Ge Wang, an endowed chair professor of biomedical engineering at Rensselaer Polytechnic Institute, recently published the first book on the foundation of deep neural networks and specific applications in tomographic imaging, in collaboration with his three co-authors.

The book, titled Machine Learning for Tomographic Imaging, is the latest in a long list of accomplishments by Wang. For example, he was recently named a fellow of the National Academy of Inventors.

Throughout his career, Wang has developed approaches to improve medical images. The methods that he has created related to CT imaging and other forms of tomography aim to deepen understanding of diseases and improve diagnosis and treatment.

He and his team are currently working on a cross-disciplinary study supported by the National Institutes of Health focused on combining innovative photon-counting X-ray and lifetime optical imaging technologies developed at Rensselaer. The goal is to enable biologists to observe drug delivery and its effect on cancer cells in living organisms — including people.