Machine Learning for
Tomographic Imaging
Ge Wang
Rensselaer Polytechnic Institute
Yi Zhang
Sichuan University
Xiaojing Ye
Georgia State University
Xuanqin Mou
Xi’an Jiaotong University
IOP Publishing, Bristol, UK
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ISBN 978-0-7503-2216-4 (ebook)
ISBN 978-0-7503-2214-0 (print)
ISBN 978-0-7503-2217-1 (myPrint)
ISBN 978-0-7503-2215-7 (mobi)
DOI 10.1088/978-0-7503-2216-4
Version: 20191201
IOP ebooks
British Library Cataloguing-in-Publication Data: A catalogue record for this book is available from the British Library.
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Contents
Part I Background
1 Background knowledge
1.1 Imaging principles and a priori information
1.1.1 Overview
1.1.2 Radon transform and non-ideality in data acquisition
1.1.5 Data decorrelation and whitening
2 Tomographic reconstruction based on a learned dictionary
2.1 Prior information guided reconstruction
2.2 Single-layer neural network
2.2.1 Matching pursuit algorithm
2.3 CT reconstruction via dictionary learning
2.3.1 Statistic iterative reconstruction framework (SIR)
2.3.2 Dictionary-based low-dose CT reconstruction
3 Artificial neural networks
3.1 Basic concepts
3.1.1 Biological neural network
3.1.4 Discrete convolution and weights
3.1.7 Backpropagation algorithm
3.1.8 Convolutional neural network
3.2 Training, validation, and testing of an artificial neural network
3.2.1 Training, validation, and testing datasets
3.2.2 Training, validation, and testing processes