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Unbiased longitudinal neuromorphometry for clinical decision support

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 4R42AG062026-02
Agency Tracking Number: R42AG062026
Amount: $1,467,494.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: NIA
Solicitation Number: PAS18-188
Timeline
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-08-15
Award End Date (Contract End Date): 2022-05-31
Small Business Information
100 CAPTAINS ROW, APT. 108
Chelsea, MA 02150-4021
United States
DUNS: 078509164
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 NICHOLAS SCHMANSKY
 (617) 959-1896
 nicks@corticometrics.com
Business Contact
 NICHOLAS SCHMANSKY
Phone: (617) 959-1896
Email: nicks@corticometrics.com
Research Institution
 MASSACHUSETTS GENERAL HOSPITAL
 
55 FRUIT STREET
BOSTON, MA 02114-2621
United States

 Domestic Nonprofit Research Organization
Abstract

Project Summary
Normal human neuroanatomy is incredibly variable, and increases with age. This impedes the
ability of neuroimaging to detect effects in neurological conditions such as Alzheimerandapos;s disease
(AD), Huntingtonandapos;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).Project Narrative
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 Alzheimerandapos;s disease (AD), Huntingtonandapos;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.

* Information listed above is at the time of submission. *

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