Part IV Others
8 Modalities and integration
8.1 Nuclear emission tomography
8.1.2 Network-based emission tomography
8.2.2 Network-based ultrasound imaging
8.3.1 Interferometric and diffusive imaging
8.3.2 Network-based optical imaging
9 Image quality assessment
10 Quantum computing*
A Math and statistics basics
B Hands-on networks
IPEM–IOP Series in Physics and Engineering in Medicine and Biology
Editorial Advisory Board Members
Frank Verhaegen Maastro Clinic, the Netherlands
Carmel Caruana University of Malta, Malta
Penelope Allisy-Roberts formerly of BIPM, Sèvres, France
Rory Cooper University of Pittsburgh, USA
Alicia El Haj University of Birmingham, UK
Ng Kwan Hoong University of Malaya, Malaysia
John Hossack University of Virginia, USA
Tingting Zhu University of Oxford, UK
Dennis Schaart TU Delft, the Netherlands
Indra J Das New York University, USA
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For my wife Ying Liu, whose support has been extraordinary, my academic mentors Michael Vannier et al, and all other family members and collaborators.
—Ge Wang
For my wife and daughter, who have been always fully supporting my academic career development.
—Yi Zhang
For my family.
—Xiaojing Ye
For my family.
—Xuanqin Mou
Foreword
We are currently witnessing a revolution in science and engineering: in only a few years, machine learning has become the basis for almost every single algorithm development. I still remember a college course on non-linear systems in the early 1990s: except for a few zealots, my peers and I used to think of neural networks as an exotic and entirely impractical field of science. Little did we realize how wrong we were. Increases in computing power, access to large amounts of data, and the creativity of many scientists have entirely changed that view. Today, neural networks have become more pervasive than the Fourier transform.
Some of the most popular applications of machine learning are in image analysis. You probably have some powerful deep learning networks in your pocket: just type ‘dog’ or ‘hat’ on your smart phone and it will instantaneously find all pictures containing your targets of interest. The application of machine learning may be less obvious in other areas, such as image generation. By now, machine learning has been at least considered for almost every imaginable algorithmic challenge. In the field of medical imaging, machine learning techniques