Normal human neuroanatomy is incredibly variable, and increases with age. This impedes the ability of neuroimaging to detect effects in neurological conditions such as Alzheimer's disease (AD), Huntington's disease (HD), multiple sclerosis (MS) and schizophrenia. Most of the recently available state-of-the-art quantitative imaging tools still use cross-sectional methods to analyze repeated scans. These tools lack the sensitivity to monitor subtle progressive changes because such approaches do not account for the large intrinsic variability of normal neuroanatomy. The goal of this project is to commercialize a longitudinal, neuro-morphometric image processing pipeline for use in radiology, neurology and related clinical fields. The successful completion of this project will result in a clinically useful neuro-morphometric longitudinal analysis stream with more statistical power than is currently available commercially. This increase in power will directly translate into an enhanced ability to detect and assess progression at both the individual and group levels. It will also alleviate a major pain point in current longitudinal neuroradiology reading workflows, reducing radiology report turnaround times (RTAT).
The proposed project will develop software to help clinicians quantitatively assess and interpret changes in brain MRI data in a way that integrates seamlessly into an existing clinical workflow. It will help radiologists detect changes to brain structures earlier and more accurately, in neurological conditions such as Alzheimer's disease (AD), Huntington's disease (HD), multiple sclerosis (MS) and schizophrenia. The resulting efforts will translate into an enhanced ability to detect and assess disease progression, and reduce radiology report turnaround time.
