Automated 4D Neuromorphometry: Detecting Change in Brain Structure

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DESCRIPTION (provided by applicant): This project will develop software to facilitate co-registration, visualization and rapid quantitative analysis of changes in longitudinally-acquired magnetic resonance images (MRIs) of the human brain. That is, it aims to provide increased automation and enhanced visualization tools for four dimensional (4D) neuromorphometric analysis. Major technical challenges must be overcome if serial change detection and analysis methods are to be effective. To be successful this project will need to implement and validate algorithmic approaches that can compensate for confounding differences between serial image data sets while accurately identifying and quantifying changes in structures of interest. CorTechs has already demonstrated the capability of developing intelligent image analysis methods for computationally deriving quantitative measures of brain anatomy and pathology in single time-point MRI measurements of patients with neurological conditions, methods that we will build on in the current application. In Phase I we will demonstrate feasibility by first implementing techniques to minimize the potential influence of image artifacts and other irrelevant differences on the assessment of anatomical change between serial data sets. We will also develop methods to optimize the spatial coregistration of such longitudinal data, and to facilitate the quantification and visual comprehension of anatomical deformations between serial scans of patients with progressive neurological disease. In addition, the validity and value of such methods will be illustrated through tests of their improvement to the detection of atrophic processes. In Phase II we would seek to further refine and extend these techniques, apply them to a wider variety of clinical conditions, empirically evaluate the improvements in radiology workflow enabled by the technology, and obtain FDA clearance for them as medical device software suitable for routine clinical use. We anticipate marketing the resulting 4D neuromorphometric methods in the form of intelligent image analysis tools that will provide computational support both to researchers and to physicians involved with the clinical review of serial MRI data. There is a growing need in clinical neuroradiology for effective and efficient computational support methods that can improve the detection and measurement of changes in brain structure over time. Potential applications of improved serial change detection and analysis methods include the derivation of atrophy measures in neurodegenerative disorders such as Alzheimer's disease (AD), or in diseases with secondary neuronal or axonal injury such as multiple sclerosis (MS) or uncontrolled epilepsy. Other important applications of 4D neuromorphometry methods may include its use in differential diagnosis, in the monitoring of brain tumor growth, and in the monitoring of recovery from, or tracking atrophy consequent to, traumatic brain injury, stroke, alcoholism, and depression. There is a large potential market for the tools this project will provide.