Worldwide there are growing concerns on radiation induced genetic, cancerous and other diseases. Facing the increasing radiation risk, how to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the state-of-the-art compressive sensing (CS) theory, very recently we proposed a total variation (TV) minimization based statistical reconstruction method for low-dose x-ray CT, which provided very promising results. Noticing that dictionary learning and sparse representation have been successfully applied in the image processing field, in this talk we will present an improved statistical reconstruction method incorporating the dictionary learning and sparse representation techniques. In this method, a sparse constraint on a redundant dictionary is firstly incorporated into a novel object function in a statistical iterative reconstruction (SIR) framework. And the dictionary can be pre-determined before image reconstruction or adaptively updated during the reconstruction process. Then, an alternating minimization algorithm is developed to minimize the objective function. Finally, our method is validated by low-dose x-ray projections from both animal and patient CT studies and evaluated quantitatively by simulation study. Our results show that the proposed method can produce better image quality than the popular TV based method in terms of suppressing noise and preserve structure information.

Dr. Hengyong Yu received his Bachelor’s degrees in information science & technology (1998) and computational mathematics (1998) respectively, and his PhD degree in information & telecommunication engineering (2003) from Xi’an Jiaotong University. He was an Instructor and Associate Professor with the College of Telecommunication Engineering, Hangzhou Dianzi University, from July 2003 to September 2004. From September 2004 to November 2006, he was a postdoctoral fellow and Associate Research Scientist with Department of Radiology, University of Iowa, Iowa City, IA. From November 2006 to May 2010, he was a Research Scientist, the Associate Director of CT Lab, Biomedical Imaging Division, VT-WFU School of Biomedical Engineering & Sciences, Virginia Tech, Blacksburg, VA, USA. Currently, he is an Assistant Professor, the Director of CT Lab, Departments of Radiology and Biomedical Engineering, Wake Forest University Health Sciences, Winston-Salem, NC, USA. His interests include computed tomography and medical image processing. He has authored or coauthored >90 peer-reviewed journal papers. He serves as an Editorial Board member for Signal Processing, Journal of Medical Engineering, CT Theory and Applications, International Journal of Biomedical Engineering and Consumer Health Informatics and Open Medical Imaging Journal, and the leading Guest Editor of the International Journal of Biomedical Imaging for the special issue entitled “Development of Computed Tomography Algorithms”. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and the IEEE Engineering in Medicine and Biology Society (EMBS), and a member of American Association of Physicists in Medicine (AAPM) and the Biomedical Engineering Society (BMES). In 2005, he was honored for an outstanding doctoral dissertation by Xi’an Jiaotong University, and received the first prize for a best natural science paper from the Association of Science & Technology of Zhejiang Province. In January 2012, he received an NSF CAREER award for development of CS-based interior tomography.