Framework for multi-feature registration of medical images

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Framework for multi-feature registration of medical images Millions of dollars of government and industrial support have been used to collect medical image data from the living brains of tens of thousands of subjects with the goal to advance our understanding of the human brain in health and in disease. Recent advances in computational technology and computer science made it possible to analyze such enormous quantities of data. Examples brain analysis can include the study of longitudinal growth trajectories in normal development or in abnormal development; the study of ageing to help understand disease progression; and the identification of imaging biomarkers for diagnostic purposes. Most analyses of brain data relies on a registration step where all brain are aligned into a standard coordinate system, so that an x,y,z coordinate addresses the same anatomical location in all brains. However, analysis of the terabytes of data representing thousands of subjects can be time consuming even when implemented on a supercomputer or cluster. This constraint limits the interactive analysis of such data. New tools are needed to align these datasets so that useful information can be extracted from the terabytes of data collected. The long-term objectives the proposed research program are to build novel image processing techniques, based on probabilistic/mathematical frameworks to improve the registration tools used in brain analysis. In our short term objectives, we propose to develop tools to enable learning the statistical variability of healthy neuroanatomy across large populations of subjects and model longitudinal change in specific sub-groups of the population. These models will be used to enhance and improve the robustness and accuracy of registration, tissue classification and structure segmentation techniques when applied to large databases of brain MRI. These tools will lead to improved neuroscientific research that may in turn lead to concrete improvements in medical diagnosis and patient care in the form of novel image-based biomarkers of disease.