Pai-Chi Li received the Ph.D. degree in Electrical Engineering: Systems, from the University of Michigan, Ann Arbor in 1994. He joined Acuson Corporation, Mountain View, CA, as a member of the Technical Staff in June 1994. In August 1997, he went back to the Department of Electrical Engineering at National Taiwan University, where he is currently Associate Dean of College of Electrical Engineering and Computer Science, Distinguished Professor of Department of Electrical Engineering and Institute of Biomedical Electronics and Bioinformatics. He served as Founding Director of Institute of Biomedical Electronics and Bioinformatics in 2006-2009 and National Taiwan University YongLin Biomedical Engineering Center in 2009-2011. He is also TBF Chair in Biotechnology. Dr. Li is IEEE Fellow, IAMBE Fellow, AIUM Fellow and SPIE Fellow. He was also Editor-in-Chief of Journal of Medical and Biological Engineering, Associate Editor of Ultrasound in Medicine and Biology, Associate Editor of IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, and on the Editorial Board of Ultrasonic Imaging and Photoacoustics.
With the knowledge of cell interaction between tumor and therapeutic cells, it can help to develop cell-based therapeutic strategies such as immunotherapy. The ability to track cells in vivo or in vitro requires development of molecular probes and 3D molecular imaging systems. Specifically, 3D in vitro models allow the use of various imaging tools in cell tracking. To this end, optical resolution photoacoustic microscopy (OR-PAM) has the potential as a molecular imaging tool for cell migration analysis in 3D cultures. OR-PAM provides superior spatial resolution of micrometer level at 1-mm depth. With the aid of exogenous molecular contrast agents, OR-PAM systems can further provide cellular information. In this study, we verified the feasibility of gold nanorods (AuNRs) contrast-enhanced OR-PAM technique for depicting an in vitro 3D tumor microenvironment. The technique can provide better morphology in 3D microenvironment and is highly potential for preclinical development. The AuNRs contrast-enhanced OR-PAM is highly potential for preclinical screening requiring cell tracking. We will present our system setup, as well as experimental results on the relations between matrix stiffness and cancer cell behaviors. The implication in cancer mechanobiology will also be discussed.
Plenary talk 2(January 19, 16:10 – 16:40)
Recent Progress of Multidisciplinary Computational Anatomy
Distinguished Professor / Department of Advanced Medical Initiatives, Kyushu University, Japan
Dr. Makoto Hashizume graduated after Kyushu University School of Medicine in 1979 and finished residency at General Surgery II, Kyushu University Hospital. He obtained PhD in 1984 from Graduate School of Medical Sciences, Kyushu University, in the area of pathology for portal hypertension. He promoted to Professor and Chairman, Department of Disaster and Emergency Medicine, Faculty of Medical Sciences, Kyushu University in 1999. He is currently the director of Centre for Advanced Medical Innovation, Kyushu University, the director of Department of Integration of Advanced Medicine and Innovative Technology, Kyushu University Hospital (CAMIT), and Distinguished Professor and Chairman, Department of Advanced Medical Initiatives, Faculty of Medical Sciences, Kyushu University. He is at work on development of minimally invasive surgical robotic system with surgical navigation system. He received an official commendation for innovative technology from the Minister of Education, Culture, Sports, Science and Technology in 2006. He also won “the special prize of this year’s robot 2007” for MR compatible surgical robotic system.
The project, “Multidisciplinary Computational Anatomy and its Application to Highly Intelligent Diagnosis and Therapy (multidisciplinary computational anatomy in short)” was funded by MEXT Grant-in-Aid for Scientific Research on Innovative Areas in 2014. Multidisciplinary computational anatomy comprises scientific research on innovative areas based on medical images integrated with those factors of information such as (1) the spatial axis, from a cell size to an organ size level, (2) the time series axis, from an embryo to post mortem body, (3) the functional axis, such as medical image modality, physiology or metabolism, and (4) the pathological axis, from a healthy physical condition to a diseased condition. It is a new scientific area that establishes a mathematical analysis base for a comprehensive and useful understanding of the human body, and defines a new mathematical method for early detection and a highly intelligent diagnosis and treatment for the diseases with a difficulty in them. A new grant for the international activity has been accepted by MEXT last year and it has started to accelerate the global initiatives in collaboration with foreign leading centers in the new frontier over the world.
Plenary talk 3(January 20, 13:10 – 13:40)
Compressed Sensing and Deep Learning for Medical Imaging
Jong Chul Ye
KAIST Endowed Chair Professor / Department of Bio/Brain Engineering, Korea Advanced Inst. Of Science and Technology (KAIST), Korea
Jong Chul Ye received the B.Sc. and M.Sc. degrees from Seoul National University, Seoul, Korea, and the Ph.D. degree from Purdue University, West Lafayette, IN, USA. He joined KAIST—Korea Advanced Institute of Science and Technology, Daejeon, Korea, in 2004, where he is currently KAIST endowed Chair Professor and the Professor of the Department of Bio/Brain Engineering. Before joining KAIST, he worked at Philips Research, and GE Global Research, both in New York. His current research interests include compressed sensing, machine learning and statistical signal processing for various imaging modalities such as MRI, fNIRS, CT, PET, optics, etc. He has served as an Associate Editor of IEEE Trans. On Image Processing, and IEEE Trans. On Computational Imaging, and an Editorial Board Member for Magnetic Resonance in Medicine. His group was the first place winner of the 2009 Recon Challenge at the International Society for Magnetic Resonance in Medicine (ISMRMR) Workshop using k-t FOCUSS algorithm, and one of the winners at 2016 Low Dose CT Grand Challenge organized by the American Association of Physicists in Medicine (AAPM) using WaveNet deep learning algorithm. In 2011, he received Beckman Senior Fellowship award from Univ. of Illinois at Urbana-Champaign. He was the advisor of Student’s Best Paper Awards at 2013 and 2016 IEEE Symposium on Biomedical Imaging.
Compressed sensing (CS) has become one of the most important topics in modern medical imaging. CS overcomes classical spatio- and/or temporal- resolution limits in many medical imaging systems, as well as gives an opportunity to design new types of systems. Moreover, the sparse recovery principle in CS is closely related to the sparse coding principle in recent deep learning. In this talk, I will review our 10 year research activities on compressed sensing and deep learning approaches for medical imaging such as MR, CT, etc. Specifically, I will focus on our pioneering works in this field - k-t FOCUSS, ALOHA, WaveNet and its variations that can overcome the many of the limitation of the existing methods.