Brain PET Imaging Research Articles

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 18F 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 18F 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.

Impact of improved dead time correction on the quantification accuracy of a dedicated BrainPET scanner.

Quantitative values derived from PET brain images are of high interest for neuroscientific applications. Insufficient DT correction (DTC) can lead to a systematic bias of the output parameters obtained by a detailed analysis of the time activity curves (TACs). The DTC method currently used for the Siemens 3T MR BrainPET insert is global, i.e., differences in DT losses between detector blocks are not considered, leading to inaccurate DTC and, consequently, to inaccurate measurements masked by a bias. However, following careful evaluation with phantom measurements, a new block-pairwise DTC method has demonstrated a higher degree of accuracy compared to the global DTC method. Differences between the global and the block-pairwise DTC method were studied in this work by applying several radioactive tracers. We evaluated the impact on [11C]ABP688, O-(2-[18F]fluoroethyl)-L-tyrosine (FET), and [15O]H2O TACs. For [11C]ABP688, a relevant bias of between -0.0034 and -0.0053 ml/ (cm3 • min) was found in all studied brain regions for the volume of distribution (VT) when using the current global DTC method. For [18F]FET-PET, differences of up to 10% were observed in the tumor-to-brain ratio (TBRmax), these differences depend on the radial distance of the maximum from the PET isocenter. For [15O]H2O, differences between +4% and -7% were observed in the GM region. Average biases of -4.58%, -3.2%, and -1.2% for the regional cerebral blood flow (CBF (K1)), the rate constant k2, and the volume of distribution VT were observed, respectively. Conversely, in the white matter region, average biases of -4.9%, -7.0%, and 3.8% were observed for CBF (K1), k2, and VT, respectively. The bias introduced by the global DTC method leads to an overestimation in the studied quantitative parameters for all applications compared to the block-pairwise method. The observed differences between the two DTC methods are particularly relevant for research applications in neuroscientific studies as they affect the accuracy of quantitative Brain PET images.

Cross-Scanner Low-Dose Brain-PET Image Noise Reduction With Self-Ensembling

Cross-Scanner Low-Dose Brain-PET Image Noise Reduction With Self-Ensembling

Investigation of imaging the somatostatin receptor by opening the blood-brain barrier with melittin – A feasibility study using positron emission tomography and [64Cu]Cu-DOTATATE

DOTATATE is a somatostatin peptide analog used in the clinic to detect somatostatin receptors which are highly expressed on neuroendocrine tumors. Somatostatin receptors are found naturally in the intestines, pancreas, lungs, and brain (mainly cortex). In vivo measurement of the somatostatin receptors in the cortex has been challenging because available tracers cannot cross the blood-brain barrier (BBB) due to their intrinsic polarity. A peptide called melittin, a main component of honeybee venom, has been shown to disrupt plasma membranes and increase the permeability of biological membranes. In this study, we assessed the feasibility of using melittin to facilitate the passage of [64Cu]Cu-DOTATATE through the BBB and its binding to somatostatin receptors in the cortex. Evaluation included in vitro autoradiography on Long Evans rat brains to estimate the binding affinity of [64Cu]Cu-DOTATATE to the somatostatin receptors in the cortex and an in vivo evaluation of [64Cu]Cu-DOTATATE binding in NMRI mice after injection of melittin. This study found an in vitro Bmax = 89 ± 4 nM and KD = 4.5 ± 0.6 nM in the cortex, resulting in a theoretical binding potential (BP) calculated as Bmax/KD ≈ 20, which is believed suitable for in vivo brain PET imaging. However, the in vivo results showed no significant difference between the control and melittin injected mice, indicating that the honeybee venom failed to open the BBB. Additional experiments, potentially involving faster injection rates are required to verify that melittin can increase brain uptake of non-BBB permeable PET tracers. Furthermore, an evaluation of whether a venom with a narrow therapeutic range can be used for clinical purposes needs to be considered.

Synthesis and evaluation of fluorine-18 labelled tetrazines as pre-targeting imaging agents for PET

BackgroundThe brain is a challenging target for antibody-based positron emission tomography (immunoPET) imaging due to the restricted access of antibody-based ligands through the blood–brain barrier (BBB). To overcome this physiological obstacle, we have previously developed bispecific antibody ligands that pass through the BBB via receptor-mediated transcytosis. While these radiolabelled ligands have high affinity and specificity, their long residence time in the blood and brain, typical for large molecules, poses another challenge for PET imaging. A viable solution could be a two-step pre-targeting approach which involves the administration of a tagged antibody that accumulates at the target site in the brain and then clears from the blood, followed by administration of a small radiolabelled molecule with fast kinetics. This radiolabelled molecule can couple to the tagged antibody and thereby make the antibody localisation visible by PET imaging. The in vivo linkage can be achieved by using the inverse electron demand Diels–Alder reaction (IEDDA), with trans-cyclooctene (TCO) and tetrazine groups participating as reactants. In this study, two novel 18F-labelled tetrazines were synthesized and evaluated for their potential use as pre-targeting imaging agents, i.e., for their ability to rapidly enter the brain and, if unbound, to be efficiently cleared with minimal background retention.ResultsThe two compounds, a methyl tetrazine [18F]MeTz and an H-tetrazine [18F]HTz were radiolabelled using a two-step procedure via [18F]F-Py-TFP synthesized on solid support followed by amidation with amine-bearing tetrazines, resulting in radiochemical yields of 24% and 22%, respectively, and a radiochemical purity of > 96%. In vivo PET imaging was performed to assess their suitability for in vivo pre-targeting. Time-activity curves from PET-scans showed [18F]MeTz to be the more pharmacokinetically suitable agent, given its fast and homogenous distribution in the brain and rapid clearance. However, in terms of rection kinetics, H-tetrazines are advantageous, exhibiting faster reaction rates in IEDDA reactions with dienophiles like trans-cyclooctenes, making [18F]HTz potentially more beneficial for pre-targeting applications.ConclusionThis study demonstrates a significant potential of [18F]MeTz and [18F]HTz as agents for pre-targeted PET brain imaging due to their efficient brain uptake, swift clearance and appropriate chemical stability.

Performance Evaluation of the uMI Panorama PET/CT System in Accordance with the National Electrical Manufacturers Association NU 2-2018 Standard.

The uMI Panorama is a novel PET/CT system using silicon photomultiplier and application-specific integrated circuit technologies and providing exceptional spatial and time-of-flight (TOF) resolutions. The objective of this study was to assess the physical performance of the uMI Panorama in accordance with the National Electrical Manufacturers Association (NEMA) NU 2-2018 standard. Methods: Spatial resolution, sensitivity, count rate performance, accuracy, image quality, and TOF resolution were evaluated in accordance with the guidelines outlined in the NEMA NU 2-2018 standard. Energy resolution was determined using the same dataset acquired for the count rate performance evaluation. Images from a Hoffman brain phantom, a mini-Derenzo phantom, and 3 patient studies were evaluated to demonstrate system performance. Results: The transaxial spatial resolution at full width at half maximum was measured as 2.88 mm with a 1-cm offset from the center axial field of view. The sensitivity at the center axial field of view was 20.1 kcps/MBq. At an activity concentration of 73.0 kBq/mL, the peak noise-equivalent count rate (NECR) reached 576 kcps with a scatter fraction of approximately 33.2%. For activity concentrations at or below the peak NECR, the maximum relative count rate error among all slices remained consistently below 3%. When assessed using the NEMA image quality phantom, overall image contrast recovery ranged from 63.2% to 88.4%, whereas background variability ranged from 4.2% to 1.1%. TOF resolution was 189 ps at 5.3 kBq/mL and was consistently lower than 200 ps for activity concentrations at or below the peak NECR. The patient studies demonstrated that scans at 2 min/bed produced images characterized by low noise and high contrast. Clear delineation of nuclei, spinal cords, and other substructures of the brain was observed in the brain PET images. Conclusion: uMI Panorama, the world's first commercial PET system with sub-200-ps TOF resolution, demonstrated fine spatial and fast TOF resolutions, robust count rate performance, and high quantification accuracy across a wide range of activity levels. This advanced technology offers enhanced diagnostic capability for detecting small and low-contrast lesions while showing promising potential under high-count-rate imaging scenarios.