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Related Experiment Video

Updated: Nov 7, 2025

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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Data-driven respiratory phase-matched PET attenuation correction without CT.

Donghwi Hwang1,2, Seung Kwan Kang1,2, Kyeong Yun Kim1,2

  • 1Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.

Physics in Medicine and Biology
|April 28, 2021
PubMed
Summary
This summary is machine-generated.

A new deep learning method provides respiratory phase-matched attenuation correction for PET scans without CT. This improves PET image quality, reduces motion artifacts, and enhances tumor size and uptake value accuracy.

Keywords:
attenuation correctiondata-driven gatingmotion correctionsimultaneous reconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Nuclear Medicine

Background:

  • Positron Emission Tomography (PET) imaging is crucial for oncology, but respiratory motion degrades image quality and affects quantitative accuracy.
  • Traditional attenuation correction (AC) often relies on CT, which can introduce artifacts and may not perfectly match PET respiratory phases.
  • Developing motion-compensated, CT-free AC methods is essential for improving PET diagnostic performance.

Purpose of the Study:

  • To develop and evaluate a novel deep learning-based, data-driven respiratory phase-matched attenuation correction (AC) method for PET that eliminates the need for gated-CT.
  • To assess the impact of this CT-free AC method on tumor size, Standard Uptake Value (SUV) measurements, and overall PET image quality (%STD).

Main Methods:

  • A multi-step approach combining data-driven respiratory gating, Maximum-Likelihood Reconstruction of Attenuation and Activity (MLAA) for gated attenuation map estimation, and Convolutional Neural Network (CNN) enhancement.
  • CNN training utilized 80 oncologic whole-body 18F-fluorodeoxyglucose (18F-FDG) PET/CT datasets for 3D patch-based learning.
  • Application and evaluation on seven regional PET/CT scans, including non-rigid registration for motion-free PET image generation.

Main Results:

  • The proposed method generated attenuation-corrected gated and motion-free PET images with sharper organ boundaries and improved noise characteristics compared to conventional methods.
  • Elimination of the 'banana artifact' commonly seen with phase-mismatched CT-based AC.
  • Significant improvements observed in tumors with >5 mm motion: 12.3% reduction in tumor size and 13.3% increase in SUV90%.
  • %STD in liver uptake was reduced by 11.1%.

Conclusions:

  • The deep learning-based, data-driven respiratory phase-matched AC method effectively improves PET image quality and reduces motion artifacts without requiring CT.
  • This CT-free approach enhances quantitative accuracy for tumor assessment, particularly in the presence of significant respiratory motion.
  • The method holds promise for advancing quantitative PET imaging in clinical oncology.