Machine learning for tomographic imaging – Physics World

The field of artificial intelligence and machine learning, particularly the subcategory of deep learning, has experienced massive growth in recent years, with applications ranging from speech recognition to material inspection, healthcare to gaming, to name but a few. One area that's being transformed by machine learning is tomographic imaging – in which a series of data projections (such as X-ray radiographs, for example) are reconstructed into a three-dimensional image. A newly published book, Machine Learning for Tomographic Imaging, presents a detailed overview of the emerging discipline of deep-learning-based tomographic imaging. The book arose from discussions among four colleagues with a long-standing interest in advanced medical image reconstruction: Ge Wang from Rensselaer Polytechnic Institute, Yi Zhang of Sichuan University, Xiaojing Ye from Georgia State University and Xuanqin Mou from Xi'an Jiaotong University. "Deep tomographic reconstruction is a new area, and the development of this area has been rapid over the past years," explains Wang.