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).

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Small Business Technology Transfer (STTR) Grants - Phase II (R42)
Project #
1R42AG062026-01A1
Application #
9775879
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Wise, Bradley C
Project Start
2019-07-01
Project End
2020-06-30
Budget Start
2019-07-01
Budget End
2020-06-30
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Corticometrics, LLC
Department
Type
DUNS #
078509164
City
Rockport
State
MA
Country
United States
Zip Code
01966