Objective: To consolidate current understanding of detection sensitivity of brain 18 F-FDG PET scans in the diagnosis of autoimmune encephalitis and to de ne speci c metabolic imaging patterns for the most frequently occurring autoantibodies. Methods: A systematic and exhaustive search of data available in the literature was performed by querying the PubMed/MEDLINE and Cochrane databases for the search terms: "FDG PET" and ""encephalitis" or "brain in ammation"". Studies had to satisfy the following criteria: i. include at least one patient suspected or diagnosed with autoimmune encephalitis according to the current recommendations, ii. be an original case-report iii. speci cally present 18 F-FDG PET and/or morphologic imaging ndings. The diagnostic 18 F-FDG PET detection sensitivity in autoimmune encephalitis was determined for all cases reported in the literature and a meta-analysis, according to the PRISMA method, was performed on a subset of these, which included PET scans for at least 10 patients, and whose quality was assessed with the QUADAS-2 tool. Results: The search strategy identi ed 1113 articles. The detection sensitivity of 18 F-FDG PET was 90%, based on 176 publications and 720 patients and 80% [75%-84%] by meta-analysis based on 21 publications and 444 patients. We also report speci c brain 18 F-FDG PET imaging patterns for the main encephalitis autoantibody subtypes. Conclusion and Relevance: Brain 18 F-FDG PET has a high detection sensitivity and should be included in future diagnostic autoimmune encephalitis recommendations. Speci c metabolic 18 F-FDG PET patterns corresponding to the main autoimmune encephalitis autoantibody subtypes further enhance the value of this diagnostic.

Background Brain 18 F-FDG PET imaging has the potential to provide an objective assessment of brain involvement in post-COVID-19 conditions but previous studies of heterogeneous patient series yield inconsistent results. The current study aimed to investigate brain 18 F-FDG PET findings in a homogeneous series of outpatients with post-COVID-19 conditions and to identify associations with clinical patient characteristics. Methods We retrospectively included 28 consecutive outpatients who presented with post-COVID-19 conditions between September 2020 and May 2022 and who satisfied the WHO definition, and had a brain 18 F-FDG PET for suspected brain involvement but had not been hospitalized for COVID-19. A voxel-based group comparison with 28 age- and sex-matched healthy controls was performed (p-voxel at 0.005 uncorrected, p-cluster at 0.05 FWE corrected) and identified clusters were correlated with clinical characteristics. Results Outpatients with post-COVID-19 conditions exhibited diffuse hypometabolism predominantly involving right frontal and temporal lobes including the orbito-frontal cortex and internal temporal areas. Metabolism in these clusters was inversely correlated with the number of symptoms during the initial infection ( r =  − 0.44, p = 0.02) and with the duration of symptoms ( r =  − 0.39, p = 0.04). Asthenia and cardiovascular, digestive, and neurological disorders during the acute phase and asthenia and language disorders during the chronic phase ( p ≤ 0.04) were associated with these hypometabolic clusters. Conclusion Outpatients with post-COVID-19 conditions exhibited extensive hypometabolic right fronto-temporal clusters. Patients with more numerous symptoms during the initial phase and with a longer duration of symptoms were at higher risk of persistent brain involvement. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-06013-2.

Background The 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). Results The 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. Conclusions The pons is the most appropriate area for brain 18FDG PET intensity normalization for examining the metabolic changes through ageing.

IntroductionThe objective of this study was to identify prenatal markers of histological chorioamnionitis (HC) during pPROM using fetal computerized cardiotocography (cCTG).Materials and methodsRetrospective review of medical records from pregnant women referred for pPROM between 26 and 34 weeks, in whom placental histology was available, in a tertiary level obstetric service over a 5-year period. Fetal heart rate variability was assessed using cCTG. Patients were included if they were monitored at least six times in the 72 hours preceding delivery. Clinical and biological cCTG parameters during the pPROM latency period were compared between cases with or without HC.ResultsIn total, 222 pPROM cases were observed, but cCTG data was available in only 23 of these cases (10 with and 13 without HC) after exclusion of co-morbidities which may potentially perturb fetal heart rate variability measures. Groups were comparable for maternal age, parity, gestational age at pPROM, pPROM duration and neonatal characteristics (p>0.1). Baseline fetal heart rate was higher in the HC group [median 147.3 bpm IQR (144.2–149.2) vs. 141.3 bpm (137.1–145.4) in no HC group; p = 0.02]. The number of low variation episodes [6.4, (3.5–15.3) vs. 2.3 (1–5.2); p = 0.04] was also higher in the HC group, whereas short term variations were lower in the HC group [7.1 ms (6–7.4) vs. 8.1 ms (7.4–9); p = 0.01] within 72 hours before delivery. Differences were especially discriminant within 24 hours before delivery, with less short-term variation [5 ms (3.7–5.9) vs. 7.8 ms (5.4–8.7); p = 0.007] and high variation episodes [3.9 (4.9–3.2) vs. 0.8 (1.5–0.2); p < 0.001] in the HC group.ConclusionThese results show differences in fetal heart rate variability, suggesting that cCTG could be used clinically to diagnoses chorioamnionitis during the pPROM latency period.

Robust, real-time event detection from physiological signals acquired during long-term ambulatory monitoring still represents a major challenge for highly-artifacted signals. In this paper, we propose an original and generic multi-feature probabilistic detector (MFPD) and apply it to real-time QRS complex detection under noisy conditions. The MFPD method calculates a binary Bayesian probability for each derived feature and makes a centralized fusion, using the Kullback-Leibler divergence. The method is evaluated on two ECG databases: 1) the MIT-BIH arrhythmia database from Physionet containing clean ECG signals, 2) a benchmark noisy database created by adding noise recordings of the MIT-BIH noise stress test database, also from Physionet, to the MIT-BIH arrhythmia database. Results are compared with a well-known wavelet-based detector, and two recently published detectors: one based on spatiotemporal characteristic of the QRS complex and the second, as the MFDP, based on feature calculations from the University of New South Wales detector (UNSW). For both benchmark Physionet databases, the proposed MFPD method achieves the lowest standard deviation in sensitivity and positive predictivity (+P) despite its online algorithm architecture. While the statistics are comparable for low-to mildly artifactual ECG signals, the MFPD outperforms reference methods for artifacted ECG with low SNR levels reaching 87.48 ± 14.21% in sensitivity and 89.39 ± 14.67% in +P as compared to 88.30 ± 17.66% and 86.06 ± 19.67% respectively from UNSW, the best performing reference method. With demonstrations on the extensively studied QRS detection problem, we consider that the proposed generic structure of the multi-feature probabilistic detector should offer promising perspectives for long-term monitoring applications for highly-artifacted signals.

The physiological mechanism induced by the isocitrate dehydrogenase 1 (IDH1) mutation, associated with better treatment response in gliomas, remains unknown.The aim of this preclinical study was to characterize the IDH1 mutation through in vivo multiparametric MRI and MRS. Multiparametric MRI, including the measurement of blood flow, vascularity, oxygenation, permeability, and in vivo MRS, was performed on a 4.7 T animal MRI system in rat brains grafted with human-derived glioblastoma U87 cell lines expressing or not the IDH1 mutation by the CRISPR/Cas9 method, and secondarily characterized with additional ex vivo HR-MAS and histological analyses. In univariate analyses, compared with IDH1−, IDH1+ tumors exhibited higher vascular density (p < 0.01) and better perfusion (p = 0.02 for cerebral blood flow), but lower vessel permeability (p < 0.01 for time to peak (TTP), p = 0.04 for contrast enhancement) and decreased T 1 map values (p = 0.02). Using linear discriminant analysis, vascular density and TTP values were found to be independent MRI parameters for characterizing the IDH1 mutation (p < 0.01). In vivo MRS and ex vivo HR-MAS analysis showed lower metabolites of tumor aggressiveness for IDH1+ tumors (p < 0.01). Overall, the IDH1 mutation exhibited a higher vascularity on MRI, a lower permeability, and a less aggressive metabolic profile. These MRI features may prove helpful to better pinpoint the physiological mechanisms induced by this mutation.

Purpose Digital PET cameras markedly improve sensitivity and spatial resolution of brain 18F-FDG PET images compared to conventional cameras. Our study aimed to assess whether specific control databases are required to improve the diagnostic performance of these recent advances. Methods We retrospectively selected two groups of subjects, twenty-seven Alzheimer's Disease (AD) patients and twenty-two healthy control (HC) subjects. All subjects underwent a brain 18F-FDG PET on a digital camera (Vereos, Philips®). These two group (AD and HC) are compared, using a Semi-Quantitative Analysis (SQA), to two age and sex matched controls acquired with a digital PET/CT (Vereos, Philips®) or a conventional PET/CT (Biograph 6, Siemens®) camera, at group and individual levels. Moreover, individual visual interpretation of SPM T-maps was provided for the positive diagnosis of AD by 3 experienced raters. Results At group level, SQA using digital controls detected more marked hypometabolic areas in AD (+ 116 cm3 at p < 0.001 uncorrected for the voxel, corrected for the cluster) than SQA using conventional controls. At the individual level, the accuracy of SQA for discriminating AD using digital controls was higher than SQA using conventional controls (86% vs. 80%, p < 0.01, at p < 0.005 uncorrected for the voxel, corrected for the cluster), with higher sensitivity (89% vs. 78%) and similar specificity (82% vs. 82%). These results were confirmed by visual analysis (accuracies of 84% and 82% for digital and conventional controls respectively, p = 0.01). Conclusion There is an urgent need to establish specific digital PET control databases for SQA of brain 18F-FDG PET images as such databases improve the accuracy of AD diagnosis.

Objective: To 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. Methods: A systematic and exhaustive search of data available in the literature was performed by querying the PubMed/MEDLINE and Cochrane databases for the search terms: "FDG PET" and “"encephalitis" or "brain inflammation"”. Studies had to satisfy the following criteria: i. include at least one patient suspected or diagnosed with autoimmune encephalitis according to the current recommendations, ii. be an original case-report iii. 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 the literature and a meta-analysis, according to the PRISMA method, was performed on a subset of these, which included PET scans for at least 10 patients, and whose quality was assessed with the QUADAS-2 tool.Results: The search strategy identified 1113 articles. The detection sensitivity of 18F-FDG PET was 90%, based on 176 publications and 720 patients and 80% [75%-84%] by meta-analysis based on 21 publications and 444 patients. We also report specific brain 18F-FDG PET imaging patterns for the main encephalitis autoantibody subtypes.Conclusion and Relevance: Brain 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.