These talks took place on 19 June at 14:00 in the Cartesium building (Rotunde) at the University of Bremen.
Translational Research in the Advanced Multimodality Image-Guided Operating Suite
Tina Kapur, Harvard Medical School and Brigham and Women’s Hospital
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. NCIGT and AMIGO are funded under the Biomedical Technology Resource Centers program of the National Institute of Biomedical Imaging and Bioengineering. A unique 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, Dr. Kapur will present an introduction to the AMIGO suite and highlight some of the technologies that enable clinical procedures in AMIGO.Biography:
Tina Kapur is the Executive Director of the Image Guided Therapy Program in the Department of Radiology at Brigham and Women’s Hospital. Dr. Kapur is the Dissemination Core PI for two national center grants, the National Alliance for Medical Image Computing (NA-MIC) and the National Center for Image Guided Therapy (NCIGT). 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 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.
Uncertainty in Non-Rigid Registration: Methodology and Preliminary Applications
William M Wells, Harvard Medical School and Brigham and Women’s Hospital
Most deformable registration systems produce, as their “answer” to the registration problem, a “best” estimate of the transformation that relates the image data being registered.
Dr. Wells’ team believes that, as deformable registration is increasingly applied to interventional applications, it is becoming important to characterize the level of uncertainty in the results.
In this talk, Dr. Wells will summarize a thread of research at the Brigham and Women’s Hospital Surgical Plannling Lab that is aimed at the estimation of posterior distributions on the resulting transformations. Dr. Wells will describe the formalism, which uses a Gaussian-like prior on mechanical configurations that depends on elastic deformation energy and the estimation approaches: MCMC and a faster approximative approach.
In addition to the methodology, Dr. Wells will also show preliminary application results for visualizing geometric uncertainty in image-guided neurosurgery, and estimating the uncertainty in delivered dose in prostate and neck radiation therap. The work is a collaborative project with Petter Rishom and Firdaus Janoos.
William Wells is Professor of Radiology at Harvard Medical School and Brigham and Women’s Hospital (BWH), a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and a member of the affiliated faculty of the Harvard-MIT division of Health Sciences and Technology (HST). He received a Ph.D. in computer vision from MIT in 1992 under the supervision of Professor Grimson, and since that time has pursued research in medical image understanding at the BWH Surgical Planning Laboratory, much of it in collaboration with MIT graduate students. Prof. Wells periodically teaches the medical image processing component of HST-582, Biomedical Signal and Image Processing. He is widely known for his ground-breaking and heavily cited work on segmentation of MRI. He is also widely known for his work on multi-modality registration by maximization of Mutual Information for which he and Paul Viola recently received the IEEE ICCV Helmholtz “test of time” award.