# Personalized Task-Based Respiratory Motion Correction for Low-Dose PET/CT

> **NIH NIH R01** · YALE UNIVERSITY · 2022 · $628,203

## Abstract

Project Abstract
PET plays an important role in cancer management. However, image blurring and mismatched attenuation
correction due to respiratory motion can substantially degrade detection efficacy and quantification accuracy
for tumors located in the lung and abdomen. Existing motion correction methods might provide satisfactory
results for patients with regular breathing patterns, which account for about 60% of patients. However, for the
remaining 40% of patients with irregular breathing patterns, these methods neglect the major effects of
intra-gate motion due to inter-cycle and intra-cycle motion variations. In addition, as dose reduction in
PET imaging has become increasingly important, existing motion correction methods typically amplify image
noise and degrade their performances on low-count data. Another important challenge is the mismatch
between CT and PET that limits phase-matched attenuation correction for every gated PET image using a
single helical CT. Therefore, to achieve accurate quantification for evaluation of response to cancer therapy
and reliable detection of tumors using low-dose PET protocols, particularly for patients with breathing pattern
changes including variable motion amplitude, baseline variation, and amplitude variation, it is critical to
develop personalized motion correction strategies optimized for individual patient's breathing patterns
and the imaging task to eliminate intra-gate motion and mismatched attenuation correction for low-
dose PET. Extending our existing collaboration, Yale and Siemens form an ideal team to optimize a
comprehensive solution to correct for breathing pattern variability with intrinsically phase-matched attenuation
correction for both regular and irregular breathers in the first two Aims. We will then develop and translate a
personalized strategy to automatically identify the most time-effective motion correction approach for each
individual patient, considering task and breathing pattern. We will optimize our personalized motion correction
methods and strategy particularly for low-count PET data, aiming to reduce radiation dose to 25%-50% of the
dose in current PET protocols. The outcome of this research will be a comprehensive motion correction
package including four correction approaches and a personalized strategy that is automatically optimized for
each individual patient. This development will be ready to translate to commercial PET/CT scanners and
clinical end-users. As existing motion correction methods only apply to ~60% regular breathers, but
have substantial limitation for the remaining ~40% irregular breathers, our proposed development can
provide a unified motion correction framework for all patients with both regular and irregular
breathing. This fast translation with industrial partners can lead to a significant and timely clinical
impact for cancer management.

## Key facts

- **NIH application ID:** 10436864
- **Project number:** 5R01CA224140-05
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Chi Liu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $628,203
- **Award type:** 5
- **Project period:** 2018-07-02 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10436864

## Citation

> US National Institutes of Health, RePORTER application 10436864, Personalized Task-Based Respiratory Motion Correction for Low-Dose PET/CT (5R01CA224140-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10436864. Licensed CC0.

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