# Data-driven Head Motion Correction in PET Imaging Using Deep Learning

> **NIH NIH R21** · YALE UNIVERSITY · 2021 · $204,499

## Abstract

Project Summary/Abstract
In the parent R21, we are developing deep learning (DL)-based head motion estimation models, based on the
PET raw data, to track head motion during a PET scan in real time without the need for external motion
sensors. In this supplement, we will pursue the development of deep learning neural networks dedicated to
estimating motion for Alzheimer's disease (AD) subjects. Brain PET imaging is highly sensitive to head motion.
Problems due to head motion are exacerbated by the long duration of the scans, with scan times commonly
over one hour, and by the small scale of disease-focused regions of interest, e.g., hippocampus, for AD
subjects. The Yale PET Center recently acquired a set of AD PET data that includes AD patients under
treatment using CT1812, a first-in-class drug that displaces Aβ oligomers bound to neuronal receptors at
synapses. In the CT1812 study, AD patients underwent baseline and post-treatment scans using 11C-UCB-J
and 18F-FDG. The longitudinal nature of this study requires the detection of small-scale changes in small-scale
AD-related brain areas over time within the same individual. Existing Polaris Vicra motion tracking has a 5-10%
failure rate, therefore, there is a compelling need to develop accurate head motion correction for this study. In
this administrative supplement, we will pursue the development of DL neural networks dedicated to estimating
motion for the AD PET dataset acquired under the CT1812 study, and perform rigorous evaluations. In Aim 1,
we will develop a novel DL methodology to perform motion correction, which includes: (1) a DL model to
generate synthetic AD PET images based on rapid back-projection images for every 1-sec frame, and (2) a
second DL model to estimate the rigid motion between two synthetic AD PET images. We will evaluate our
motion estimation models using the data from the twenty subjects acquired in the CT1812 study against
Polaris Vicra motion tracking. In Aim 2, we will perform kinetic modeling analysis for all the CT1812 studies for
both tracers. Dynamic motion corrected reconstruction will be performed using the DL estimated motion
correction (from Aim 1) and be compared to reconstruction using Vicra-based motion correction. We will
correlate the changes in synaptic density (11C-UCB-J), glucose metabolism (18F-FDG) and cognitive function
following CT1812 treatment. We hypothesize that our proposed DL-based approach will outperform the Vicra-
based approach by reducing cross-subject variations within cohorts for any quantitative PET measure in both
11C-UCB-J and 18F-FDG tracers. We also hypothesize the DL-based approach will outperform Vicra by
increasing absolute correlation coefficient value for any correlation between changes in PET measures and
cognitive improvement.

## Key facts

- **NIH application ID:** 10288215
- **Project number:** 3R21EB028954-02S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Yihuan Lu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $204,499
- **Award type:** 3
- **Project period:** 2020-04-15 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10288215

## Citation

> US National Institutes of Health, RePORTER application 10288215, Data-driven Head Motion Correction in PET Imaging Using Deep Learning (3R21EB028954-02S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10288215. Licensed CC0.

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