Brain PET Imaging Research Articles

Validation of a data-driven motion-compensated PET brain image reconstruction algorithm in clinical patients using four radiotracers

PurposePatients with dementia symptoms often struggle to limit movements during PET examinations, necessitating motion compensation in brain PET imaging to ensure the high image quality needed for diagnostic accuracy. This study validates a data-driven motion-compensated (MoCo) PET brain image reconstruction algorithm that corrects head motion by integrating the detected motion frames and their associated rigid body transformations into the iterative image reconstruction. Validation was conducted using phantom scans, healthy volunteers, and clinical patients using four radiotracers with distinct tracer activity distributions.MethodsWe conducted technical validation experiments of the algorithm using Hoffman brain phantom scans during a series of controlled movements, followed by two blinded reader studies assessing image quality between standard images and MoCo images in 38 clinical patients receiving dementia scans with [18F]Fluorodeoxyglucose, [18F]N-(3-iodopro-2E-enyl)-2beta-carbomethoxy-3beta-(4’-methylphenyl)-nortropane, [18F]flutemetamol, and a research group comprising 25 elderly subjects scanned with [18F]fluoroethoxybenzovesamicol.ResultsThe Hoffman brain phantom study demonstrated the algorithm’s capability to detect and correct for even minimal movements, 1-mm translations and 1⁰ rotations, applied to the phantom. Within the clinical cohort, where standard images were deemed suboptimal or non-diagnostic, all MoCo images were classified as having acceptable diagnostic quality. In the research cohort, MoCo images consistently matched or surpassed the standard image quality even in cases with minimal head movement, and the MoCo algorithm never led to degraded image quality.ConclusionThe PET brain MoCo reconstruction algorithm was robust and worked well for four different tracers with markedly different uptake patterns. Moco images markedly improved the image quality for patients who were unable to lie still during a PET examination and obviated the need for any repeat scans. Thus, the method was clinically feasible and has the potential for improving diagnostic accuracy.

PET Imaging of Sphingosine-1-Phosphate Receptor 1 with [18F]TZ4877 in Nonhuman Primates.

The sphingosine-1-phosphate receptor-1 (S1PR1) is involved in regulating responses to neuroimmune stimuli. There is a need for S1PR1-specific radioligands with clinically suitable brain pharmcokinetic properties to complement existing radiotracers. This work evaluated a promising S1PR1 radiotracer, [18F]TZ4877, in nonhuman primates. [18F]TZ4877 was produced via nucleophilic substitution of tosylate precursor with K[18F]/F- followed by deprotection. Brain PET imaging data were acquired with a Focus220 scanner in two Macaca mulatta (6, 13 years old) for 120-180 min following bolus injection of 118-163 MBq [18F]TZ4877, with arterial blood sampling and metabolite analysis to measure the parent input function and plasma free fraction (fP). Each animal was scanned at baseline, 15-18 min after 0.047-0.063 mg/kg of the S1PR1 inhibitor ponesimod, 33 min after 0.4-0.8 mg/kg of the S1PR1-specific compound TZ82112, and 167-195 min after 1 ng/kg of the immune stimulus endotoxin. Kinetic analysis with metabolite-corrected input function was performed to estimate the free fraction corrected total distribution volume (VT/fP). Whole-body dosimetry scans were acquired in 2 animals (1M, 1F) with a Biograph Vision PET/CT System, and absorbed radiation dose estimates were calculated with OLINDA. [18F]TZ4877 exhibited fast kinetics that were described by the reversible 2-tissue compartment model. Baseline [18F]TZ4877 fP was low (<1%), and [18F]TZ4877 VT/fP values were 233-866 mL/cm3. TZ82112 dose-dependently reduced [18F]TZ4877 VT/fP, while ponesimod and endotoxin exhibited negligible effects on VT/fP, possibly due to scan timing relative to dosing. Dosimetry studies identified the critical organs of gallbladder (0.42 (M) and 0.31 (F) mSv/MBq) for anesthetized nonhuman primate. [18F]TZ4877 exhibits reversible kinetic properties, but the low fP value limits reproducible quantification with this radiotracer. S1PR1 is a compelling PET imaging target, and these data support pursuing alternative F-18 labeled radiotracers for potential future human studies.

18 F-FDG metabolic abnormalities precede perfusion and atrophic changes in diagnosis of early frontotemporal dementia: study from a tertiary care university hospital.

Diagnosis of early onset dementia is critical for initiating management. Although structural MRI is an established procedure for dementia evaluation, early cases may be missed. Neurodegenerative diseases lead to reductions in glucose consumption and grey matter volume loss. Our primary aim was to establish whether metabolic changes precede perfusion abnormalities in early cases of dementia especially, frontotemporal dementia (FTD). Secondly to study if cerebral atrophy using Pasquier visual rating scales can be used reliably to correlate with hypometabolism in this group of patients. A total of 56 patients (M:F = 39:17) with memory loss as per the DSM-5 diagnostic criteria were clinically and neurologically examined and referred for 18 F-Fluorodeoxyglucose (FDG) PET brain imaging. A few patients who had a prior (recent, <1 week) MR brain underwent brain 18 F-FDG PET-CT, and all others were considered for simultaneous 18 F-FDG PETMR imaging of brain. T2-weighted images were used to report Pasquier rating scales in all our patients as per recommendation. Cognitive assessments were analysed along with neuroimaging findings. Highest diagnostic performance was obtained with 18 F-FDG PET for identifying early FTD in our series of patients. Sensitivity, specificity and accuracy of FDG and arterial spin labeling (ASL) using simultaneous PETMR were found to be 96.34%/90.1%/89 : 53.57%/62.12%/78, respectively. Cerebral atrophy rated using Pasquier visual scales showed the lowest diagnostic performance. Our study showed that the earliest phase of cognitive decline was found to be associated with specific patterns of hypometabolism, even in the absence of atrophy, which are currently considered diagnostic biomarkers. Metabolic derangements indeed precede perfusion changes and cerebral atrophy in the setting of early dementia. Simultaneous FDG PETMR is being recommended as the investigation of choice for the evaluation of early FTD. This pilot study shows that FDG PET outperforms cognitive assessments and anatomical imaging modalities in early dementia evaluation. Although ASL can detect perfusion deficits in dementia, compared with FDG PET, its sensitivity and specificity for discerning early onset dementia from controls remain inferior to FDG PET. Pasquier scales, although easy to implement, cannot replace FDG PET metabolic findings, which start very early in the neuronal disease process.

[18F]SF51, a novel 18F-labeled PET radioligand for translocator protein 18kDa (TSPO) in brain, works well in monkeys but fails in humans

[18F]SF51 is a novel radioligand for imaging translocator protein 18 kDa (TSPO) that previously displayed excellent imaging properties in nonhuman primates. This study assessed its performance in human brain and its dosimetry. Seven healthy participants underwent brain PET imaging to measure TSPO binding using a two-tissue compartment model (2TCM) to calculate total distribution volume (VT). This cohort included two high-affinity binders (HABs), three mixed-affinity binders (MABs), and two low-affinity binders (LABs). Two other participants received whole-body scans to assess radiation exposure. Peak brain radioactivity reached a standardized uptake value (SUV) of 1.4 at 3 minutes post-injection, diminishing to 30% of peak by 120 minutes. The average VT for all genotype groups was notably low (<1 mL·cm−3), emphasizing the radioligand’s poor binding in brain. [18F]SF51 remained sensitive to the TSPO polymorphism in vivo, as shown by a two-fold difference in VT between HABs and LABs. VT stabilization by 80 minutes post-injection suggested minimal radiometabolite accumulation in brain. The average effective dose was 13.8 ± 0.9 µSv/MBq. Contrary to previously published animal data, [18F]SF51 showed low binding to human TSPO, with uptake remaining influenced by the rs6971 polymorphism. These findings highlight the challenges of developing TSPO radioligands and underscore the significant species differences that may influence translational outcomes. ClinicalTrials.gov identifier: NCT05564429; registered 10/03/2022

First Comprehensive Intranasal PET imaging of Insulin using the Aptar device in Nonhuman Primates

AbstractBackgroundInsulin resistance (IR) is associated with abnormal tau‐phosphorylation and IR markers in AD brain co‐localize with neurofibrillary tangles. One strategy to overcome brain IR is to increase brain insulin is via intranasal insulin (INI) administration using specialized intranasal devices that deliver insulin to the brain. Our recent INI vs. placebo‐controlled double‐blinded clinical trial in MCI/AD showed that INI improved CSF biomarkers and slowed symptom progression over the placebo group treated with one type of delivery device (with no benefit with another). While these studies highlight the potential of INI for therapeutic applications, methods are needed to verify the successful INI delivery with specific devices. Here we report our novel [68Ga]‐NOTA‐insulin‐based brain PET imaging and whole‐body dosimetry through intranasal route in healthy vervets using the Aptar Cartridge Pump System (CPS) device for the first time.MethodHuman‐grade insulin was chelated with NOTA and radiolabeled with [68Ga]. 0‐90min and 0‐3h PET scans were conducted for brain and whole‐body dosimetry in healthy vervets (n=4/sex, 9‐12 y) after intranasal administration (2 puffs per nostril) of [68Ga]‐NOTA‐insulin (25‐37 MBq) through the Aptar device. Blood glucose, oxygen, heartbeat, and temperature were collected throughout the scan. Standard uptake values (SUVs), time‐activity curves (TACs), and standard dosimetry organ doses were calculated.Result[68Ga]‐NOTA‐insulin radiochemistry was fully‐optimized: shorter reaction times (∼6‐7min), lower insulin mass (2‐4 units), high radiochemical purity (&gt;99%), and molar activity (108 GBq/µmol). No significant changes were observed in vitals recorded. PET/MRI images showed brain penetration; SUV whole‐brain=0.29±0.11, hippocampus=0.18±0.05, cortex=0.21±0.08, choroid plexus=0.38±0.11, and cerebellum=0.09±0.01. TACs showed that radioactivity peaked in brain within 15‐20min of radiotracer administration and washed out by 90 min. Dosimetry showed nasal cavity and eyes as critical organs and effective dose=0.11 mSv/MBq. No sex differences were seen in brain uptake or dosimetry parameters.ConclusionBrain PET and whole‐body dosimetry PET scans of [68Ga]‐NOTA‐insulin in vervets demonstrate brain penetration and favorable distribution of INI respectively. This preliminary data provides critical information for translating [68Ga]‐NOTA‐insulin PET with intranasal delivery to humans using the Aptar device. This strategy will create a new paradigm for understanding the INI in AD pathogenesis and advance novel therapeutic platforms.

Evaluating the Efficacy of CortexID Quantitative Analysis in Localization of the Epileptogenic Zone in Patients with Temporal Lobe Epilepsy.

There remains a critical need for precise localization of the epileptogenic foci in individuals with drug-resistant epilepsy (DRE). 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging can reveal hypometabolic regions during the interval between seizures in patients with epilepsy. However, visual-based qualitative analysis is time-consuming and strongly influenced by physician experience. CortexID Suite is a quantitative analysis software that helps to evaluate PET imaging of the human brain. Therefore, we aimed to evaluate the efficacy of CortexID quantitative analysis in the localization of the epileptogenic zone in patients with temporal lobe epilepsy (TLE). A total of 102 patients with epilepsy who underwent 18F-FDG-PET examinations were included in this retrospective study. The PET visual analysis was interpreted by two nuclear medicine physicians, and the quantitative analysis was performed automatically using CortexID analysis software. The assumed epileptogenic zone was evaluated comprehensively by two skilled neurologists in the preoperative assessment of epilepsy. The accuracy of epileptogenic zone localization in PET visual analysis was compared with that in CortexID quantitative analysis. The diagnostic threshold for the difference in the metabolic Z-score between the right and left sides of medial temporal lobe epilepsy (MTLE) was calculated as 0.87, and that for lateral temporal lobe epilepsy (LTLE) was 2.175. In patients with MTLE, the area under the curve (AUC) was 0.922 for PET visual analysis, 0.853 for CortexID quantitative analysis, and 0.971 for the combined diagnosis. In patients with LTLE, the AUC was 0.842 for PET visual analysis, 0.831 for CortexID quantitative analysis, and 0.897 for the combined diagnosis. These results indicate that the diagnostic efficacy of CortexID quantitative analysis is not inferior to PET visual analysis (p > 0.05), while combined analysis significantly increases diagnostic efficacy (p < 0.05). Among the 23 patients who underwent surgery, the sensitivity and specificity of PET visual analysis for localization were 95.4% and 66.7%, and the sensitivity and specificity of CortexID quantitative analysis were 100% and 50%. The diagnostic efficacy of CortexID quantitative analysis is comparable to PET visual analysis in the localization of the epileptogenic zone in patients with TLE. CortexID quantitative analysis combined with visual analysis can further improve the accuracy of epileptogenic zone localization.

Deep learning-based binary classification of beta-amyloid plaques using 18 F florapronol PET.

This study aimed to investigate a deep learning model to classify amyloid plaque deposition in the brain PET images of patients suspected of Alzheimer's disease. A retrospective study was conducted on patients who were suspected of having a mild cognitive impairment or dementia, and brain amyloid 18 F florapronol PET/computed tomography images were obtained from 2019 to 2022. Brain PET images were visually assessed by two nuclear medicine specialists, who classified them as either positive or negative. Image rotation was applied for data augmentation. The dataset was split into training and testing sets at a ratio of 8 : 2. For the convolutional neural network (CNN) analysis, stratified k-fold ( k = 5) cross-validation was applied using training set. Trained model was evaluated using testing set. A total of 175 patients were included in this study. The average age at the time of PET imaging was 70.4 ± 9.3 years and included 77 men and 98 women (44.0% and 56.0%, respectively). The visual assessment revealed positivity in 62 patients (35.4%) and negativity in 113 patients (64.6%). After stratified k-fold cross-validation, the CNN model showed an average accuracy of 0.917 ± 0.027. The model exhibited an accuracy of 0.914 and an area under the curve of 0.958 in the testing set. These findings affirm the model's high reliability in distinguishing between positive and negative cases. The study verifies the potential of the CNN model to classify amyloid positive and negative cases using brain PET images. This model may serve as a supplementary tool to enhance the accuracy of clinical diagnoses.

Optimization of reconstruction in quantitative brain PET images: Benefits from PSF modeling and correction of edge artifacts.

Modern PET reconstruction algorithms incorporate point-spread-function (PSF) correction to mitigate partial volume effect. However, PSF correction can introduce edge artifacts that lead to quantification errors. Consequently, current international guidelines advise against using PSF correction in brain PET reconstruction. We aimed to investigate PSF-induced quantification errors in recent digital PET systems and identify conditions that mitigate them. This study utilized brain PET imaging with alginate-based realistic phantoms, simulating lesion-to-background activity ratios of 10:1 and 2:1, with eleven reconstruction parameter sets. Phantoms were prepared using a commercial anthropomorphic head phantom and two homemade inserts. Each insert contained a homogeneous 18F-FDG alginate background with hot spheres of varying diameter (3, 4, 6, 8, 10, 12, and 15mm). PET imaging was conducted on a digital PET-CT system Biograph Vision 600 (Siemens), with a 10 min scan duration. Imaging was performed with and without PSF correction, with 2, 4, 6, 12, 18, or 24 iterations in reconstruction, and with or without additional Gaussian postfiltering. We assessed the recovery coefficient (RC), contrast recovery coefficient (CRC), variability, and CRC-to-variability ratios for each sphere size and reconstruction parameter set. PSF-corrected images of the 10:1 spheres exhibited a nonmonotonic CRC-to-sphere diameter relationship due to edge artifacts overshoot in the 10mm-diameter sphere. In contrast, PSF images of the 2:1 spheres showed a monotonically increasing relationship. Non-PSF images of both phantoms showed an expected monotonically increasing CRC-to-sphere diameter relationship but with lower CRC values compared to PSF images. The nonmonotonic relationship observed with 10:1 spheres was mitigated by applying a 3-mm FWHM Gaussian postfiltering. For both phantoms, reconstructions with 6 iterations, PSF correction, and additional 3-mm FWHM Gaussian postfiltering demonstrated the highest CRC-to-variability ratios. Our findings indicate that Gaussian postfiltering suppresses PSF artifacts. This parameter set corrected the nonmonotonic CRC-to-sphere diameter relationship and improved the CRC-to-variability ratio compared to non-PSF reconstructions. Therefore, to enhance lesion detectability without compromising quantification accuracy, PSF correction coupled with Gaussian postfiltering should be used in brain PET.

Accurate Whole-Brain Image Enhancement for Low-Dose Integrated PET/MR Imaging Through Spatial Brain Transformation.

Positron emission tomography/magnetic resonance imaging (PET/MRI) systems can provide precise anatomical and functional information with exceptional sensitivity and accuracy for neurological disorder detection. Nevertheless, the radiation exposure risks and economic costs of radiopharmaceuticals may pose significant burdens on patients. To mitigate image quality degradation during low-dose PET imaging, we proposed a novel 3D network equipped with a spatial brain transform (SBF) module for low-dose whole-brain PET and MR images to synthesize high-quality PET images. The FreeSurfer toolkit was applied to derive the spatial brain anatomical alignment information, which was then fused with low-dose PET and MR features through the SBF module. Moreover, several deep learning methods were employed as comparison measures to evaluate the model performance, with the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and Pearson correlation coefficient (PCC) serving as quantitative metrics. Both the visual results and quantitative results illustrated the effectiveness of our approach. The obtained PSNR and SSIM were 41.96 ± 4.91 dB (p < 0.01) and 0.9654 ± 0.0215 (p < 0.01), which achieved a 19% and 20% improvement, respectively, compared to the original low-dose brain PET images. The volume of interest (VOI) analysis of brain regions such as the left thalamus (PCC = 0.959) also showed that the proposed method could achieve a more accurate standardized uptake value (SUV) distribution while preserving the details of brain structures. In future works, we hope to apply our method to other multimodal systems, such as PET/CT, to assist clinical brain disease diagnosis and treatment.

Improving 18F-FDG PET Quantification Through a Spatial Normalization Method.

Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset's different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning-based brain PET spatial normalization techniques for various clinical and research radiotracers.

Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment.

The goal of this paper is to build an automatic way to interpret conclusions from brain molecular imaging reports performed for investigation of cognitive disturbances (FDG, Amyloid and Tau PET) by comparing several traditional machine learning (ML) techniques-based text classification methods. Two purposes are defined: to identify positive or negative results in all three modalities, and to extract diagnostic impressions for Alzheimer's Disease (AD), Fronto-Temporal Dementia (FTD), Lewy Bodies Dementia (LBD) based on metabolism of perfusion patterns. A dataset was created by manual parallel annotation of 1668 conclusions of reports from the Nuclear Medicine and Molecular Imaging Division of Geneva University Hospitals. The 6 Machine Learning (ML) algorithms (Support Vector Machine (Linear and Radial Basis function), Naive Bayes, Logistic Regression, Random Forrest, and K-Nearest Neighbors) were trained and evaluated with a 5-fold cross-validation scheme to assess their performance and generalizability. The best classifier was SVM showing the following accuracies: FDG (0.97), Tau (0.94), Amyloid (0.98), Oriented Diagnostic (0.87 for a diagnosis among AD, FTD, LBD, undetermined, other), paving the way for a paradigm shift in the field of data handling in nuclear medicine research.

Adaptive Strategies of Single-Sided Deaf Cochlear-Implant Users Revealed Through Resting State Activity: an Auditory PET Brain Imaging Study

Brain plasticity refers to the brain's ability to reorganize its structure or function in response to experiences, learning, and environmental influences. This phenomenon is particularly significant in individuals with deafness, as the brain adapts to compensate for the lack of auditory stimulation. The aim of this study is to investigate whether cochlear implantation can restore a normal pattern of brain activation following auditory stimulation in cases of asymmetric hearing loss. We used a PET-scan technique to assess brain activity after cochlear implantation, specifically during an auditory voice/non-voice discrimination task. The results indicated a nearly normal pattern of brain activity during the auditory discrimination task, except for increased activation in areas related to attentional processes compared to controls. Additionally, brain activity at rest showed significant changes in implanted participants, including cross modal visuo-auditory processing. Therefore, cochlear implants can restore the brain's activation pattern through long-term adaptive adjustments in intrinsic brain activity.

Development of a Radiolabeled Cyclin-Dependent Kinases 4 and 6 (CDK4/6) Inhibitor for Brain and Cancer PET Imaging.

The synthesis, biochemical evaluation and radiosynthesis of a cyclin-dependent kinases 4 and 6 (CDK4/6) inhibitor and radioligand was performed. NT431, a newly synthesized 4-fluorobenzyl-abemaciclib, exhibited high potency to CDK4/6 and against four cancer cell lines with IC50 similar to that of the parent abemaciclib. We performed a two-step one-pot radiosynthesis to produce [18F]NT431 with good radiochemical yield (9.6 ± 3%, n = 3, decay uncorrected), high radiochemical purity (>95%), and high molar activity (>370 GBq/µmol (>10.0 Ci/µmol). In vitro autoradiography confirmed the specific binding of [18F]NT431 to CDK4/6 in brain tissues. Dynamic PET imaging supports that both [18F]NT431 and the parent abemaciclib crossed the BBB albeit with modest brain uptake. Therefore, we conclude that it is unlikely that NT431 or abemaciclib (FDA approved drug) can accumulate in the brain in sufficient concentrations to be potentially effective against breast cancer brain metastases or brain cancers. However, despite the modest BBB penetration, [18F]NT431 represents an important step towards the development and evaluation of a new generation of CDK4/6 inhibitors with superior BBB penetration for the treatment and visualization of CDK4/6 positive tumors in the CNS. Also, [18F]NT431 may have potential application in peripheral tumors such as breast cancer and other CDK4/6 positive tumors.

AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.

PET Imaging of Sphingosine-1-Phosphate Receptor 1 with [18F]TZ4877 in Nonhuman Primates.

The sphingosine-1-phosphate receptor-1 (S1PR1) is involved in regulating responses to neuroimmune stimuli. There is a need for S1PR1-specific radioligands with clinically suitable brain pharmcokinetic properties to complement existing radiotracers. This work evaluated a promising S1PR1 radiotracer, [18F]TZ4877, in nonhuman primates. [18F]TZ4877 was produced via nucleophilic substitution of tosylate precursor with K[18F]/F- followed by deprotection. Brain PET imaging data were acquired with a Focus220 scanner in two Macaca mulatta (6, 13 years old) for 120-180 min following bolus injection of 118-163 MBq [18F]TZ4877, with arterial blood sampling and metabolite analysis to measure the parent input function and plasma free fraction (f P). Each animal was scanned at baseline, 15-18 min after 0.047-0.063 mg/kg of the S1PR1 inhibitor ponesimod, 33 min after 0.4-0.8 mg/kg of the S1PR1-specific compound TZ82112, and 167-195 min after 1 ng/kg of the immune stimulus endotoxin. Kinetic analysis with metabolite-corrected input function was performed to estimate the free fraction corrected total distribution volume (V T/f P). Whole-body dosimetry scans were acquired in 2 animals (1M, 1F) with a Biograph Vision PET/CT System, and absorbed radiation dose estimates were calculated with OLINDA. [18F]TZ4877 exhibited fast kinetics that were described by the reversible 2-tissue compartment model. Baseline [18F]TZ4877 f P was low (< 1%), and [18F]TZ4877 V T/f P values were 233-866 mL/cm3. TZ82112 dose-dependently reduced [18F]TZ4877 V T/f P, while ponesimod and endotoxin exhibited negligible effects on V T/f P, possibly due to scan timing relative to dosing. Dosimetry studies identified the critical organs of gallbladder (0.42 (M) and 0.31 (F) mSv/MBq) for anesthetized nonhuman primate. [18F]TZ4877 exhibits reversible kinetic properties, but the low f P value limits quantification with this radiotracer. S1PR1 is a compelling PET imaging target, and these data support pursuing alternative F-18 labeled radiotracers for potential future human studies.

Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method

PurposeThis study assesses the clinical performance of BTXBrain-Amyloid, an artificial intelligence-powered software for quantifying amyloid uptake in brain PET images.Methods150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted.ResultsBoth BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups.ConclusionBTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification

IntroductionSpatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images. MethodsWe propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR). ResultsIn total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05). ConclusionThe automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

11C-CFT PET brain imaging in Parkinson’s disease using a total-body PET/CT scanner

PurposeThis study aimed to evaluate the feasibility of 11C-CFT PET brain imaging in Parkinson’s Disease using a total-body PET/CT scanner and explore the optimal scan duration to guide the clinical practice.MethodsThirty-two patients with Parkinson’s disease (PD) performing 11C-CFT PET/CT brain imaging using a total-body PET/CT scanner were retrospectively enrolled. The PET data acquired over a period of 900 s were reconstructed into groups of different durations: 900-s, 720-s, 600-s, 480-s, 300-s, 180-s, 120-s, 60-s, and 30-s (G900 to G30). The subjective image quality analysis was performed using 5-point scales. Semi-quantitative measurements were analyzed by SUVmean and dopamine transporter (DAT) binding of key brain regions implicated in PD, including the caudate nucleus and putamen. The full-time images (G900) were served as reference.ResultsThe overall G900, G720, and G600 image quality scores were 5.0 ± 0.0, 5.0 ± 0.0, and 4.9 ± 0.3 points, respectively, and there was no significant difference among these groups (P > 0.05). A significant decrease in these scores at durations shorter than 600 s was observed when compared to G900 images (P < 0.05). However, all G300 image quality was clinically acceptable (≥ 3 points). As the scan duration reduced, the SUVmean and DAT binding of caudate nucleus and putamen decreased progressively, while there were no statistically significant variations in the SUVmean of the background among the different groups. Moreover, the changes in the lesion DAT binding (ΔDAT-binding) between the full-time reference G900 image and other reconstructed group G720 to G30 images generally increased along with the reduced scan time.ConclusionSufficient image quality and lesion conspicuity could be achieved at 600-s scan duration for 11C-CFT PET brain imaging in PD assessment using a total-body PET/CT scanner, while the image quality of G300 was acceptable to meet clinical diagnosis, contributing to improve patient compliance and throughput of PET brain imaging.

Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images

PurposeBayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging.MethodsImages were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [18F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid β deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR).ResultsThe overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing β values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal β value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91–0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods.ConclusionGibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [18F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [18F]flutemetamol amyloid PET imaging.

Metabolite Study and Structural Authentication for the First-in-Human Use Sphingosine-1-phosphate Receptor 1 Radiotracer.

The sphingosine-1-phosphate receptor 1 (S1PR1) radiotracer [11C]CS1P1 has shown promise in proof-of-concept PET imaging of neuroinflammation in multiple sclerosis (MS). Our HPLC radiometabolite analysis of human plasma samples collected during PET scans with [11C]CS1P1 detected a radiometabolite peak that is more lipophilic than [11C]CS1P1. Radiolabeled metabolites that cross the blood-brain barrier complicate quantitative modeling of neuroimaging tracers; thus, characterizing such radiometabolites is important. Here, we report our detailed investigation of the metabolite profile of [11C]CS1P1 in rats, nonhuman primates, and humans. CS1P1 is a fluorine-containing ligand that we labeled with C-11 or F-18 for preclinical studies; the brain uptake was similar for both radiotracers. The same lipophilic radiometabolite found in human studies also was observed in plasma samples of rats and NHPs for CS1P1 labeled with either C-11 or F-18. We characterized the metabolite in detail using rats after injection of the nonradioactive CS1P1. To authenticate the molecular structure of this radiometabolite, we injected rats with 8 mg/kg of CS1P1 to collect plasma for solvent extraction and HPLC injection, followed by LC/MS analysis of the same metabolite. The LC/MS data indicated in vivo mono-oxidation of CS1P1 produces the metabolite. Subsequently, we synthesized three different mono-oxidized derivatives of CS1P1 for further investigation. Comparing the retention times of the mono-oxidized derivatives with the metabolite observed in rats injected with CS1P1 identified the metabolite as N-oxide 1, also named TZ82121. The MS fragmentation pattern of N-oxide 1 also matched that of the major metabolite in rat plasma. To confirm that metabolite TZ82121 does not enter the brain, we radiosynthesized [18F]TZ82121 by the oxidation of [18F]FS1P1. Radio-HPLC analysis confirmed that [18F]TZ82121 matched the radiometabolite observed in rat plasma post injection of [18F]FS1P1. Furthermore, the acute biodistribution study in SD rats and PET brain imaging in a nonhuman primate showed that [18F]TZ82121 does not enter the rat or nonhuman primate brain. Consequently, we concluded that the major lipophilic radiometabolite N-oxide [11C]TZ82121, detected in human plasma post injection of [11C]CS1P1, does not enter the brain to confound quantitative PET data analysis. [11C]CS1P1 is a promising S1PR1 radiotracer for detecting S1PR1 expression in the CNS.