The Use of Geometrical and Physical Models in Quantitative Medical Image Analysis


                  James S. Duncan, Ph.D.
            Professor of Diagnostic Radiology and Electrical Engineering
               Image Processing and Analysis Group
         Yale University


The development of methods to accurately and reproducibly recover useful
quantitative information from medical images is often hampered by uncertainties
in handling these data related to image acquisition parameters, the variability
of normal human anatomy and physiology, the presence of disease or other
abnormal conditions, and a variety of other factors. In this talk, I will
present several image analysis strategies that have been developed in our
laboratory that make use of models based on geometrical and physical/
biomechanical information to help constrain the range of possible solutions in
the presence of such uncertainty. These include approaches for image
segmentation, object motion tracking, shape/ volume measurement, and
deformation analysis. These ideas will be presented in the context of three
problem/application areas: i.) the characterization of cardiac function from
noninvasive 4D (3 spatial dimensions plus time) image data, ii.) the analysis
of neuroanatomical structure from Magnetic Resonance Images and iii.) the
development of an approach that compensates for brain shift in the image data
during image-guided neurosurgery. The talk will include a description of the
problem areas and visual examples of the image datasets being used, an overview
of the mathematical techniques involved and a presentation of results obtained
when analyzing patient image data using these methods.