Evaluation of the early-phase [18F]AV45 PET as an optimal surrogate of [18F]FDG PET in ageing and Alzheimer's clinical syndrome.
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ABSTRACT:
SUBMITTER: Vanhoutte M
PROVIDER: S-EPMC8274342 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gating of positron emission tomography images has been shown to reduce the motion effects, especially when imaging small targets, such as coronary plaques. However, the selection of optimal number of gates for gating remains a challenge. Selecting too high number of gates results in a loss of signal-to-noise ratio, while too low number of gates does remove only part of the motion. Here, we introduce a respiratory-cardiac motion model to determine the optimal number of respiratory and cardiac gates. We evaluate the model using a realistic heart phantom and data from 12 cardiac patients (47-77 years, 64.5 on average). To demonstrate the benefits of our model, we compared it with an existing respiratory model. Based on our study, the optimal number of gates was determined to be five respiratory and four cardiac gates in the phantom and patient studies. In the phantom study, the diameter of the most active hot spot was reduced by 24% in the dual gated images compared to non-gated images. In the patient study, the thickness of myocardium wall was reduced on average by 21%. In conclusion, the motion model can be used for estimating the optimal number of respiratory and cardiac gates for dual gating.
| S-EPMC7653943 | biostudies-literature
Project description:Knowledge of the within-subject variability of 18F-FDG PET/MRI measurements is necessary for proper interpretation of quantitative PET or MRI metrics in the context of therapeutic efficacy assessments with integrated PET/MRI scanners. The goal of this study was to determine the test-retest repeatability of these metrics on PET/MRI, with comparison to similar metrics acquired by PET/CT. Methods: This prospective study enrolled subjects with pathology-proven pelvic malignancies. Baseline imaging consisted of PET/CT immediately followed by PET/MRI, using a single 370-MBq 18F-FDG dose. Repeat imaging was performed within 7 d using an identical imaging protocol, with no oncologic therapy between sessions. PET imaging on both scanners consisted of a list-mode acquisition at a single pelvic station. The MRI consisted of 2-point Dixon imaging for attenuation correction, standard sequences for anatomic correlation, and diffusion-weighted imaging. PET data were statically reconstructed using various frame durations and minimizing uptake time differences between sessions. SUV metrics were extracted for both PET/CT and PET/MRI in each imaging session. Apparent diffusion coefficient (ADC) metrics were extracted for both PET/MRI sessions. Results: The study cohort consisted of 14 subjects (13 female, 1 male) with various pelvic cancers (11 cervical, 2 rectal, 1 endometrial). For SUVmax, the within-subject coefficient of variation (wCV) appeared higher for PET/CT (8.5%-12.8%) than PET/MRI (6.6%-8.7%) across all PET reconstructions, though with no significant repeatability differences (all P values ≥ 0.08) between modalities. For lean body mass-adjusted SUVpeak, the wCVs appeared similar for PET/CT (9.9%-11.5%) and PET/MRI (9.2%-11.3%) across all PET reconstructions, again with no significant repeatability differences (all P values ≥ 0.14) between modalities. For PET/MRI, the wCV for ADCmedian of 3.5% appeared lower than the wCVs for SUVmax (6.6%-8.7%) and SULpeak (9.2%-11.3%), though without significant repeatability differences (all P values ≥ 0.23). Conclusion: For solid tumors of the pelvis, the repeatability of the evaluated SUV and ADC metrics on 18F-FDG PET/MRI is both acceptably high and similar to previously published values for 18F-FDG PET/CT and MRI, supporting the use of 18F-FDG PET/MRI for quantitative oncologic treatment response assessments.
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Project description:Preclinical animal model studies of brain energy metabolism and neuroinflammation in Alzheimer's disease have produced conflicting results, hampering both the elucidation of the underlying disease mechanism and the development of effective Alzheimer's disease therapies. Here, we aimed to quantify the relationship between brain energy metabolism and neuroinflammation in the APP/PS1-21 transgenic mouse model of Alzheimer's disease using longitudinal in vivo18F-FDG and 18F-DPA-714) PET imaging and ex vivo brain autoradiography. APP/PS1-21 (TG, n?=?9) and wild type control mice (WT, n?=?9) were studied longitudinally every third month from age 6 to 15 months with 18F-FDG and 18F-DPA-714 with a one-week interval between the scans. Additional TG (n?=?52) and WT (n?=?29) mice were used for ex vivo studies. In vivo, the 18F-FDG SUVs were lower and the 18F-DPA-714 binding ratios relative to the cerebellum were higher in the TG mouse cortex and hippocampus than in WT mice at age 12 to 15 months ( p?<?0.05). The ex vivo cerebellum binding ratios supported the results of the in vivo18F-DPA-714 studies but not the 18F-FDG studies. This longitudinal PET study demonstrated decreased energy metabolism and increased inflammation in the brains of APP/PS1-21 mice compared to WT mice.
| S-EPMC5536795 | biostudies-literature
Project description:PurposeThis study aimed to explore the utility of hippocampal radiomics using multiparametric simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) for early diagnosis of Alzheimer's disease (AD).MethodsA total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F-fluorodeoxyglucose (18F-FDG) PET, 3D arterial spin labeling (ASL), and high-resolution T1-weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad-Score) of logistic regression models were evaluated from 5-fold cross-validation.ResultsThe hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single-modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad-Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests.ConclusionMultiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications.
| S-EPMC11017421 | biostudies-literature
Project description:BackgroundThe clinical diagnosis of deep sternal wound infection (DSWI) is supported by imaging findings including 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT). To avoid misinterpretation due to normal post-surgery inflammation we assessed normal imaging findings in non-infected patients after sternotomy.MethodsThis is a prospective cohort study including non-infectious patients with sternotomy. All patients underwent 18F-FDG-PET/CT at either 5 weeks (group 1), 12 weeks (group 2) or 52 weeks (group 3) post-surgery. 18F-FDG uptake was scored visually in five categories and assessed quantitatively.ResultsA total of 44 patients were included. Sternal mean SUVmax was 7.34 (± 1.86), 5.22 (± 2.55) and 3.20 (± 1.80) in group 1, 2 and 3, respectively (p < 0.01). Sternal mean SUVmean was 3.84 (± 1.00), 2.69 (± 1.32) and 1.71 (± 0.98) in group 1, 2 and 3 (p < 0.01). All patients in group 1 had elevated uptake whereas group 2 and 3 showed 2/15 (13%) and 11/20 (55%) patients respectively with no elevated uptake. Group 3 still showed an elevated uptake pattern in in 9/20 (45%) and in 3/9 (33%) with a high-grade diffuse uptake pattern.ConclusionThis study shows significant lower sternal 18F-FDG at 55 weeks compared to 5 weeks post-sternotomy however elevated uptake patterns may persist.
| S-EPMC10261398 | biostudies-literature
Project description:BackgroundConsidering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[18F]FDG).MethodsWe used 2-[18F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer's disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metabolism between the amyloid PET-positive and PET-negative participants. This facilitated an understanding of the deep model classification. In addition, we compared these regions with the classification probability from the submodules.ResultsThere were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803-0.819) and 0.798 (95% CI, 0.789-0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values.ConclusionThe proposed model based on the 2-[18F]FDG PET imaging data and a DL framework might successfully classify amyloid PET positivity in clinical practice, without performing amyloid PET, which have limited accessibility.
| S-EPMC8192639 | biostudies-literature
Project description:Several limitations of [18F]FDG have been reported, such as nonspecific uptake of inflammation foci. Moreover, [11C]MET has been found to accumulate in normal and inflammatory tissues as well as tumors. To increase specificity to tumor tissues, PET probes with tumor-specific molecular targets have been actively developed. [18F]FIMP was found to be highly accumulated in LAT1-positive tumors but not in inflamed tissue. The aim of this study was to explore whether [18F]FIMP can be used for the early-phase evaluation of radiotherapy accompanied by inflammation, and compare its effectiveness with those of [11C]MET and [18F]FDG. Tumor uptake of [18F]FIMP decreased at day 1 after irradiation, and remained low until day 14. Comparatively, that of [18F]FDG initially decreased at day 3 but was transiently elevated at day 7 and then decreased again at day 10. Decreased tumor uptake of [11C]MET was observed at day 10. In line with the uptake of [18F]FIMP, the ratio of Ki-67 immuno-positive cells in tumor tissues significantly decreased at day 1, 7, and 10 as compared with that in the control. These findings suggest that [18F]FIMP may be a PET probe involved in the early detection and prediction of radiotherapy efficacy, although further clarification is needed.
| S-EPMC9898523 | biostudies-literature
Project description:PurposeSecond-generation tau radiotracers for use with positron emission tomography (PET) have been developed for visualization of tau deposits in vivo. For several β-amyloid and first-generation tau-PET radiotracers, it has been shown that early-phase images can be used as a surrogate of neuronal injury. Therefore, we investigated the performance of early acquisitions of the novel tau-PET radiotracer [18F]PI-2620 as a potential substitute for [18F]fluorodeoxyglucose ([18F]FDG).MethodsTwenty-six subjects were referred with suspected tauopathies or overlapping parkinsonian syndromes (Alzheimer's disease, progressive supranuclear palsy, corticobasal syndrome, multi-system atrophy, Parkinson's disease, multi-system atrophy, Parkinson's disease, frontotemporal dementia) and received a dynamic [18F]PI-2620 tau-PET (0-60 min p.i.) and static [18F]FDG-PET (30-50 min p.i.). Regional standardized uptake value ratios of early-phase images (single frame SUVr) and the blood flow estimate (R1) of [18F]PI-2620-PET were correlated with corresponding quantification of [18F]FDG-PET (global mean/cerebellar normalization). Reduced tracer uptake in cortical target regions was also interpreted visually using 3-dimensional stereotactic surface projections by three more and three less experienced readers. Spearman rank correlation coefficients were calculated between early-phase [18F]PI-2620 tau-PET and [18F]FDG-PET images for all cortical regions and frequencies of disagreement between images were compared for both more and less experienced readers.ResultsHighest agreement with [18F]FDG-PET quantification was reached for [18F]PI-2620-PET acquisition from 0.5 to 2.5 min p.i. for global mean (lowest R = 0.69) and cerebellar scaling (lowest R = 0.63). Correlation coefficients (summed 0.5-2.5 min SUVr & R1) displayed strong agreement in all cortical target regions for global mean (RSUVr 0.76, RR1 = 0.77) and cerebellar normalization (RSUVr 0.68, RR1 = 0.68). Visual interpretation revealed high regional correlations between early-phase tau-PET and [18F]FDG-PET. There were no relevant differences between more and less experienced readers.ConclusionEarly-phase imaging of [18F]PI-2620 can serve as a surrogate biomarker for neuronal injury. Dynamic imaging or a dual time-point protocol for tau-PET imaging could supersede additional [18F]FDG-PET imaging by indexing both the distribution of tau and the extent of neuronal injury.
| S-EPMC7567714 | biostudies-literature
Project description:In Mild Cognitive Impairment (MCI), the study of brain metabolism, provided by 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) can be integrated with brain perfusion through pseudo-Continuous Arterial Spin Labeling Magnetic Resonance sequences (MR pCASL). Cortical hypometabolism identification generally relies on wide control group datasets; pCASL control groups are instead not publicly available yet, due to lack of standardization in the acquisition parameters. This study presents a quantitative pipeline to be applied to PET and pCASL data to coherently analyze metabolism and perfusion inside 16 matching cortical regions of interest (ROIs) derived from the AAL3 atlas. The PET line is tuned on 36 MCI patients and 107 healthy control subjects, to agree in identifying hypometabolic regions with clinical reference methods (visual analysis supported by a vendor tool and Statistical Parametric Mapping, SPM, with two parametrizations here identified as SPM-A and SPM-B). The analysis was conducted for each ROI separately. The proposed PET analysis pipeline obtained accuracy 78 % and Cohen's к 60 % vs visual analysis, accuracy 79 % and Cohen's к 58 % vs SPM-A, accuracy 77 % and Cohen's к 54 % vs SPM-B. Cohen's к resulted not significantly different from SPM-A and SPM-B Cohen's к when assuming visual analysis as reference method (p-value 0.61 and 0.31 respectively). Considering SPM-A as reference method, Cohen's к is not significantly different from SPM-B Cohen's к as well (p-value = 1.00). The complete PET-pCASL pipeline was then preliminarily applied on 5 MCI patients and metabolism-perfusion regional correlations were assessed. The proposed approach can be considered as a promising tool for PET-pCASL joint analyses in MCI, even in the absence of a pCASL control group, to perform metabolism-perfusion regional correlation studies, and to assess and compare perfusion in hypometabolic or normo-metabolic areas.
| S-EPMC10755331 | biostudies-literature
Project description:PurposeBrain 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG-PET) is widely used in the diagnostic workup of Alzheimer's disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [18F]FDG-PET baseline from the patient's own MRI, and showcase its applicability in detecting AD pathology.MethodsWe included [18F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities.ResultsThe model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects.ConclusionThis work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [18F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines.
| S-EPMC9749397 | biostudies-literature