Impact of Deep-Learning-Based Respiratory Motion Correction on [18F] FDG PET/CT Test–Retest Reliability and Consistency of Tumor Quantification in Patients with Lung Cancer

<b>Objectives:</b> Respiratory motion degrades the quantitative accuracy and test-retest (TRT) reliability of fluorine-18 fluorodeoxyglucose ([<sup>18</sup>F] FDG) positron emission tomography (PET)/computed tomography (CT) in lung cancer. This study investigated whether a deep-learning-based respiratory motion correction (RMC) method improves the TRT reliability and image quality of [<sup>18</sup>F] FDG PET tumor quantification compared with non-motion-corrected (NMC) reconstructions. <b>Methods:</b> Thirty-one patients with primary lung cancer underwent three PET acquisitions: whole body free breathing (Scan1), thoracic free breathing (Scan2), and thoracic controlled breathing (ScanCB). Each dataset was reconstructed with and without RMC. Visual assessments of liver motion artifacts, lesion clarity, and PET-CT co-registration were scored. Lung tumors were segmented to derive standardized uptake value max (SUVmax), SUVmean, metabolic tumor volume (MTV), PET-derived lesion length (PLL), and total lesion glycolysis (TLG). Visual image scores and TRT reliability of tumor quantification were compared using Kruskal-Wallis one-way analysis of variance and intraclass correlation coefficients (ICCs). <b>Results:</b> RMC reconstructions achieved higher visual scores of lesion clarity and PET-CT co-registration across all lung lobes and significantly reduced liver motion artifacts compared with NMC reconstructions. Differences in SUVmax, SUVmean, PLL, MTV, and TLG between Scan2 and ScanCB were significantly smaller with RMC than with NMC. ICCs for SUVmax, SUVmean, MTV, and TLG were higher between scans with RMC than NMC reconstructions, indicating improved TRT reliability. <b>Conclusions:</b> The deep-learning-based RMC method improved the image quality and TRT reproducibility of [<sup>18</sup>F] FDG PET/CT quantification in lung cancer, supporting its potential for routine adoption in therapy-response assessments.

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Journal
Biomedicines
Published
2026-01-21
DOI
https://doi.org/10.3390/biomedicines14010245
Primary Topic
Medical Imaging Techniques and Applications
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article
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article

Impact of Deep-Learning-Based Respiratory Motion Correction on [18F] FDG PET/CT Test–Retest Reliability and Consistency of Tumor Quantification in Patients with Lung Cancer

Yuanyan Cao, Shijia Weng, Limei Jiang, Runze Wu et al.
Biomedicines
Medical Imaging Techniques and Applications
article

Impact of Deep-Learning-Based Respiratory Motion Correction on [18F] FDG PET/CT Test–Retest Reliability and Consistency of Tumor Quantification in Patients with Lung Cancer

Yuanyan Cao, Shijia Weng, Limei Jiang, Runze Wu, Qian Wang, Yuan Li
article en

Abstract

<b>Objectives:</b> Respiratory motion degrades the quantitative accuracy and test-retest (TRT) reliability of fluorine-18 fluorodeoxyglucose ([<sup>18</sup>F] FDG) positron emission tomography (PET)/computed tomography (CT) in lung cancer. This study investigated whether a deep-learning-based respiratory motion correction (RMC) method improves the TRT reliability and image quality of [<sup>18</sup>F] FDG PET tumor quantification compared with non-motion-corrected (NMC) reconstructions. <b>Methods:</b> Thirty-one patients with primary lung cancer underwent three PET acquisitions: whole body free breathing (Scan1), thoracic free breathing (Scan2), and thoracic controlled breathing (ScanCB). Each dataset was reconstructed with and without RMC. Visual assessments of liver motion artifacts, lesion clarity, and PET-CT co-registration were scored. Lung tumors were segmented to derive standardized uptake value max (SUVmax), SUVmean, metabolic tumor volume (MTV), PET-derived lesion length (PLL), and total lesion glycolysis (TLG). Visual image scores and TRT reliability of tumor quantification were compared using Kruskal-Wallis one-way analysis of variance and intraclass correlation coefficients (ICCs). <b>Results:</b> RMC reconstructions achieved higher visual scores of lesion clarity and PET-CT co-registration across all lung lobes and significantly reduced liver motion artifacts compared with NMC reconstructions. Differences in SUVmax, SUVmean, PLL, MTV, and TLG between Scan2 and ScanCB were significantly smaller with RMC than with NMC. ICCs for SUVmax, SUVmean, MTV, and TLG were higher between scans with RMC than NMC reconstructions, indicating improved TRT reliability. <b>Conclusions:</b> The deep-learning-based RMC method improved the image quality and TRT reproducibility of [<sup>18</sup>F] FDG PET/CT quantification in lung cancer, supporting its potential for routine adoption in therapy-response assessments.

BiomedicinesVol. 14(1)
Peking University (CN), Peking University People's Hospital (CN), United Imaging Healthcare (China) (CN)
Good health and well-being
Openalex Percentile: Top 82%
Medical Imaging Techniques and Applications
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