By fusing information from multiple imaging modalities, multimodal imaging can provide substantially greater biological information content and detection power than the individual modalities employed in isolation. Despite the promise of the multimodal imaging framework, integration of multiple imaging modalities is often hindered in practice by the need to integrate advanced software applications which were not designed for interoperability. For example, integration of separate analysis environments requires reconciling different coordinate systems, harmonizing quality assurance goals, and integrating developer knowledge. While the unique challenges of software engineering for multimodal imaging are recognized in the field, no specific software methodology has been developed yet to address these challenges. In this grant, we propose to develop a set of software engineering methods for multimodality brain imaging. The project consists of two software engineering aims, and a driving biological aim: (1) integrate and modernize an anatomical analysis package (Free Surfer) and a diffusion tensor analysis package (Free Diffusion). (2) develop software methods for automatic failure detection in multimodal imaging; and (3) apply multimodal framework to integrate high resolution structural MRI and high angular resolution diffusion MRI. The multimodal structural-diffusion MRI framework will be employed to investigate the relationship between gray matter cortical thickness and the white matter microstructural integrity in the aging brain. This approach will allow us to investigate the correlation between the cortical structure and long-range connectional architecture of the aging brain, a key question in the physiology of aging. Development of a robust software engineering framework for integrating multiple image analysis environments promises to significantly accelerate the development of multimodal brain imaging methods and allow for richer exploitation of the multimodal framework. ? ? ?
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