Academic Lecture: X-ray Imaging through Machine Learning


Speaker:Ge Wang, PhD (Clark & Crossan Endowed Chair Professor and Director of Biomedical Imaging, Rensselaer Polytechnic Institute, Troy, NY, USA) 
Moderator: Prof.Wei Long
Time: 15:00, August 9
Place: Room C305, IHEP Main Building

Computer vision and image analysis are great examples of machine learning, especially deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic image reconstruction produces images of internal structures from measurement data, which are various features (attenuated/non-attenuated line integrals, Fourier/harmonic components, echoed/scattered/transmitted ultrasound signatures, diffused/excited/interfered light signals, and so on) of the underlying images (features to images). Recently, machine learning, especially deep learning, techniques are being actively developed worldwide for tomographic image reconstruction, which is a new area of research as shown by the perspective, the 20 high-quality papers included in the latest TMI special issue,-June-2018, as well as similar publications in other journals and conferences. In addition to well-known analytic and iterative methods for tomographic image reconstruction, machine learning is an emerging approach for image reconstruction, and likewise image reconstruction is a new frontier of machine learning. There are exciting research and application opportunities ahead for smart imaging and precision medicine. 

About the speaker: 
Ge Wang is the Clark & Crossan Endowed Chair Professor and the Director of the Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA. He authored the pioneering paper on the first spiral/helical cone-beam/multi-slice CT algorithm in 1991. Currently, there are over 100 million medical CT scans yearly with a majority in the spiral/helical cone-beam/multi-slice mode. He pioneered the area of bioluminescence tomography. His group published the first papers on interior tomography and omni-tomography (“all-in-one”) to acquire diverse datasets simultaneously (“all-at-once”) with CT-MRI as an example. His results were featured in Nature, Science, and PNAS, and recognized with academic awards. He wrote over 430 peer-reviewed journal publications, receiving a high number of citations. His team has been in collaboration with world-class groups and continuously well-funded. His interest includes x-ray CT, optical molecular tomography, multimodality fusion, and machine learning for medical imaging (supported by GE Global Research Center). He is the Lead Guest Editor of the five IEEE Transactions on Medical Imaging Special Issues, the founding Editor-in-Chief of the International Journal of Biomedical Imaging, and an Associate Editor of the IEEE Transactions on Medical Imaging, Medical Physics, and other journals. He is a fellow of the IEEE, SPIE, OSA, AIMBE, AAPM, and AAAS.