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PET plays an increasingly important role in the management of brain tumors. This review outlines currently available PET radiotracers and their respective indications. It specifically focuses on 18F-FDG, amino acid and somatostatin receptor radiotracers, for imaging gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in radiopharmaceuticals, image analyses and translational applications to therapy are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain MRI for all medical professionals implicated in brain tumor diagnosis and care.

ObjectiveTo consolidate current understanding of detection sensitivity of brain 18F-FDG PET scans in the diagnosis of autoimmune encephalitis and to define specific metabolic imaging patterns for the most frequently occurring autoantibodies.MethodsA systematic and exhaustive search of data available in the literature was performed by querying the PubMed/MEDLINE and Cochrane databases for the search terms: ((PET) OR (positron emission tomography)) AND ((FDG) OR (fluorodeoxyglucose)) AND ((encephalitis) OR (brain inflammation)). Studies had to satisfy the following criteria: (i) include at least ten pediatric or adult patients suspected or diagnosed with autoimmune encephalitis according to the current recommendations, (ii) specifically present 18F-FDG PET and/or morphologic imaging findings. The diagnostic 18F-FDG PET detection sensitivity in autoimmune encephalitis was determined for all cases reported in this systematic review, according to a meta-analysis following the PRISMA method, and selected publication quality was assessed with the QUADAS-2 tool.ResultsThe search strategy identified 626 articles including references from publications. The detection sensitivity of 18F-FDG PET was 87% (80–92%) based on 21 publications and 444 patients included in the meta-analysis. We also report specific brain 18F-FDG PET imaging patterns for the main encephalitis autoantibody subtypes.Conclusion and relevanceBrain 18F-FDG PET has a high detection sensitivity and should be included in future diagnostic autoimmune encephalitis recommendations. Specific metabolic 18F-FDG PET patterns corresponding to the main autoimmune encephalitis autoantibody subtypes further enhance the value of this diagnostic.

Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due to the difficulties of data collection and data privacy, finding an appropriate dataset (balanced, enough samples, etc.) is quite a challenge. Although image synthesis could be beneficial to overcome this issue, synthesizing 3D images is a hard task. The main objective of this paper is to generate 3D T1 weighted MRI corresponding to FDG-PET. In this study, we propose a separable convolution-based Elicit generative adversarial network (E-GAN). The proposed architecture can reconstruct 3D T1 weighted MRI from 2D high-level features and geometrical information retrieved from a Sobel filter. Experimental results on the ADNI datasets for healthy subjects show that the proposed model improves the quality of images compared with the state of the art. In addition, the evaluation of E-GAN and the state of art methods gives a better result on the structural information (13.73% improvement for PSNR and 22.95% for SSIM compared to Pix2Pix GAN) and textural information (6.9% improvements for homogeneity error in Haralick features compared to Pix2Pix GAN).

PurposeKetogenic diet (KD) is recommended to avoid intense [18F]FDG myocardial physiologic uptake in PET imaging. Neuroprotective and anti-seizure effects of KD have been suggested, but their mechanisms remain to be elucidated. This [18F]FDG PET study aims to evaluate the effect of KD on glucose brain metabolism.MethodSubjects who underwent KD prior to whole-body and brain [18F]FDG PET between January 2019 and December 2020 in our department for suspected endocarditis were retrospectively included. Myocardial glucose suppression (MGS) on whole-body PET was analyzed. Patients with brain abnormalities were excluded. Thirty-four subjects with MGS (mean age: 61.8 ± 17.2 years) were included in the KD population, and 14 subjects without MGS were considered for a partial KD group (mean age: 62.3 ± 15.1 years). Brain SUVmax was first compared between these two KD groups to determine possible global uptake difference. Semiquantitative voxel-based intergroup analyses were secondarily performed to determine possible inter-regional differences by comparing KD groups with and without MGS, separately, to 27 healthy subjects fasting for at least 6 h (mean age of 62.4 ± 10.9 years), and KD groups between them (p-voxel < 0.001, and p-cluster < 0.05, FWE-corrected).ResultsA 20% lower brain SUVmax was found in subjects under KD with MGS in comparison to those without MGS (Student’s t-test, p = 0.02). Whole-brain voxel-based intergroup analysis revealed that patients under KD with and without MGS had relative hypermetabolism of limbic regions including medial temporal cortices and cerebellum lobes and relative hypometabolism of bilateral posterior regions (occipital), without significant difference between them.ConclusionKD globally reduces brain glucose metabolism but with regional differences, requiring special attention to clinical interpretation. On a pathophysiological perspective, these findings could help understand underlying neurological effects of KD through possible decrease of oxidative stress in posterior regions and functional compensation in the limbic regions.

Introduction The impact of age is crucial and must be taken into account when applying a voxel-based quantitative analysis on brain images from [ 18 F]-fluorodeoxyglucose Positron Emission Tomography (FDG-PET). This study aimed to determine whether age-related changes in brain FDG-PET images are more accurately assessed when the conventional statistical parametric mapping (SPM) normalization method is used with an adaptive template, obtained from analysed PET images using a Block-Matching (BM) algorithm to fit with the characteristics of these images. Methods Age-related changes in FDG-PET images were computed with linear models in 84 neurologically healthy subjects (35 women, 19 to 82-year-old), and compared between results provided by the SPM normalization algorithm applied on its dedicated conventional template or on the adaptive BM template. A threshold P value of 0.05 was used together with a family-wise error correction. Results The age-related changes in FDG-PET images were much more apparent when computed with the adaptive template than with the conventional template as evidenced by: (1) stronger correlation coefficients with age for the overall frontal and temporal uptake values (respective R 2 values of 0.20 and 0.07) and (2) larger extents of involved areas (13 and 5 % of whole brain template volume, respectively), leading to reveal several age-dependent areas (especially in dorsolateral prefrontal, inferior temporal/fusiform and primary somatosensory cortices). Conclusion Age-related changes in brain FDG uptake may be more accurately determined when applying the SPM method of voxel-based quantitative analysis on a template that best fits the characteristics of the analysed TEP images.

ObjectiveWe aim to search for predictors of survival among clinical and brain 18F-FDG positron emission tomography (PET) metabolic features in our cohort of patients with multiple system atrophy (MSA).MethodsWe included patients with a ‘probable’ MSA diagnosis for whom a clinical evaluation and a brain PET were performed early in the course of the disease (median 3 years, IQR 2–5). A retrospective analysis was conducted using standardised data collection. Brain PET metabolism was characterised using the Automated Anatomical Labelling Atlas. A Cox model was applied to look for factors influencing survival. Kaplan-Meier method estimated the survival rate. We proposed to develop a predictive ‘risk score’, categorised into low-risk and high-risk groups, using significant variables entered in multivariate Cox regression analysis.ResultsEighty-five patients were included. The overall median survival was 8 years (CI 6.64 to 9.36). Poor prognostic factors were orthostatic hypotension (HR=6.04 (CI 1.58 to 23.12), p=0.009), stridor (HR=3.41 (CI 1.31 to 8.87), p=0.012) and glucose PET hypometabolism in the left insula (HR=0.78 (CI 0.66 to 0.92), p=0.004). Good prognostic factors were time to diagnosis (HR=0.68 (CI 0.54 to 0.86), p=0.001) and use of selective serotonin reuptake inhibitor (SSRI) (HR=0.17 (CI 0.06 to 0.46), p<0.001). The risk score revealed a 5-year gap separating the median survival of the two groups obtained (5 years vs 10 years; HR=5.82 (CI 2.94 to 11.49), p<0.001).ConclusionThe clinical prognosis factors we have described support published studies. Here, we also suggest that brain PET is of interest for prognosis assessment and in particular in the search for left insula hypometabolism. Moreover, SSRIs are a potential drug candidate to slow the progression of the disease.

Fibromyalgia (FM) is a chronic overlapping pain condition that impairs quality of life (QOL). Adapted physical activity (APA) is recommended as a key non-pharmacological intervention, though its implementation and underlying mechanisms remain limited. Fibromyactiv, a prospective, randomised, single-blind clinical and neuroimaging study, evaluated the efficacy and mechanisms of a structured, supervised APA program in FM. Seventy-nine adults were assigned to supervised APA ( n  = 39) or standard care ( n  = 40) and followed for 12 months. The intervention consisted of diversified, low-intensity, fractionated sessions three times weekly for 6 months. Outcomes included the Fibromyalgia Impact Questionnaire (FIQ, primary endpoint at M6), clinical and psychological scales, functional tests, pedometer monitoring, and mechanistic measures (¹⁸F-FDG PET, blood/urine biomarkers). Supervised APA produced significantly greater FIQ improvement and broader benefits across PGIC, Widespread Pain Index, Symptom Severity Scale, tender points, mean pain, sleep quality, flexibility, and daily step counts. Immediate symptom relief occurred after each session. Brain PET revealed increased metabolism in the right cerebellum in the APA group, correlating positively with step-count improvement. This interdisciplinary trial demonstrates that a reproducible supervised APA program yields sustained improvements in QOL, symptoms, and functional activity in FM, potentially mediated by cerebellar modulation of motor and behavioural engagement. ClinicalTrials.govIdentifier: NCT03640806 N°IDRCB:2017-A03011-52. Sponsor: Assistance Publique Hôpitaux De Marseille (Marseille Public Hospital System). Other Study ID Numbers: 2017-57.

BackgroundThe objective of the study is to define the most appropriate region for intensity normalization in brain 18FDG PET semi-quantitative analysis. The best option could be based on previous absolute quantification studies, which showed that the metabolic changes related to ageing affect the quasi-totality of brain regions in healthy subjects. Consequently, brain metabolic changes related to ageing were evaluated in two populations of healthy controls who underwent conventional (n = 56) or digital (n = 78) 18FDG PET/CT. The median correlation coefficients between age and the metabolism of each 120 atlas brain region were reported for 120 distinct intensity normalizations (according to the 120 regions). SPM linear regression analyses with age were performed on most significant normalizations (FWE, p < 0.05).ResultsThe cerebellum and pons were the two sole regions showing median coefficients of correlation with age less than − 0.5. With SPM, the intensity normalization by the pons provided at least 1.7- and 2.5-fold more significant cluster volumes than other normalizations for conventional and digital PET, respectively.ConclusionsThe pons is the most appropriate area for brain 18FDG PET intensity normalization for examining the metabolic changes through ageing.

Background and significance. There is a need to develop new hypothesis-driven treatment for both both major depression (MD) and schizophrenia in which the risk of depression is 5 times higher than the general population. Major depression has been also associated with poor illness outcomes including pain, metabolic disturbances, and less adherence. Conventional antidepressants are partly effective, and 44% of the subjects remain unremitted under treatment. Improving MD treatment efficacy is thus needed to improve the SZ prognosis. Microbiota-orientated treatments are currently one of the most promising tracks. Method. This work is a systematic review synthetizing data of arguments to develop microbiota-orientated treatments (including fecal microbiota transplantation (FMT)) in major depression and schizophrenia. Results. The effectiveness of probiotic administration in MD constitutes a strong evidence for developing microbiota-orientated treatments. Probiotics have yielded medium-to-large significant effects on depressive symptoms, but it is still unclear if the effect is maintained following probiotic discontinuation. Several factors may limit MD improvement when using probiotics, including the small number of bacterial strains administered in probiotic complementary agents, as well as the presence of a disturbed gut microbiota that probably limits the probiotics’ impact. FMT is a safe technique enabling to improve microbiota in several gut disorders. The benefit/risk ratio of FMT has been discussed and has been recently improved by capsule administration. Conclusion. Cleaning up the gut microbiota by transplanting a totally new human gut microbiota in one shot, which is referred to as FMT, is likely to strongly improve the efficacy of microbiota-orientated treatments in MD and schizophrenia and maintain the effect over time. This hypothesis should be tested in future clinical trials.