Computational Representation of Brain Architectures

Speaker:Tianming Liu, Ph.D.
Affiliation: The University of Georgia
Time: 9:00-10:00, Jun.21 (Tuesday)
Venue: Medical Science Building B321
Host: Prof. Hongen Liao 

Abstract. This talk will discuss the importance, challenges and opportunities of mapping and representing brain architectures via neuroimaging and computing techniques. We will present and discuss the ongoing projects at the Cortical Architecture Imaging and Discovery (CAID) Lab of the University of Georgia.

About the Speaker

Dr . Tianming Liu is a Professor of Computer Science at UGA. Dr. Liu is also an affiliated faculty with the UGA Bioimaging Research Center (BIRC), the UGA Institute of Bioinformatics (IOB), the UGA Biomedical and Health Sciences Institute (BHSI), and the UGA Neuroscience PhD program. Before he moved to UGA, Dr . Liu was a faculty member of Weill Medical College of Cornell University (Assistant Professor, 2007-2008) and Harvard Medical School (Instructor, 2005-2007). Dr . Liu was a postdoc in neuroimaging in the University of Pennsylvania (2002-2004) and Harvard Medical School (2004-2005). Dr . Liu is the recipient of the Microsoft Fellowship Award (2000-2002), the NIH Career Award (2007-2012) and the NSF CAREER Award (2012-2017).

Advanced Multimodality Image-Guided Surgery and Interventional Radiology

Speaker:Tina Kapur
Affiliation: Harvard Medical School and Brigham and Women's Hospital
Time: 10:00-11:30, Nov.17th (Tuesday)
Venue: Medical Science Building C301
Host: Prof. Hongen Liao 

Abstract. The Advanced Multimodal Image-Guided Operating (AMIGO) suite is a clinical translational test-bed for research of the National Center for Image-Guided Therapy (NCIGT) at Brigham and Women's Hospital (BWH) and Harvard Medical School in Boston. NCIGT and AMIGO are funded under the Biomedical Technology Resource Centers program of the National Institute of Biomedical Imaging and Bioengineering. A unique national resource for Image-Guided therapy, AMIGO represents and encourages multidisciplinary cooperation and collaboration among teams of surgeons, interventional radiologists, imaging physicists, computer scientists, biomedical engineers, nurses, and technologists to achieve the common goal of delivering the safest and the most effective state-of-the-art therapy to patients in a technologically advanced and patient-friendly environment.

In this talk, I will present an introduction to the AMIGO suite and highlight some of the technologies that enable surgical and interventional procedures in AMIGO.

About the Speaker

Tina Kapur is the Executive Director of Image Guided Therapy in the Department of Radiology at Brigham and Women's Hospital and Harvard Medical School.
Dr. Kapur’s research interests and accomplishments are in the area of medical image computing and computer aided interventions in image-guided neurosurgery, surgical navigation, and MR-guided pelvic brachytherapy. She has numerous publications in medical image segmentation, and is the holder of several issued US and international patents in the field of surgical navigation. She leads national and international outreach and collaboration efforts for image-guided therapy and is particularly interested in fostering collaborations between efforts in open science to accelerate important discoveries that improve health and save lives.
She received her Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 1999. She was the Chief Scientist at a Boston area surgical navigation company, Visualization Technology Inc., and upon its acquisition by GE Healthcare, the Chief Scientist at the GE Navigation.

Multi-Scale Computational Medicine and Multi-Sense Visualization: Theory and its Impact on Diagnosis, Prognosis and Medical Training

Speaker: Prof. Yusheng Feng (冯雨生), Ph.D.
Professor, Center for Simulation Visualization and Real-Time Prediction (SiViRT)
The University of Texas, San Antonio
Time: 14:00pm-16:00pm, May 13, 2015 (Wednesday)
Venue: Medical Science Building B321
Host: Prof. Hongen Liao 

Abstract. Cancer is the second leading cause of death in the United States and expected to be the leading cause of death in the next few years. With the advancement of computational mathematics, big data science and unprecedented computing power, it becomes possible to investigate the complex tissue growth phenomenon of tumors for cancer prognosis using mathematical modeling and computer simulation. The growth of biological tissue is a complex process because it involves various biophysically- and biochemically-induced events at different spatial and temporal scales. Multi-scale modeling techniques allow us to incorporate important features at various levels to examine the tissue growth mechanism and determine the major factors affecting the growth process. In this talk, I will discuss a multi-scale modeling framework across tissue level, cellular and sub-cellular levels, along with the solution strategies. Then, I will present some preliminary results. Finally, I will discuss future challenges and opportunities in computational medicine as well as how immersive 3D visualization and biomedical big data analytics would likely to reshape medical education, treatment planning and clinical trials.

About the Speaker

Dr. Feng is a Professor of Mechanical and Biomedical Engineering at UT San Antonio, and the Director and co-Founder of NSF-Sponsored Center for Simulation Visualization and Real-Time Prediction (SiViRT), which manage UTSA’s Advanced Visualization Lab (VizLab). He is also a core faculty member of joint Biomedical Graduate Program of UT San Antonio and UT Health Science Center at San Antonio. His research areas involvecomputational cancer research, virtual surgery for surgical training and medical device design.

Prof. Feng received his Ph.D. in Computational Mechanics from the University of Texas at Austin in 1995. He has two Master’s degrees in Mechanical Engineering and Applied Mathematics from University of Oklahoma in 1989 and 1990, respectively. He received his Bachelor’s degree in Solid Mechanics from Tsinghua University in China in 1982.  Dr. Feng is a recipient of highly competitive NIH/K25 career award from 2007 – 2012 for his work on integrative modeling of image-guided cancer treatment simulation. Dr. Feng was awarded the Excellence of Research Award in 2012, and named as the Innovator of the Year in 2013 - an inaugural Innovation Award from UT San Antonio. Before joining University of Texas at San Antonio, he was a faculty of Mathematics Department at Concordia University and Research Scientist at University of Texas at Austin. He also has more than 10 years extensive industry experiences across aerospace, materials, semiconductor, and computer software industries.

Translational Research on Smart Medical Devices

Speaker: Chii-Wann Lin, PhD
Professor, Department of Electrical Engineering, National Taiwan University, Taiwan
Time: 14:00pm-15:30pm, April 23, 2014 (Wednesday)
Venue: Medical Science Building C301
Host: Prof. Hongen Liao 

Abstract: The penetration of MEMS-based technologies has shown great impacts on medical devices for various clinical applications, especially in physical sensors and actuators. Further integration of chemical sensors, CMOS processing circuitries, and wireless can lead to smart bio-systems for efficient and quality healthcare in prevention, diagnosis and treatment. For this area of research - the interface between bench and bedside is the key element to produce a promising new treatment that can be used clinically or brought to market. NTU has its unique strengths in this area for micro-fabrication, integration and medical care infrastructure. We will use our recent activities in Surface Plasmon Resonance (SPR) Point of Care Testing (POCT) device, wearable cardiac monitor and implantable medical electronics to highlight the critical issues of translational researches.

About the Seminar Speaker

Chii-Wann Lin received his B.S. from Department of Electrical Engineering, National Cheng-Kung University in 1984. He then started his career in biomedical engineering with M.S. degree from Graduate Institute of Biomedical Engineering, National Yang-Ming University in 1986. He received his Ph.D. from Case Western Reserve University in 1993.He joined the Center for BiomedicalEngineering, College of Medicine, National Taiwan University from Sept. 1993 till Aug. 1998. He is now a professor in Graduate Institute of Biomedical Engineering and holds joint appointments in both Department of Electrical Engineering and Institute of Applied Mechanics, National Taiwan University. He is also a member of IEEE EMBS, IFMBE, ACST and Chinese BMES. He is the President of Taiwan Association of Chemical Sensors (ACST) from 2008-2010. His research interests include biomedical micro sensors, optical biochip, surface plasmon resonance, bio-plasmonics, nano-medicine, and personal e-health system.

3D Personalized Reconstruction of Shape and Intensity from 2D X-ray Images: Statistical Model-based Solutions

Speaker: Guoyan Zheng, PhD PD
Head of the Information Processing in Medical Interventions
Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland
Time: 10:00am-11:30am, November 13, 2013 (Wednesday)
Venue: Medical Science Building C201
Host: Prof. Hongen Liao
Abstract: The applications of two-dimensional (2D) X-ray imaging in orthopedics are pervasive, both pre-operatively and intra-operatively. However, due to the projective characteristics of 2D X-ray imaging, the accuracy of an X-ray image based application is restricted. One way to address this limitation is to learn a statistical model and to adapt the learned model to the patient’s individual anatomy based on a limited number of calibrated X-ray images. The reconstructed model can then provide detailed 3D information for the considered anatomical structure. In this talk, I will present various solutions that have been developed in my team for reconstructing 3D personalized shape and intensity from 2D X-ray images. I will start with a solution that can reconstruct the shape of an anatomical structure with none or mild degree of pathology even when a statistical model learned from a normal population is used. Challenges and adaptations of applying this method to various pre-operative and intra-operative scenarios will be discussed. Our more recent work focuses on reconstructing not only the shape but also the internal intensity distribution. Applications of our solutions are pre-operative planning, intra-operative surgical interventions, and post-operative treatment evaluation.

Neuroimage-based Diagnosis of Brain Disorders

Time:2:00pm, April 28, (Monday)
Venue: Medical Science Building B323
Speaker:professor Dinggang Shen (University of North Carolina at Chapel Hill)

Abstract: This talk will summarize our work on analysis of MR brain images. Our main goal is to develop automated analysis methods for precise quantification of subtle and complex structural/functional changes in the brains, with applications in early detection of brain disorders, such as Alzheimer's Disease (AD). To achieve this goal, we developed a 3D brain registration method, called HAMMER, for inter-subject registration, and also a 4D (3 spatial dimensions + 1 temporal dimension) brain registration method to obtain more accurate measurements for tiny longitudinal brain changes, compared to the 3D registration methods. For better alignment of a population of images, we have recently developed several groupwise registration methods for joint registration of all images together, thus further improving the registration accuracy among all images. With accurate brain structural/functional information measured by our registration methods, we further developed a multivariate analysis method, based on support vector machine, to jointly consider all structural/functional changes for determining the group difference between normal and abnormal brains, i.e., due to diseases, aging, or brain disorders. We have applied our developed methods to computer-aided diagnosis of schizophrenia and AD. Details of these 3D, 4D, and groupwise registration methods, as well as brain classification methods, will be discussed in this talk.