Purpose: To investigate the performance of the new long axial field-of-view (LAFOV) Biograph Vision Quadra PET/CT and a standard axial field-of-view (SAFOV) Biograph Vision 600 PET/CT (both: Siemens Healthineers) system using an intra-patient comparison. Methods: Forty-four patients undergoing routine oncological PET/CT were prospectively included and underwent a same-day dual-scanning protocol following a single administration of either ¹⁸F-FDG (n = 20), ¹⁸F-PSMA-1007 (n = 16) or ⁶⁸Ga-DOTA-TOC (n = 8). Half the patients first received a clinically routine examination on the SAFOV (FOV_axial 26.3 cm) in continuous bed motion and then immediately afterwards on the LAFOV system (10-min acquisition in list mode, FOV_axial 106 cm); the second half underwent scanning in the reverse order. Comparisons between the LAFOV at different emulated scan times (by rebinning list mode data) and the SAFOV were made for target lesion integral activity, signal to noise (SNR), target lesion to background ratio (TBR) and visual image quality. Results: Equivalent target lesion integral activity to the SAFOV acquisitions (16-min duration for a 106 cm FOV) were obtained on the LAFOV in 1.63 ± 0.19 min (mean ± standard error). Equivalent SNR was obtained by 1.82 ± 1.00 min LAFOV acquisitions. No statistically significant differences (p > 0.05) in TBR were observed even for 0.5 min LAFOV examinations. Subjective image quality rated by two physicians confirmed the 10 min LAFOV to be of the highest quality, with equivalence between the LAFOV and the SAFOV at 1.8 ± 0.85 min. By analogy, if the LAFOV scans were maintained at 10 min, proportional reductions in applied radiopharmaceutical could obtain equivalent lesion integral activity for activities under 40 MBq and equivalent doses for the PET component of <1 mSv. Conclusion: Improved image quality, lesion quantification and SNR resulting from higher sensitivity were demonstrated for an LAFOV system in a head-to-head comparison under clinical conditions. The LAFOV system could deliver images of comparable quality and lesion quantification in under 2 min, compared to routine SAFOV acquisition (16 min for equivalent FOV coverage). Alternatively, the LAFOV system could allow for low-dose examination protocols. Shorter LAFOV acquisitions (0.5 min), while of lower visual quality and SNR, were of adequate quality with respect to target lesion identification, suggesting that ultra-fast or low-dose acquisitions can be acceptable in selected settings.

FDG-PET shows that microglial activation might drive cerebral glucose uptake in mice and patients with AD.

Amyloid imaging by small animal PET (µPET) in models of Alzheimer’s disease (AD) offers the possibility to track amyloidogenesis and brain energy metabolism. Since microglial activation is thought to contribute to AD pathology, we undertook a triple tracer µPET study to assess microglia activation and glucose metabolism in association with amyloid plaque load in a transgenic AD mouse model. Methods: Groups of PS2APP and C57Bl/6 wild-type (WT) mice of various ages were examined by µPET. We acquired 90 min dynamic emission data with ¹⁸F-GE180 for imaging activated microglia (18kD translocator protein ligand, TSPO), and static 30-60 min recordings with ¹⁸F-FDG for energy metabolism and ¹⁸F-florbetaben for amyloidosis. Optimal fusion of PET data was obtained through automatic non-linear spatial normalization, and standardized-uptake-value-ratios (SUVR) were calculated. For the novel TSPO tracer ¹⁸F-GE180, we then calculated distribution volume ratios (DVR) after establishing a suitable reference region. Immunohistochemical analyses with TSPO antisera, methoxy-X04 staining for fibrillary β-amyloid, and ex vivo autoradiography served as terminal gold standard assessments. Results: SUVR at 60-90 min p.i. gave robust quantitation of ¹⁸F-GE180, which correlated well with DVR calculated from the entire recording, and using a white matter reference region. Relative to age-matched WT, ¹⁸F-GE180 SUVR was slightly elevated in PS2APP mice at five months (+9%; p<0.01), and distinctly increased at 16 months (+25%; p<0.001). Over this age range, there was high positive correlation between µPET findings of microglial activation with amyloid load (R=0.85; p<0.001), and likewise with metabolism (R=0.61; p<0.005). Immunohistochemical and autoradiographic findings confirmed the in vivo µPET data. Conclusion: In this first triple tracer µPET in a well-established AD mouse model, we find evidence for age-dependent microglia activation. This activation, correlating positively with the amyloid-load, implies a relationship between amyloidosis and inflammation in the PS2APP AD mouse model.

Recently introduced long-axial field-of-view (LAFOV) PET/CT systems represent one of the most significant advancements in nuclear medicine since the advent of multi-modality PET/CT imaging. The higher sensitivity exhibited by such systems allow for reductions in applied activity and short duration scans. However, we consider this to be just one small part of the story: Instead, the ability to image the body in its entirety in a single FOV affords insights which standard FOV systems cannot provide. For example, we now have the ability to capture a wider dynamic range of a tracer by imaging it over multiple half-lives without detrimental image noise, to leverage lower radiopharmaceutical doses by using dual-tracer techniques and with improved quantification. The potential for quantitative dynamic whole-body imaging using abbreviated protocols potentially makes these techniques viable for routine clinical use, transforming PET-reporting from a subjective analysis of semi-quantitative maps of radiopharmaceutical uptake at a single time-point to an accurate and quantitative, non-invasive tool to determine human function and physiology and to explore organ interactions and to perform whole-body systems analysis. This article will share the insights obtained from 2 years’ of clinical operation of the first Biograph Vision Quadra (Siemens Healthineers) LAFOV system. It will also survey the current state-of-the-art in PET technology. Several technologies are poised to furnish systems with even greater sensitivity and resolution than current systems, potentially with orders of magnitude higher sensitivity. Current barriers which remain to be surmounted, such as data pipelines, patient throughput and the hindrances to implementing kinetic analysis for routine patient care will also be discussed.

Purpose: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer’s disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model’s performance to that of multiple expert nuclear medicine physicians’ readers. Materials and methods: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer’s disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model’s performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6–100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7–100) in AD, 71.4% (51.6–91.2) in MCI-AD, and 94.7% (90–99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.