | 30 December 2019

At a Glance

Hardcover
412 Pages

Dimensions(cm)
25.4 x 17.8 x 2.4

Hardcover

$154.75

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The area of machine learning, especially deep learning, has exploded in recent years, producing advances in everything from speech recognition and gaming to drug discovery. Tomographic imaging is another major area that is being transformed by machine learning, and its potential to revolutionise medical imaging is significant.

Written by active researchers in the field, Machine Learning for Tomographic Imaging presents a unified overview of deep-learning-based tomographic imaging. Key concepts, including classic reconstruction ideas and human vision inspired insights, are introduced as a foundation for a thorough examination of artificial neural networks and deep tomographic reconstruction. X-ray CT and MRI reconstruction methods are covered in detail, and other medical imaging applications are discussed.

An engaging and accessible style makes this book an ideal introduction for those in applied disciplines, as well as those in more theoretical fields who wish to learn about application contexts. Hands-on projects are also suggested, and links to open source software, working datasets, and network models are included.

Industry Reviews

Machine Learning for Tomographic Imaging, presents a detailed overview of the emerging discipline of deep-learning-based tomographic imaging.

The book begins with an introduction to imaging principles, tomographic reconstruction and artificial neural networks. Parts two and three provide in-depth tutorials on CT and MR image reconstruction and describe a range of recent machine learning techniques. The final part of the book covers other imaging modalities, including PET, SPECT, ultrasound and optical imaging, as well as taking a look at image quality evaluation and quantum computing. The text also includes appendices describing relevant numerical methods and suggesting hands-on projects, with sample codes and working datasets.

Tami Freeman 2020 Physics World IOP Publishing

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