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Scientific article
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Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance

Published inClinical nuclear medicine, vol. 48, no. 12, p. 1035-1046
Publication date2023-12
First online date2023-10-26
Abstract

Purpose: Medical imaging artifacts compromise image quality and quantitative analysis and might confound interpretation and misguide clinical decision-making. The present work envisions and demonstrates a new paradigm PET image Quality Assurance NETwork (PET-QA-NET) in which various image artifacts are detected and disentangled from images without prior knowledge of a standard of reference or ground truth for routine PET image quality assurance.

Methods: The network was trained and evaluated using training/validation/testing data sets consisting of 669/100/100 artifact-free oncological 18F-FDG PET/CT images and subsequently fine-tuned and evaluated on 384 (20% for fine-tuning) scans from 8 different PET centers. The developed DL model was quantitatively assessed using various image quality metrics calculated for 22 volumes of interest defined on each scan. In addition, 200 additional 18F-FDG PET/CT scans (this time with artifacts), generated using both CT-based attenuation and scatter correction (routine PET) and PET-QA-NET, were blindly evaluated by 2 nuclear medicine physicians for the presence of artifacts, diagnostic confidence, image quality, and the number of lesions detected in different body regions.

Results: Across the volumes of interest of 100 patients, SUV MAE values of 0.13 ± 0.04, 0.24 ± 0.1, and 0.21 ± 0.06 were reached for SUVmean, SUVmax, and SUVpeak, respectively (no statistically significant difference). Qualitative assessment showed a general trend of improved image quality and diagnostic confidence and reduced image artifacts for PET-QA-NET compared with routine CT-based attenuation and scatter correction.

Conclusion: We developed a highly effective and reliable quality assurance tool that can be embedded routinely to detect and correct for 18F-FDG PET image artifacts in clinical setting with notably improved PET image quality and quantitative capabilities.

eng
Keywords
  • Artifacts
  • Artificial Intelligence
  • Fluorodeoxyglucose F18
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Positron Emission Tomography Computed Tomography / methods
  • Positron-Emission Tomography / methods
Citation (ISO format)
SHIRI LORD, Isaac et al. Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance. In: Clinical nuclear medicine, 2023, vol. 48, n° 12, p. 1035–1046. doi: 10.1097/RLU.0000000000004912
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Identifiers
ISSN of the journal0363-9762
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Technical informations

Creation10/26/2023 7:16:11 PM
First validation01/12/2024 5:34:58 PM
Update time01/12/2024 5:34:58 PM
Status update01/12/2024 5:34:58 PM
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