Geometrical and physical models for the recovery of quantitative information from medical image analysis

Summary form only given. 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. Several image analysis strategies are presented 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. Included are approaches for image segmentation, object motion tracking, shape/volume measurement, and deformation analysis. These ideas are presented in the context of three problem/application areas within the general field of organ/tissue-level medical image analysis: (i) the characterization of cardiac function from noninvasive 4D image data, (ii) the analysis of neuro-anatomical 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.