HomeNewsListPh.D candidate Hao Nan visited on October 21, 2016

Ph.D candidate Hao Nan visited on October 21, 2016

Ph.D. candidate Hao Nan from Stanford University visited our lab on October 21, 2016. He gave a talk on "When microwave meets ultrasound: a hybrid system in medical imaging" in Room 9-206, Rohm Building at 10:30AM.


Hao Nan received the B.S. degree in microelectronics with honor from Tsinghua University, Beijing, China, in 2011, and the M.S. degree in electrical engineering from Stanford University, Stanford, CA, USA, in 2013. He is currently a Ph.D. candidate in electrical engineering at Stanford University. From December 2014 to March 2015, he was with the flat panel display team at Apple Inc., Cupertino where he worked on next-generation display system. His research interests include biomedical imaging, machine learning, analog & RF circuits design and system design. Mr. Nan was a receipt of the Larry C.K. Yung Graduate Student Fellowship in 2011, Analog Devices Outstanding Student Designer Award in 2013, Qualcomm Innovation Fellowship in 2014, and the Best Student Paper Award at Progress in Electromagnetics Research Symposium (PIERS) in 2015.



When microwave meets ultrasound: a hybrid system in medical imaging


Conventional imaging modalities rely on expensive and bulky hardware that limit usage to hospitals and clinics. A handheld medical imaging system with sufficient contrast and resolution can help on-site diagnosis and accelerate the treatment process. Such a handheld system will benefit numerous medical applications, such as on-site detection of internal injuries and hemorrhages, identification of abnormal tissue, and other life-threatening situations or ambulatory care in which medical personnel need immediate access. The limited size and power of the imager are two major challenges in such applications.

My talk will discuss hybrid imaging techniques that combine RF/microwave with ultrasound to provide high-resolution imaging of dielectric contrasts. Additionally, a new non-contact modality that can be used to detect imbedded target in highly dispersive media will be presented. I will also discuss some recent works on beam forming, machine learning and forward reconstruction for this hybrid imaging system.