This study aimed to assess whether the event of SQA and the evolution of digital PET imaging requires the control databases to evolve concomitantly. Diagnostic performances observed at the group and individual level show that the diagnostic accuracy of SQA on digital controls is improved compared to SQA on conventional controls, particularly as it relates to the detection sensitivity of AD. This observation is an argument which supports the development of digital control databases for SQA of brain 18F-FDG PET images for clinical practice.
Digital PET technology is associated with improvements in image quality, specifically better spatial resolution and signal-to-noise ratios compared to conventional PET cameras (2). These distinct image qualities lead to problematic head-to-head comparisons between digital and conventional PET images as reflected in our study by the relatively poor diagnostic performance obtained for SQA using conventional controls for discriminating AD. However, all currently implemented control databases in dedicated software for automated SQA in clinical practice still rely on conventional PET control images (9,13). Of course, it is now possible to implement local databases in the majority of these types of software, but establishing control databases acquired with digital PET technology remains an extensive undertaking, particularly because it involves a relative recently implemented technology.
Our present study shows that implementing a control database acquired with digital PET technology yields an increase in the detection sensitivity of AD patients, not only at the group level (+ 116 cm3 of detected hypometabolism volume, Table 2) but also at the individual level (sensitivity of detection increased from 78 % to 89%, Table 4). A high detection sensitivity is primordial in the diagnosis of AD since 18F-FDG PET is a biomarker of neurodegeneration, which contributes to the ATN classification, the N biomarker being directly associated with cognitive impairment in patients suspected to have neurodegenerative diseases (14).
All results in the current study were initially obtained using a fully automated analysis, which supports the objective nature of our observations in both the group and individual level analyses. This original fully automated methodology, which necessitated an adaptation of the levels of significance to detect anomalies, was exclusively based on the SPM software. From a clinical standpoint and at the individual level, this fully automated analysis was nevertheless consistent with the visual analysis, using a methodology that is very similar to that applied in previously published SQA studies (6,12). By using this visual analysis, the diagnostic performance of SQA for discriminating AD with the digital controls observed in our study (accuracy, sensitivity and specificity of respectively 84, 85 and 82 %) was within the range of previously reported results (70-97.5% for accuracy, 62.3-96 % for sensitivity and 84-99% for specificity) (6,8–12,23–25).
SQA at the individual level was also performed using an intensity normalisation based on the sensorimotor cortex, which has been suggested to improve diagnostic performance of SQA (26). The finds results were comparable to intensity normalisation on cerebellum. SQA on digital controls show respectively accuracy, sensitivity and specificity 84 %, 89 % and 77 % (versus 86 %, 89 % and 82 %). SQA on conventional controls show respectively accuracy, sensitivity and specificity 73 %, 52 % and 100 % (versus 80 % 78 % and 82 %).
The post-filter used (Gaussian kernel of 12 mm FWHM) can be decreased relative to the size of the voxels used particularly for digital PET scans. However, when smoothing images with a Gaussian kernel of 4 mm FWHM, the accuracy to detect AD, respectively vs. digital and conventional controls, remained unchanged (86 % versus 86 % and 82 % versus 80 %).
The main limitation of our study results from the fact that controls included in the conventional and digital control databases were different individuals. From an ethical perspective, it remains problematic to establish control databases acquired in parallel with both the conventional and digital PET systems, even if it would be feasible to scan the same set of HC subjects within a few months on two different scanners using the half-dose permitted by the high sensitivity of the digital PET. It should however be noted that controls included in our conventional and digital databases did not exhibit any differences in age, sex, MMSE and educational level when compared to each other, or when compared to the AD and HC groups. In addition, these two, distinct conventional and digital control databases are representative samples from current daily clinical practice. The main objective of the current study was to assess whether there is indeed a requirement to establish digital control databases when acquisitions are performed with the new digital PET system. A secondary issue that may be addressed is that the sample size of conventional and digital control databases is rather small (n= 19 and 20). This number of controls is nevertheless known to be sufficient to accurately perform group analyses with SPM (27).
Overall, in light of recent digital PET technology developments and considering that SQA is now clearly recommended for brain 18F-FDG PET image analysis, there is an urgent need to establish digital PET control databases for SQA of brain 18F-FDG PET images. This would be particularly helpful for improving the sensitivity required to detect AD patients. Large healthy control databases should be constituted and shared through the multicentre community using standardised imaging protocols.