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与标准采集相比,基于深度学习的F-18-FDG PET图像去噪算法在低检测计数情况下的临床和体模验证。

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.

作者信息

Bonardel Gerald, Dupont Axel, Decazes Pierre, Queneau Mathieu, Modzelewski Romain, Coulot Jeremy, Le Calvez Nicolas, Hapdey Sébastien

机构信息

Nuclear Medicine, Centre Cardiologique du Nord, Saint-Denis, France.

Nuclear Medicine, Hopital Delafontaine, Saint-Denis, France.

出版信息

EJNMMI Phys. 2022 May 11;9(1):36. doi: 10.1186/s40658-022-00465-z.

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 SUV 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 SUV 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.

摘要

背景

PET/CT图像质量直接受F-18-FDG注射活度影响。注射活度越高,重建图像中的噪声越少,但工作人员受到的放射性暴露越多。一种新的获得美国食品药品监督管理局(FDA)批准的软件已被引入,用于获取临床PET图像,该图像是按照美国的做法在计数统计的25%时采集的。我们的目的是根据放射性示踪剂注射活度的欧洲指南,确定一种基于深度学习的去噪算法(SubtlePET)应用于来自3种不同的新一代PET扫描仪的统计减少的PET原始数据时的局限性,与在体模和患者中的常规采集进行比较。在3种不同的PET设备上采集了国际电工委员会(IEC)体模的低对比度和高对比度(SBR = 2和5)球体以及Jaszczak体模的高对比度(SBR = 5)微球体的图像。纳入了110例患有不同疾病的患者。数据以列表模式采集,并使用常规采集计数统计(PET100)、计数减少50%(PET50)和计数减少66%(PET33)进行回顾性重建。这些计数减少的图像用SubtlePET进行后处理,以获得PET50 + SP和PET33 + SP图像。由2名资深核医学医师对患者图像质量进行评分。计算峰值信噪比和结构相似性指标,以将低计数图像与常规采集(PET100)进行比较。

结果

SubtlePET可靠地对图像进行了去噪,并在PET50 + SP中保持了SUV值(标准化摄取值)。与PET100相比,SubtlePET增强图像(PET33 + SP)的噪声略有增加,并且在病变可检测性方面可能导致信息潜在丢失。关于患者数据集,PET100和PET50 + SP在质量上具有可比性。SubtlePET算法能够正确恢复病变的SUV值,并保持与全时图像相当的噪声水平。

结论

基于我们的结果,根据欧洲推荐的3 MBq/kg注射剂量,SubtlePET适用于临床实践中的半时或半剂量采集,且不会损失诊断置信度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfe9/9095795/6729fb002581/40658_2022_465_Fig1_HTML.jpg

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