Spatiotemporal Analysis of Brain Development and Disease Progression

Rapid advances in image acquisition and shape capturing technology provide continuous or time-discrete 3-D volumetric images and/or surfaces, motivated by the notion that dynamic spatiotemporal changes may provide information not available from snapshots in time. Image analysis of 3-D image data taken over time, resulting in 4-D data with embedded time-varying structures, requires a new image processing methods and computational approaches to make use of the inherent correlation and causality of repeated acquisitions. This talk will discuss work in progress towards the development of advanced 4-D image analysis methodologies that carry the notion of linear and nonlinear image registration and regression. We will demonstrate that regression and functional data analysis concepts, well established for univariate or low-dimensional data, are extended for application to complex, high-dimensional time-discrete data such as longitudinal images, image-derived shapes and structures, or combinations thereof.

Our research is driven by challenging medical image analysis problems. Clinical assessment routinely uses terms such as development, growth trajectory, aging, degeneration, disease progress, recovery or prediction. This terminology inherently carries the aspect of dynamic processes, suggesting that snapshots in time and cross-sectional analysis are not sufficient. We will show examples from ongoing clinical studies such as analysis of early brain growth in healthy and at-risk paediatric subjects, modelling of neurodegeneration in normal aging and Huntington’s and Alzheimer’s disease, and assessment of brain changes in trauma patients (traumatic brain injury TBI). Methodologies are generic and may find applications in a wide range of areas where we would like to extract spatiotemporal models from dynamic image series.