Purpose To investigate the cortical thickness features in Parkinson's disease(PD)patients at various stages and their association with dopamine transporter(DAT)levels in the putamen.Materials and Methods We retrospectively enrolled 30 PD patients and 15 healthy subject who underwent 11C-CFT PET and T1 MRI scans at the Department of Nuclear Medicine/PET Center of Huashan Hospital from August 2016 to October 2020.DAT average radioactivity in the anterior and posterior putamen was analysis using SPM12 software,with the occipital lobe as the reference region.Cortical segmentation and reconstruction were performed on T1 images using Freesurfer v7.2.The differences in cortical thinning between the groups were compared using a general linear model.Additionally,the relationship between cortical thickness in various brain regions and DAT uptake in the putamen were assessed.Results Compared to healthy subjects,significant cortical thinning was observed in the left inferior parietal lobule and the right and left inferior middle frontal gyrus of PD patients(all P<0.05).There was a significant positive correlation between the cortical thickness of the left inferior parietal lobule and right inferior middle frontal gyrus and DAT uptake in the corresponding anterior/posterior parts of the putamen(r=0.30-0.47,all P<0.05).Furthermore,the DAT uptake in the right precentral gyrus was positively correlated with the ipsilateral posterior putamen,exhibiting a stronger correlation than on the contralateral side(r=0.32,P=0.029).Conclusion The results show that the thickness of the thinning cortex area in the PD patients correlates significantly positively with DAT levels in the putamen,highlighting the importance of the basal ganglia cortical circuit and providing a basis for further research into the neural mechanisms of PD.

Purpose To investigate the cortical thickness features in Parkinson's disease(PD)patients at various stages and their association with dopamine transporter(DAT)levels in the putamen.Materials and Methods We retrospectively enrolled 30 PD patients and 15 healthy subject who underwent 11C-CFT PET and T1 MRI scans at the Department of Nuclear Medicine/PET Center of Huashan Hospital from August 2016 to October 2020.DAT average radioactivity in the anterior and posterior putamen was analysis using SPM12 software,with the occipital lobe as the reference region.Cortical segmentation and reconstruction were performed on T1 images using Freesurfer v7.2.The differences in cortical thinning between the groups were compared using a general linear model.Additionally,the relationship between cortical thickness in various brain regions and DAT uptake in the putamen were assessed.Results Compared to healthy subjects,significant cortical thinning was observed in the left inferior parietal lobule and the right and left inferior middle frontal gyrus of PD patients(all P<0.05).There was a significant positive correlation between the cortical thickness of the left inferior parietal lobule and right inferior middle frontal gyrus and DAT uptake in the corresponding anterior/posterior parts of the putamen(r=0.30-0.47,all P<0.05).Furthermore,the DAT uptake in the right precentral gyrus was positively correlated with the ipsilateral posterior putamen,exhibiting a stronger correlation than on the contralateral side(r=0.32,P=0.029).Conclusion The results show that the thickness of the thinning cortex area in the PD patients correlates significantly positively with DAT levels in the putamen,highlighting the importance of the basal ganglia cortical circuit and providing a basis for further research into the neural mechanisms of PD.

Purpose To explore the application value of combining 18F-FDG PET images with interpretable deep learning radiomics(IDLR)models in the differential diagnosis of primary Parkinson's disease(IPD)and atypical Parkinson's syndrome.Materials and Methods This cross-sectional study was conducted using the Parkinson's Disease PET Imaging Benchmark Database from Huashan Hospital,Fudan University from March 2015 to February 2023.A total of 330 Parkinson's disease patients underwent 18F-FDG PET imaging,both 18F-FDG PET imaging and clinical scale information were collected for all subjects.The study included two cohorts,a training group(n=270)and a testing group(n=60),with a total of 211 cases in the IPD group,59 cases in the progressive supranuclear palsy(PSP)group,and a group of 60 patients with multiple system atrophy(MSA).The clinical information between different groups were compared.An IDLR model was developed to extract feature indicators.Under the supervision of radiomics features,IDLR features were selected from the features collected by neural network extractors,and a binary support vector machine model was constructed for the selected features in images of in testing group.The constructed IDLR model,traditional radiomics model and standard uptake ratio model were separately used to calculate the performance metrics and area under curve values of deep learning models for pairwise classification between IPD/PSP/MSA groups.The study conducted independent classification and testing in two cohorts using 100 10-fold cross-validation tests.Brain-related regions of interest were displayed through feature mapping,using gradient weighted class activation maps to highlight and visualize the most relevant information in the brain.The output heatmaps of different disease groups were examined and compared with clinical diagnostic locations.Results The IDLR model showed promising results for differentiating between Parkinson's syndrome patients.It achieved the best classification performance and had the highest area under the curve values compared to other comparative models such as the standard uptake ratio model(Z=1.22-3.23,all P<0.05),and radiomics model(Z=1.31-2.96,all P<0.05).The area under the curve values for the IDLR model in differentiating MSA and IPD were 0.935 7,for MSA and PSP were 0.975 4,for IPD and PSP were 0.982 5 in the test set.The IDLR model also showed consistency between its filtered feature maps and the visualization of gradient-weighted class activation mapping slice thermal maps in the radiomics regions of interest.Conclusion The IDLR model has the potential for differential diagnosis between IPD and atypical Parkinson's syndrome in 18F-FDG PET images.

Purpose To explore the value of the new generation tau PET tracer 18F-Florzolotau in Alzheimer's disease(AD)at different stages.Materials and Methods Twenty-five MCI patients and sixty-one AD patients with positive β-amyloid status in Huashan Hospital,Fudan University from February 2020 to January 2022 were retrospectively enrolled with 18F-Florzolotau PET imaging and demographic and clinical data.The pre-processed PET images were analyzed by SPM two-sample t-test between MCI and AD groups,and the standardized uptake value ratios(SUVR)were extracted from the region of interest defined by SPM analysis(P＜0.001);scaled subprofile model/principal component analysis was used to construct the different tau related patterns(MCItauRP,ADtauRP)and calculate the corresponding expression values.The classification efficiency of SUVR and MCItauRP,ADtauRP expression values was evaluated by receiver operating characteristic curve.Results Compared with MCI patients,tau protein deposition of AD patients was increased mainly in the bilateral temporal,occipital lobe(P＜0.001),and the SUVR of these brain region in the AD group was higher than that in the MCI group(Z=-3.164,P＜0.00l);the expression values of MCItauRP and ADtauRP were significantly different between the AD group and MCI group(t=3.72,Z=-3.51;both P＜0.001),and these expression values of AD patients were higher than those in the MCI group;the accuracy of tauRP expression values and SUVR for the differentiation between the AD and MCI group were 61.63％,65.12％and 65.12％,respectively;the sensitivity was 88.00％,96.00％and 100.00％,respectively;the specificity was 50.82％,52.46％and 50.82％,respectively.Conclusion The new tau PET can identify and distinguish the differences in tau protein deposition between AD and MCI patients.However,the classification and diagnosis efficiency is not high.In the future,it is necessary to find a more ideal analysis method.

Objective:A voxel-level quantification method based on the tau IQ algorithm and Braak staging, excluding β-amyloid (Aβ) imaging, was developed to achieve specific tau quantification. Methods:This cross-sectional study included 92 subjects (35 males, 57 females; age (62.9±10.4) years) from the Nuclear Medicine/PET Center of Huashan Hospital, Fudan University between November 2018 and July 2020. The cohort comprised 28 cognitively normal (CN) individuals, 20 patients with mild cognitive impairment (MCI), and 44 patients with Alzheimer′s disease (AD). All participants underwent 18F-florzolotau PET imaging, Mini-Mental State Examination (MMSE), and Clinical Dementia Rating (CDR) scoring. A longitudinal tau dataset was constructed based on Braak staging. Voxel-level logistic regression fitting provided a baseline matrix, decomposed via least squares to yield the Tau load coefficient. One-way analysis of variance (with post hoc Tukey) was used to compare Tau load and SUV ratio (SUVR) among groups. ROC curve analysis was used to evaluate classification between CN, MCI and AD. Spearman rank correlation was used to assess the relationships between Tau load, SUVR, and MMSE scores or CDR scores. Results:The Tau load in the CN group was close to 0 and significantly lower than that in the MCI and AD groups ( F=55.03, P<0.001; post hoc tests all P<0.001). Significant differences were also observed in the SUVR across all ROIs ( F values: 36.46-55.38, all P<0.001). Compared to SUVR, Tau load demonstrated greater intergroup differences. In ROC curve analyses between each pair of CN, MCI, and AD groups, Tau load consistently achieved the highest AUC (0.754-1.000). Both Tau load and SUVR for each ROI were negatively correlated with MMSE scores ( rs values: from -0.698 to -0.583, all P<0.05) and positively correlated with CDR scores ( rs values: 0.648-0.783, all P<0.05), with Tau load showing the highest absolute correlation coefficients. Conclusion:Compared to the traditional semi-quantitative SUVR method, the Braak-tau IQ algorithm does not require a specific reference brain region to achieve specific tau quantification.

Objective:To establish standard spatial brain template and ROIs template of 11C-methyl- N-2β-carbomethoxy-3β-(4-fluorophenyl)tropane (CFT) PET images for automated quantitative analysis of dopamine transporter (DAT) distribution. Methods:From May 2014 to December 2015, 11C-CFT PET and MRI T 1 brain images of 16 healthy volunteers (3 males, 13 females; age (63.3±6.9) years) from Huashan Hospital, Fudan University were co-registered and smoothed using statistical parametric mapping(SPM)5 software based on MATLAB to create a standard spatial brain template. The ROIs template was established by ScAnVp procedures. These templates were clinically verified by using 11C-CFT PET images of 37 healthy volunteers (23 males, 14 females; age (61.7±7.1) years), 32 Parkinson′s disease (PD) patients (20 males, 12 females; age (61.1±5.4) years), 10 multiple system atrophy with predominant parkinsonism (MSA-P) patients (7 males, 3 females; age (60.8±7.1) years) and 10 progressive supranuclear palsy (PSP) patients (5 males, 5 females; age (58.4±6.1) years) from Huashan Hospital, Fudan University between January 2014 and March 2019. One-way analysis of variance was used to analyze data. Results:Based on the 11C-CFT PET images and MRI T 1 images of healthy volunteers, a standard spatial brain template for normalization of 11C-CFT PET images was created. The ROIs template was established including seven regions: bilateral caudate, anterior putamen, posterior putamen (along the long axis) and the occipital cortex. The ROIs template was accurately aligned in each verification group. The normal reference values of semi-quantitative DAT distribution in caudate, anterior putamen and posterior putamen were obtained (1.84±0.13, 2.18±0.16, 1.77±0.11). The semi-quantitative values of 11C-CFT uptake in each ROI in patients were significantly lower than those in healthy volunteers ( F values: 49.79-283.83, all P<0.05). Conclusion:The established brain templates with accurate spatial alignment for 11C-CFT image analysis can provide foundational tools for the application of 11C-CFT PET imaging in clinical practice and scientific research.

Multi-centre clinical trials on PET/CT brain imaging are complex to organize and require careful co-ordination and management. This article describes considerations, which are necessary when designing and starting a multi-centre clinical trial on PET/CT brain imaging, based on guidelines and multi-center clinical brain imaging studies, providing references for further studies.

Objective:To explore the potential application of combining 18F-FDG PET imaging and radiomics in the diagnosis of Parkinson′s disease (PD) and atypical parkinsonian syndromes (APS). Methods:A total of 154 subjects of two cohorts (training set and validation set) were enrolled from Huashan Hospital, Fudan University from March 2015 to August 2020 in this cross-sectional study, including 40 normal controls (NC; 23 males and 17 females, age: (60.2±10.5) years), 40 PD patients (20 males and 20 females, age: (64.7±6.3) years), 40 progressive supranuclear palsy (PSP) patients (20 males and 20 females, age: (64.1±5.9) years), and 34 multiple system atrophy (MSA) patients (19 males and 15 females, age: (65.0±9.2) years). 18F-FDG PET images and clinical scale were selected, and one-way analysis of variance was used to compare differences of clinical scale among groups. Radiomic features extraction and feature selection were carried out. Two and three classification models were constructed based on logistic regression, and the ROC curves of clinical model, radiomics model and combined model were calculated. Independent classification tests were conducted 100 times with 5-fold cross validation in two cohorts. Results:There were significant differences in the scores of unified PD Rating Scale (UPDRS) and Hoehn-Yahr rating scale (H&Y) among different groups in cohort 1 and cohort 2 respectively ( F values: 4.83-17.95, all P<0.05). A total of 2 444 imaging features were extracted from each subject, and after features selection, 15 features for classification were obtained. In the two classification experiment, the AUCs of the three models in binary classification of PD/MSA/PSP/NC group were 0.56-0.68, 0.74-0.93 and 0.72-0.93, respectively. The classification effects of the radiomics model were significantly better than those of the clinical model ( z values: 1.71-2.85, all P<0.05). In the three classification experiment, the sensitivity of the radiomics model reached 80%, 80% and 77% for PD, MSA and PSP, respectively. Conclusion:18F-FDG imaging combined with radiomics has potential in the diagnosis of PD and APS.

Due to the availability of 18F-FDG in PET centers, this article aims to advocate and promote the standardization of 18F-FDG PET brain imaging in dementia in order to improve the reliability, repeatability and comparison of the imaging process and results. It is also provided to guide the PET imaging operation standard and to give suggestions on image interpretation.

Objective: To investigate the value of statistical parametric mapping (SPM) analysis of ¹⁸F-fluorodeoxyglucose (FDG) PET imaging in the differential diagnosis of Parkinsonism in single-case level. Methods: SPM software was used to retrospectively analyze the ¹⁸F-FDG PET images of 160 patients (104 males, 56 females, age: 30-82 years) who were suspected with Parkinsonism at baseline and were clinical confirmed by follow-up from April 2010 to December 2017. ¹⁸F-FDG PET images of patients was compared with those of age-matched healthy controls in single-case level using two-sample t test in SPM software to obtain the imaging diagnosis. By comparing imaging diagnosis with the final clinical diagnosis, the diagnostic accuracy of SPM in the overall cohort as well as the early subcohort (duration of disease less than 2 years (56 males, 22 females, age: 50-82 years)) were calculated respectively. Results: Among 160 patients with Parkinsonism, 146(91.2%) had the same ¹⁸F-FDG PET diagnosis as their final clinical diagnosis. The diagnostic sensitivity for Parkinson′s disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and cortical basal ganglia degeneration (CBD) were 93.5%(86/92), 92.3%(24/26), 84.0%(21/25) and 15/17, respectively. The specificity were 95.6%(65/68), 95.5%(128/134), 96.3%(130/135) and 100%(143/143), respectively. In the early subcohort, the analysis also achieved similar differential diagnosis effectiveness(92.3%). Conclusion The single-case ¹⁸F-FDG PET imaging SPM analysis can be helpful in the early differential diagnosis of Parkinsonism effectively.