Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition

Abstract

Background PET/CT image quality is directly influenced by the F-18-FDG injected activity. The higher the injected activity, the less noise in the reconstructed images but the more radioactive staff exposition. A new FDA cleared software has been introduced to obtain clinical PET images, acquired at 25% of the count statistics considering US practices. Our aim is to determine the limits of a deep learning based denoising algorithm (SubtlePET) applied to statistically reduced PET raw data from 3 different last generation PET scanners in comparison to the regular acquisition in phantom and patients, considering the European guidelines for radiotracer injection activities. Images of low and high contrasted (SBR = 2 and 5) spheres of the IEC phantom and high contrast (SBR = 5) of micro-spheres of Jaszczak phantom were acquired on 3 different PET devices. 110 patients with different pathologies were included. The data was acquired in list-mode and retrospectively reconstructed with the regular acquisition count statistic (PET100), 50% reduction in counts (PET50) and 66% reduction in counts (PET33). These count reduced images were post-processed with SubtlePET to obtain PET50 + SP and PET33 + SP images. Patient image quality was scored by 2 senior nuclear physicians. Peak-signal-to-Noise and Structural similarity metrics were computed to compare the low count images to regular acquisition (PET100). Results SubtlePET reliably denoised the images and maintained the SUVmax values in PET50 + SP. SubtlePET enhanced images (PET33 + SP) had slightly increased noise compared to PET100 and could lead to a potential loss of information in terms of lesion detectability. Regarding the patient datasets, the PET100 and PET50 + SP were qualitatively comparable. The SubtlePET algorithm was able to correctly recover the SUVmax values of the lesions and maintain a noise level equivalent to full-time images. Conclusion Based on our results, SubtlePET is adapted in clinical practice for half-time or half-dose acquisitions based on European recommended injected dose of 3 MBq/kg without diagnostic confidence loss.

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“…Moreover, as the SubtlePET® algorithm is a postreconstruction image processing tool, it is independent of machine brand or model and therefore allows flexibility. In addition, two recent French studies (25,26) found similar results on the main objective of image quality with only half the activity injected. One of these studies (25) used the Philips technology.…”

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“…We did not conduct a proper visual inspection of the enhanced image quality as the studies [8]- [10] did but demonstrated our results for the renginologist community, which approved the high quality of our reconstructions.…”

“…The recent studies [2], [9], [12] investigated FT 90 sec The study [10] examined the limits of the SubtlePET denoising algorithm applied to statistically reduced PET raw data from 3 different last-generation PET scanners compared to the regular acquisition in phantom (spheres) and patient. Enhanced images (PET 33% + SubtlePET) had slightly increased noise compared to PET 100% and could potentially lose information regarding lesion detectability.…”

On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies

“…Here, DL methods can be used for denoising to maintain adequate image quality. This concept has shown promising results not only for gamma camera [35], SPECT [36,37], but also for PET [38,39].…”