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BACKGROUND: Accurate analysis of quantitative PET data plays a crucial role in studying small, specific brain structures. The integration of PET and MRI through an integrated PET/MR system presents an opportunity to leverage the benefits of precisely aligned structural MRI and molecular PET images in both spatial and temporal dimensions. However, in many clinical workflows, PET studies are often performed without the aid of individually matched structural MRI scans, primarily for the sake of convenience in the data collection and brain segmentation possesses. Currently, two commonly employed segmentation strategies for brain PET analysis are distinguished: methods with or without MRI registration and methods employing either atlas-based or individual-based algorithms. Moreover, the development of artificial intelligence (AI)-assisted methods for predicting brain segmentation holds promise but requires further validation of their efficiency and accuracy for clinical applications. This study aims to compare and evaluate the correlations, consistencies, and differences among the above-mentioned brain segmentation strategies in quantification of brain metabolism in 18F-FDG PET/MR analysis. RESULTS: Strong correlations were observed among all methods (r = 0.932 to 0.999, P < 0.001). The variances attributable to subject and brain region were higher than those caused by segmentation methods (P < 0.001). However, intraclass correlation coefficient (ICC)s between methods with or without MRI registration ranged from 0.924 to 0.975, while ICCs between methods with atlas- or individual-based algorithms ranged from 0.741 to 0.879. Brain regions exhibiting significant standardized uptake values (SUV) differences due to segmentation methods were the basal ganglia nuclei (maximum to 11.50 ± 4.67%), and various cerebral cortexes in temporal and occipital regions (maximum to 18.03 ± 5.52%). The AI-based method demonstrated high correlation (r = 0.998 and 0.999, P < 0.001) and ICC (0.998 and 0.997) with FreeSurfer, substantially reducing the time from 8.13 h to 57 s on per subject. CONCLUSIONS: Different segmentation methods may have impact on the calculation of brain metabolism in basal ganglia nuclei and specific cerebral cortexes. The AI-based approach offers improved efficiency and is recommended for its enhanced performance.
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(1) Background: Accurate localization of the epileptogenic zone and understanding the related functional connectivity (FC) alterations are critical for the prediction of clinical prognosis in patients with temporal lobe epilepsy (TLE). We aim to localize the hypometabolic region in TLE patients, compare the differences in FC alterations based on hypometabolic region and structural lesion, respectively, and explore their relationships with clinical prognosis. (2) Methods: Thirty-two TLE patients and 26 controls were recruited. Patients underwent 18F-FDG PET/MR scan, surgical treatment, and a 2−3-year follow-up. Visual assessment and voxel-wise analyses were performed to identify hypometabolic regions. ROI-based FC analyses were performed. Relationships between clinical prognosis and FC values were performed by using Pearson correlation analyses and receiver operating characteristic (ROC) analysis. (3) Results: Hypometabolic regions in TLE patients were found in the ipsilateral hippocampus, parahippocampal gyrus, and temporal lobe (p < 0.001). Functional alterations based on hypometabolic regions showed a more extensive whole-brain FC reduction. FC values of these regions negatively correlated with epilepsy duration (p < 0.05), and the ROC curve of them showed significant accuracy in predicting postsurgical outcome. (4) Conclusions: In TLE patients, FC related with hypometabolic region obtained by PET/fMRI may provide value in the prediction of disease progression and seizure-free outcome.
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Objective: In the detection of seizure onset zones, arterial spin labeling (ASL) can overcome the limitations of positron emission tomography (PET) with 18F-fluorodeoxyglucose (18F-FDG), which is invasive, expensive, and radioactive. PET/magnetic resonance (MR) systems have been introduced that allow simultaneous performance of ASL and PET, but comparisons of these techniques with stereoelectroencephalography (SEEG) and comparisons among the treatment outcomes of these techniques are still lacking. Here, we investigate the effectiveness of ASL compared with that of SEEG and their outcomes in localizing mesial temporal lobe epilepsy (MTLE) and assess the correlation between simultaneously acquired PET and ASL. Methods: Between October 2016 and August 2017, we retrospectively studied 12 patients diagnosed with pure unilateral MTLE. We extracted and quantitatively computed values for ASL and PET in the bilateral hippocampus. SEEG findings and outcome were considered the gold standard of lateralization. Finally, the bilateral asymmetry index (AI) was calculated to assess the correlation between PET and ASL. Results: Our results showed that hypoperfusion in the hippocampus detected using ASL matched the SEEG-defined epileptogenic zone in this series of patients. The mean normalized voxel value of ASL in the contralateral hippocampus was 0.97⯱â¯0.19, while in the ipsilateral hippocampus, it was 0.84⯱â¯0.14. Meanwhile, significantly decreased perfusion and metabolism were observed in these patients (Wilcoxon, pâ¯<â¯0.05), with a significant positive correlation between the AI values derived from PET and ASL (Pearson's correlation, râ¯=â¯0.74, pâ¯<â¯0.05). Significance: In our SEEG- and outcome-defined patients with MTLE, ASL could provide significant information during presurgical evaluation, with the hypoperfusion detected with ASL reliably lateralizing MTLE. This non-invasive technique may be used as an alternative diagnostic tool for MTLE lateralization.