EJNMMI Research (May 2023)

Imaging quality of an artificial intelligence denoising algorithm: validation in 68Ga PSMA-11 PET for patients with biochemical recurrence of prostate cancer

  • Charles Margail,
  • Charles Merlin,
  • Tommy Billoux,
  • Maxence Wallaert,
  • Hosameldin Otman,
  • Nicolas Sas,
  • Ioana Molnar,
  • Florent Guillemin,
  • Louis Boyer,
  • Laurent Guy,
  • Marion Tempier,
  • Sophie Levesque,
  • Alban Revy,
  • Florent Cachin,
  • Marion Chanchou

DOI
https://doi.org/10.1186/s13550-023-00999-y
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 18

Abstract

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Abstract Background 68 Ga-PSMA PET is the leading prostate cancer imaging technique, but the image quality remains noisy and could be further improved using an artificial intelligence-based denoising algorithm. To address this issue, we analyzed the overall quality of reprocessed images compared to standard reconstructions. We also analyzed the diagnostic performances of the different sequences and the impact of the algorithm on lesion intensity and background measures. Methods We retrospectively included 30 patients with biochemical recurrence of prostate cancer who had undergone 68 Ga-PSMA-11 PET-CT. We simulated images produced using only a quarter, half, three-quarters, or all of the acquired data material reprocessed using the SubtlePET® denoising algorithm. Three physicians with different levels of experience blindly analyzed every sequence and then used a 5-level Likert scale to assess the series. The binary criterion of lesion detectability was compared between series. We also compared lesion SUV, background uptake, and diagnostic performances of the series (sensitivity, specificity, accuracy). Results VPFX-derived series were classified differently but better than standard reconstructions (p 0.05). The SubtlePET® algorithm significantly decreased lesion SUV (p < 0.005) and increased liver background (p < 0.005) and had no substantial effect on the diagnostic performance of each reader. Conclusion We show that the SubtlePET® can be used for 68 Ga-PSMA scans using half the signal with similar image quality to Q.Clear series and superior quality to VPFX series. However, it significantly modifies quantitative measurements and should not be used for comparative examinations if standard algorithm is applied during follow-up.

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