Virtual Meets Reality in
Image Guided Intervention
4:00PM Integrating Models and Imaging to Guide Interventions
David Hawkes, PhD, FREng, FInstP, FIPEM, Director of the Centre
for Medical Image Computing (CMIC), University College London , and
co-Founder, IXICO Ltd.,d.hawkes@ucl.ac.uk
Moderator: Ron Kikinis, MD, Director of the Surgical Planning Laboratory of
the Department of Radiology, Brigham and Women's Hospital and Harvard Medical
School; Professor of Radiology, Harvard Medical School; Co-Program Leader,
Image Guided Therapy, CIMIT, kikinis@bwh.harvard.edu
Recent
progress in developing motion, shape and biomechanical models is enhancing
image guided interventions. A current challenge is to provide navigational
support for interventions and image directed therapies in (a) interventions
where detailed patient specific image derived models are not available or (b)
interventions on soft or mobile structures. David Hawkes will
describe recent progress in generating and testing models of respiratory motion
for image directed radiotherapy and focal ablation in the lung, liver and
heart, statistical shape models in orthopaedic surgery and the development of
biomechanical models for image guided local excision in breast surgery.
5:00PM Functional
Hierarchy: Representation and Modeling of Spatial Patterns of Activation in
fMRI
Polina Golland, PhD,
Assistant Professor, EECS Department and Computer Science and Artificial
Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, polina@csail.mit.edu
Moderator: Ferenc Jolesz, MD, Director, National Center for Image guided
Therapy and Director of the Division of MRI, Brigham and Women's Hospital;
Professor of Radiology, Harvard Medical School; Director of the Image Guided
Therapy Program, BWH; Co-Program Leader, Image Guided Therapy, CIMIT,
fjolesz@partners.org
Polina
Golland will present a novel approach to computational modeling of spatial
activation patterns observed through fMRI. The traditional way to define
networks considers correlation with a user-selected “seed” region of
interest. In contrast, the method of Golland's lab simultaneously
identifies interesting seed time courses and associates voxels with the
respective networks. Based on the empirical observation that the detected
patterns of co-activation are inherently hierarchical, we propose a new
representation for spatial patterns of functional organization. Just like the anatomical
hierarchies represent the structure of the brain as a tree of increasingly
simple systems, we believe that the functional description of the brain should
also be of a hierarchical nature. The lab constructs the
functional hierarchy through an iterative decomposition that utilizes
clustering for network subdivision at each step. The experimental results
demonstrate that the functional region hierarchy provides a robust and
anatomically meaningful model for spatial patterns of co-activation in fMRI. In
addition, subject-specific region hierarchies tend to share common tree
structure, further confirming the validity of this representation for modeling
group-wise patterns of co-activation.