# Data-Driven Simultaneous Respiratory and Body Motion Correction for PET/CT

> **NIH NIH R03** · YALE UNIVERSITY · 2020 · $83,750

## Abstract

Project Summary
Positron Emission Tomography (PET) can be used to assess physiological or pathological processes via the
use of specific tracers. Acquisition time of a clinical or research PET scan can vary from minutes to hours,
depending on the application. During the acquisition, patients breathe and may undergo voluntary body motion.
Both respiratory motion and body motion may cause artifacts or tracer quantification error in the reconstructed
images. Many motion correction methods have been proposed in the past, for either respiratory motion or body
motion. However, there has been no unified approach that can simultaneously correct for both motions. Lack of
full correction for both motions can result in inadequate correction results. In addition, to detect motions,
external devices are commonly used in research studies. Device-based methods typically require attachments
to the patient, which is not clinically accepted. Instead, data-driven methods, based on the PET raw count data
to detect motions, are clinically attractive. However, for data-driven methods, the presence of body motion can
impair the detection of respiratory motion (cross-talk effect), which can result in sub-optimal correction
performance. In this study, we propose to develop algorithms to correct both body and respiratory motions,
simultaneously for PET/CT. Specifically, we will devise a data-driven method which handles the motion cross-
talk effect, to accurately detect both body and respiratory motions. Finally, our approach will be applicable to
both single-bed and whole-body PET. The proposed algorithms will be first validated, evaluated and optimized
using 4D PET simulations with multiple tracers and simulated respiratory and body motions, based on human
measurements. In vivo validation will be performed using previously acquired non-human primate data. CT
scans before- and after- motion positions will be used as the gold standard for comparison. Existing datasets of
human dynamic PET studies with several tracers will be used to test the proposed algorithms. Both semi-
quantitative metrics and absolute quantitative metrics will be used to test the efficacy of the proposed
algorithms. Successful development of these algorithms will lead to retrospective and prospective evaluation in
larger trials in the future. With GPU acceleration, the motion estimation process of proposal is very
computationally efficient. In addition, with the GPU acceleration development of the list-mode reconstruction in
the future, the computation time of our proposed method will be fully clinically acceptable.

## Key facts

- **NIH application ID:** 9989847
- **Project number:** 5R03EB027209-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Yihuan Lu
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $83,750
- **Award type:** 5
- **Project period:** 2019-08-15 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989847

## Citation

> US National Institutes of Health, RePORTER application 9989847, Data-Driven Simultaneous Respiratory and Body Motion Correction for PET/CT (5R03EB027209-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9989847. Licensed CC0.

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