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

> **NIH NIH R21** · YALE UNIVERSITY · 2020 · $243,150

## Abstract

Project Summary
Positron-emission tomography (PET) is an imaging modality that allows clinicians and researchers to study the
physiological or pathological processes of the human body, and in particular the brain via the use of specific
tracers. For brain PET imaging, patient head movement during scanning presents a challenge for accurate
PET image reconstruction and subsequent quantitative analysis. Problems due to head motion are
exacerbated by the long duration of the scans, with scan times commonly over one hour. Furthermore, some
PET studies specifically involve subjects that either have trouble staying still due to psychological variations,
e.g. patients with neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease, or
psychological variations, e.g. subjects with anxiety disorders, or are required to participate in tasks that involve
movement, e.g. smoking cigarettes while scanning. In brain scans, the average head motion can vary from 7
mm in clinical scans to triple this amount for longer research scans. Quantitatively, a 5 mm head motion can
produce biases of up to ~35% in regional intensities and ∼15% in volume of distribution estimates, which could
much larger than the difference observed in regional intensities or binding potential that distinguish different
demographic groups being studied. The ability to track and correct head motion, therefore, would be of high
utility in both clinical and research PET studies. In the past, many motion correction methods have been
proposed. However, except for hardware-based approaches, there has been no method that can track frequent
head motion on-the-fly during the PET acquisition. Hardware-based approaches are not readily available for
clinical translation or used by other research facilities due to highly-customized software/hardware setup. To
address this challenge, we propose to develop a data-driven methodology using deep learning to track and
estimate rigid head motion using PET raw data, and incorporate both tracer type and time as conditional
variables into this deep neural network design in order to handle diverse PET tracer types and their dynamic
behavior. Overall, these solutions will provide for a data-driven motion estimation methodology to improve the
quality of PET imaging. Specifically, we will start with the development and testing of our methodology for rigid
head motion estimation using single-tracer PET raw data. Then we will perform evaluation of our multi-tracer
motion estimation methodology applied to real PET data with a diverse range of tracers. Finally, in the
exploratory phase, we will integrate time-of-flight information into deep learning-based motion prediction. The
significance of this proposal is that it will allow for improved quality of PET imaging in real time and potentially
allow for its use in clinical PET systems that do not have special motion tracking hardware. This work will serve
as a first step towards developing data-driven mot...

## Key facts

- **NIH application ID:** 9877261
- **Project number:** 1R21EB028954-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Yihuan Lu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $243,150
- **Award type:** 1
- **Project period:** 2020-04-15 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9877261

## Citation

> US National Institutes of Health, RePORTER application 9877261, Data-driven Head Motion Correction in PET Imaging Using Deep Learning (1R21EB028954-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9877261. Licensed CC0.

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