ULTRA-PRECISION CLINICAL IMAGING AND DETECTION OF ALZHEIMERS DISEASE USING DEEP LEARNING - PROJECT SUMMARY…

Assistance award details

Assistance award identifier

ASST_NON_R00AG081493_075

FAIN

R00AG081493

Recipient

THE GENERAL HOSPITAL CORPORATION

Awarding agency

Department of Health and Human Services

Funding agency

Department of Health and Human Services

Assistance listing (CFDA)

93.866

Total obligated

$243,800

Period

May 15, 2023 – May 31, 2028

Fiscal year

2025

Place of performance state

MA

Source file

FY2025_All_Assistance_Full_20261006.zip

Description

ULTRA-PRECISION CLINICAL IMAGING AND DETECTION OF ALZHEIMERS DISEASE USING DEEP LEARNING - PROJECT SUMMARY AND ABSTRACT IN ALZHEIMER’S DISEASE (AD) STUDIES, LONGITUDINAL WITHIN-SUBJECT IMAGING AND ANALYSIS OF THE HUMAN BRAIN GIVES US VALUABLE INSIGHT INTO THE TEMPORAL DYNAMICS OF THE EARLY DISEASE PROCESS IN INDIVIDUAL SUBJECTS AND ALLOWS TO ASSESS THERAPEUTIC EFFICACY. HOWEVER, LONGITUDINAL IMAGING TOOLS HAVE NOT YET BEEN OPTIMIZED FOR CLINICAL STUDIES OR FOR USE ON NONHARMONIZED SCANS. CHALLENGES INCLUDE REDUCTION OF NOISE ACROSS SERIAL MAGNETIC RESONANCE IMAGING (MRI) SCANS WHILE WEIGHTING EACH TIME POINT EQUALLY TO AVOID BIASES; AND APPROPRIATELY ACCOUNTING FOR ATROPHY ALL IN THE PRESENCE OF VARYING IMAGE INTENSITY, CONTRASTS, MR DISTORTIONS AND SUBJECT MOTION ACROSS TIME. MANY GENERAL TOOLS EXIST FOR DETECTING LONGITUDINAL CHANGE IN CAREFULLY CURATED RESEARCH DATA (SUCH AS ADNI) IN WHICH THE SCAN PROTOCOL HAS BEEN HARMONIZED ACROSS ACQUISITION SITES SO AS TO MINIMIZE DIFFERENTIAL DISTORTION AND GRADIENT NONLINEARITIES REMOVED PRIOR TO DATA RELEASE. UNFORTUNATELY, THESE TOOLS DO NOT WORK ACCURATELY FOR UNHARMONIZED MRI SCANS THAT COMPRISE THE BULK OF THE RESEARCH DATA AVAILABLE, AND ON CLINICAL DATA, WHERE THE PRACTICAL NEED FOR CLINICIANS TO SCHEDULE A SUBJECT ON DIFFERENT SCANNERS LEADS TO ADDITIONAL DIFFERENCES IN SCANS ACQUIRED ACROSS MULTIPLE SCAN SESSIONS. FOR RETROSPECTIVE ANALYSIS OF PAST SCANS OR CLINICAL USE, IT IS THUS CRITICAL TO DEVELOP IMAGING TOOLS THAT ARE AGNOSTIC TO GLOBAL SCANNER-INDUCED DIFFERENCES IN IMAGES BUT VERY SENSITIVE TO SUBTLE NEUROANATOMICAL CHANGE, SUCH AS ATROPHY IN AD, THAT IS HIGHLY PREDICTIVE OF THE EARLY DISEASE PROCESS. TO ADDRESS THE ABOVE ISSUES, WE PROPOSE TO DESIGN, IMPLEMENT AND VALIDATE A DEEP LEARNING (DL) AD IMAGE ANALYSIS FRAMEWORK FOR DETECTING NEUROANATOMICAL CHANGE IN THE PRESENCE OF LARGE IMAGE DIFFERENCES DUE TO THE ACQUISITION PROCESS ITSELF, INCLUDING THE FIELD STRENGTH, RECEIVE COIL, SEQUENCE PARAMETERS,