Convolutional neural networks for automatic image quality control and EARL compliance of PET images

Elisabeth Pfaehler*, Daniela Euba, Andreas Rinscheid, Otto S. Hoekstra, Josee Zijlstra, Joyce van Sluis, Adrienne H. Brouwers, Constantin Lapa, Ronald Boellaard

*Corresponding author for this work

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Abstract

Background Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. Materials and methods 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using fivefold cross-validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations. Results In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. Conclusion The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by, e.g., adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential.

Original languageEnglish
Article number53
Number of pages13
JournalEJNMMI physics
Volume9
Issue number1
Early online date9-Aug-2022
DOIs
Publication statusPublished - 2022

Keywords

  • PET
  • Image quality
  • Convolutional neural network
  • CELL LUNG-CANCER
  • F-18-FDG PET
  • FDG-PET/CT
  • RADIOTHERAPY
  • DEFINITION
  • SURVIVAL

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