# Ultra-precision clinical imaging and detection of Alzheimers Disease using deep learning

> **NIH NIH K99** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $133,920

## Abstract

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, gradient nonlinearities
and B0 distortions, scanner manufacturer, and subject motion in the images across time. We leverage the fact
that, within a subject, there is a physical deformation that relates the brain scans acquired across time unlike the
cross-subject case. Focusing exclusively on longitudinal within-subject studies allows us to craft ultra-sensitive
registration and change detection tools that drastically outperform general purpose ones used in cross-subject
studies, where registration is intended only to find approximate anatomical correspondences. Our longitudinal
imaging framework is thus able to learn to disentangle true neuroanatomical change from irrelevant distortions.
 Since the applicant has a computational background, the proposed training program at Harvard, MIT and
MGH will focus on neuroscience and neurology during the K99 phase to develop the skills needed to transition
to independence in the R00 phase. The applicant aims to become an expert in clinical imaging of AD and push
the limits of what is currently possible in AD research, fundamentally enh...

## Key facts

- **NIH application ID:** 10839950
- **Project number:** 5K99AG081493-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Sean I Young
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $133,920
- **Award type:** 5
- **Project period:** 2023-05-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10839950

## Citation

> US National Institutes of Health, RePORTER application 10839950, Ultra-precision clinical imaging and detection of Alzheimers Disease using deep learning (5K99AG081493-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10839950. Licensed CC0.

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