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

Abstract

Background 68Ga-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. Material and Methods We retrospectively included 30 patients with biochemical recurrence of prostate cancer who had undergone 68Ga-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 noise, and diagnostic performances of the series (sensitivity, specificity, accuracy). Results VPFX-derived series were classified differently but better than standard reconstructions (p < 0.001) using half the data. Q.Clear series were not classified differently using half the signal. Some series were noisy but had no significant effect on lesion detectability (p > 0.05). The SubtlePET® algorithm significantly decreased lesion SUV values (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 68Ga-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 in case of anteriorities reconstructed with standard algorithm.