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The article discusses the perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging. The authors highlight the potential of AI in advancing research and clinical practice in the field of molecular brain imaging. They emphasize the need for standardization of clinical brain image recording and imaging protocols, as well as the importance of annotated images and additional data for feeding AI algorithms. The article also explores the applications of AI in image acquisition, segmentation and registration, interpretability analysis, and specific clinical areas such as epilepsy, neurodegenerative and movement disorders, neuro-oncology, and psychiatric disorders. The authors acknowledge the challenges and limitations in implementing AI in these areas, including the need for large datasets, validation studies, and harmonization of AI algorithms. Overall, the article provides a comprehensive overview of the current state and future potential of AI in molecular brain imaging. [Extracted from the article]

Although the hypometabolic long COVID brain profile described above seems consistent with hypotheses and findings obtained for other viruses before the COVID-19 outbreak, with symptoms reported by patients with long COVID, and with results obtained from other diagnostic approaches assessing long COVID, the significance of these metabolic abnormalities in the individual patients remains to be determined. 2 Axial slices of a brain FDG PET scan of a patient with a hypometabolic long COVID pattern (A) and in a patient with a normal brain scan (B). Here, in 143 patients diagnosed in accordance with the current WHO definition of post-COVID syndrome, a hypometabolic long COVID pattern was observed in 47% of patients presenting persistent symptoms at an average of 10.9 months after COVID-19 [[28]]. [Extracted from the article]

The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [18F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [18F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [18F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations. [ABSTRACT FROM AUTHOR]

Objectives: To test the performance of a 3D convolutional neural network (CNN) in analysing brain [18F]DOPA PET/CT in order to identify patients with nigro-striatal neurodegeneration. We evaluated the robustness of the 3D CNN by testing it against a manual regional analysis of the striata by using a striatal-to-occipital ratio (SOR). Methods: We analyzed patients who had undergone [18F]DOPA PET/CT from 2016 to 2018. Two examiners interpreted PET/CT images as positive or negative. Only patients with at least 2 years of follow-up and an ascertained neurological diagnosis were included. A 3D CNN was developed to evaluate [18F]DOPA PET/CT and refine the diagnosis of movement disorder. This system required training and testing, which were carried out on 2/3 and 1/3 of patients, respectively. A regional analysis was also conducted by drawing region of interest on T1-weighted 3D MRI scans, on which the [18F]DOPA PET images were first co-registered. Results: Ninety-eight patients were enrolled: 43 presented nigro-striatal degeneration and 55 negative cases used as controls. After training on 69 patients, the diagnostic performance of the 3D CNN was then calculated in 29 patients. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 100%, 89%, 100%, 85% and 93%, respectively. When we compared the 3D CNN results with the SOR analysis, we found that the two patients falsely classified as positive by the 3D CNN procedure showed SOR values ≤ 5th percentile of the negative cases' distribution. Conclusions: 3D CNNs are able to interpret [18F]DOPA PET/CT properly, revealing patients affected by Parkinson's disease. Key Points: •[18F]DOPA PET/CT is a sensitive diagnostic tool to identify patients with nigro-striatal neurodegeneration. • A semiquantitative evaluation of the images allows a more confident interpretation of the PET findings. • 3D convolutional neural network allows an accurate interpretation of 18F-DOPA PET/CT images, revealing patients affected by Parkinson's disease. [ABSTRACT FROM AUTHOR]

Purposes: We present the first in-human brain PET imaging data of the new α4β2 nicotinic acetylcholine receptor (nAChR)–targeting radioligand (+)-[18F]Flubatine. Aims were to develop a kinetic modeling-based approach to quantify (+)-[18F]Flubatine and compare the data of healthy controls (HCs) and patients with Alzheimer's disease (AD); to investigate the partial volume effect (PVE) on regional (+)-[18F]Flubatine binding; and whether (+)-[18F]Flubatine binding and cognitive test data respective β-amyloid radiotracer accumulation were correlated. Methods: We examined 11 HCs and 9 mild AD patients. All subjects underwent neuropsychological testing and [11C]PiB PET/MRI examination. (+)-[18F]Flubatine PET data were evaluated using full kinetic modeling and regional as well as voxel-based analyses. Results: With 270-min p.i., the unchanged parent compound amounted to 97 ± 2%. Adequate fits of the time-activity curves were obtained with the 1 tissue compartment model (1TCM). (+)-[18F]Flubatine distribution volume (binding) was significantly reduced in bilateral mesial temporal cortex in AD patients compared with HCs (right 10.6 ± 1.1 vs 11.6 ± 1.4, p = 0.049; left 11.0 ± 1.1 vs 12.2 ± 1.8, p = 0.046; one-sided t tests each). PVE correction increased not only (+)-[18F]Flubatine binding of approximately 15% but also standard deviation of 0.4–70%. Cognitive test data and (+)-[18F]Flubatine binding were significantly correlated in the left anterior cingulate, right posterior cingulate, and right parietal cortex (r > 0.5, p < 0.05 each). In AD patients, (+)-[18F]Flubatine binding and [11C]PiB standardized uptake value ratios were negatively correlated in several regions; whereas in HCs, a positive correlation between cortical (+)-[18F]Flubatine binding and [11C]PiB accumulation in the white matter was found. No adverse event related to (+)-[18F]Flubatine occurred. Conclusion: (+)-[18F]Flubatine is a safe and stable PET ligand. Full kinetic modeling can be realized by 1TCM without metabolite correction. (+)-[18F]Flubatine binding affinity was high enough to detect group differences. Of interest, correlation between white matter β-amyloid PET uptake and (+)-[18F]Flubatine binding indicated an association between white matter integrity and availability of α4β2 nAChRs. Overall, (+)-[18F]Flubatine showed favorable characteristics and has therefore the potential to serve as α4β2 nAChR–targeting PET ligand in further clinical trials. [ABSTRACT FROM AUTHOR]

Background: The pathological processes underlying cognitive impairment in Parkinson's disease (PD) are heterogeneous and the contribution of cerebral amyloid deposits is poorly defined, particularly in the early stages of the disease.Objective: To investigate regional [18F]florbetaben binding to amyloid-β (Aβ) and its contribution to cognitive dysfunction in early stage PD.Methods: A multicenter cohort of 48 PD patients from the Parkinson's Progression Marker Initiative (PPMI) underwent [18F]florbetaben positron emission tomography (PET) scanning. Clinical features, including demographic characteristics, motor severity, cerebrospinal fluid (CSF), and cognitive testing were systematically assessed according to the PPMI study protocol. For the purpose of this study, we analyzed various neuropsychological tests assessing all cognitive functions.Results: There were 10/48 (21%) amyloid positive PD patients (PDAβ+). Increased [18F]florbetaben uptake in widespread cortical and subcortical regions was associated with poorer performance on global cognition, as assessed by Montreal Cognitive Assessment (MoCA), and impaired performance on Symbol Digit Modality test (SDMT). Further, we found that PDAβ+ patients had higher CSF total-tau/Aβ1 - 42 (p = 0.001) and phosphorylated-tau/Aβ1 - 42 in (p = 0.002) compared to amyloid-negative PD.Conclusion: These findings suggest that multiple disease processes are associated with PD cognitive impairment and amyloid deposits may be observed already in early stages. However, prevalence of amyloid positivity is in the range of literature age-matched control population. Increased cortical and subcortical amyloid is associated with poor performance in attentive-executive domains while cognitive deficits at MoCA and SDMT may identify amyloid-related dysfunction in early PD. [ABSTRACT FROM AUTHOR]

The article focuses on advancing the biological definitions of Parkinson's disease (PD) and dementia with Lewy bodies (DLB) through novel biomarker-driven frameworks like SyNeurGe and NSD-ISS, analogous to the ATN system in Alzheimer's disease. Topics include the integration of α-synucleinopathy detection using seed amplification assays in cerebrospinal fluid and skin, alongside neurodegeneration and dopaminergic deficit assessments via imaging biomarkers.