Static Positron Emission Tomography Research Articles

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components

Objective. Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging. Approach. The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyer et al (2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system. Main results. The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods. Significance. This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

PET Imaging of Neurofibromatosis Type 1 with a Fluorine-18 Labeled Tryptophan Radiotracer.

Neurofibromatosis type 1 (NF1) is a neurocutaneous disorder. Plexiform neurofibromas (PNFs) are benign tumors commonly formed in patients with NF1. PNFs have a high incidence of developing into malignant peripheral nerve sheath tumors (MPNSTs) with a 5-year survival rate of only 30%. Therefore, the accurate diagnosis and differentiation of MPNSTs from benign PNFs are critical to patient management. We studied a fluorine-18 labeled tryptophan positron emission tomography (PET) radiotracer, 1-(2-[18F]fluoroethyl)-L-tryptophan (L-[18F]FETrp), to detect NF1-associated tumors in an animal model. An ex vivo biodistribution study of L-[18F]FETrp showed a similar tracer distribution and kinetics between the wild-type and triple mutant mice with the highest uptake in the pancreas. Bone uptake was stable. Brain uptake was low during the 90-min uptake period. Static PET imaging at 60 min post-injection showed L-[18F]FETrp had a comparable tumor uptake with [1⁸F]fluorodeoxyglucose (FDG). However, L-[18F]FETrp showed a significantly higher tumor-to-brain ratio than FDG (n = 4, p < 0.05). Sixty-minute-long dynamic PET scans using the two radiotracers showed similar kidney, liver, and lung kinetics. A dysregulated tryptophan metabolism in NF1 mice was further confirmed using immunohistostaining. L-[18F]FETrp is warranted to further investigate differentiating malignant NF1 tumors from benign PNFs. The study may reveal the tryptophan-kynurenine pathway as a therapeutic target for treating NF1.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

Full kinetic modeling analysis of [18F]fluorocholine Positron Emission Tomography (PET) at initial diagnosis of high-grade glioma

PurposeThe main objective was to characterize the tracer uptake kinetics of [18F]fluoromethylcholine ([18F]F-CHO) in high-grade gliomas (HGG) through a full PET kinetic modeling approach. Secondarily, we aimed to explore the relationship between the PET uptake measures and the HGG molecular features. Materials and methodsTwenty-four patients with a suspected diagnosis of HGG were prospectively included. They underwent a dynamic brain [18F]F-CHO-PET/CT, from which a tumoral time-activity curve was extracted. The plasma input function was obtained through arterial blood sampling with metabolite correction. These data were fitted to 1- and 2-tissue-compartment models, the best of which was selected through the Akaike information criterion. We assessed the correlation between the kinetic parameters and the conventional static PET metrics (SUVmax, SUVmean and tumor-to-background ratio TBR). We explored the association between the [18F]F-CHO-PET quantitative parameters and relevant molecular biomarkers in HGG. ResultsTumoral time-activity curves in all patients showed a rapid rise of [18F]F-CHO uptake followed by a plateau-like shape. Best fits were obtained with near-irreversible 2-tissue-compartment models. The perfusion-transport constant K1 and the net influx rate Ki showed strong correlation with SUVmax (r = 0.808–0.861), SUVmean (r = 0.794–0.851) and TBR (r = 0.643–0.784), p < 0.002. HGG was confirmed in 21 patients, of which those with methylation of the O-6-methylguanine-DNA methyltransferase (MGMT) gene promoter showed higher mean Ki (p = 0.020), K1 (p = 0.025) and TBR (p = 0.001) than the unmethylated ones. Conclusion[18F]F-CHO uptake kinetics in HGG is best explained by a 2-tissue-compartment model. The conventional static [18F]F-CHO-PET measures have been validated against the perfusion-transport constant (K1) and the net influx rate (Ki) derived from kinetic modeling. A relationship between [18F]F-CHO uptake rate and MGMT methylation is suggested but needs further confirmation.

NIMG-33. [18F]-FLUOROMISONIDAZOLE RELATIVE PET INFLUX RATE IMPROVES DIAGNOSTIC ACCURACY FOR GLIOBLASTOMA PROGRESSION FROM THERAPY-INDUCED NEUROINFLAMMATORY PSEUDOPROGRESSION

Abstract Glioblastoma is the most common and aggressive form of primary brain tumor in adults. Standard of care gadolinium-enhanced MRI (Gd-MRI) fails to capture hypoxia, a disease-defining feature of glioblastoma. Hypoxia potentiates resistance to chemo-radiation therapy (CRT) but is not extensively observed with therapy-induced pseudoprogression (PSP). This neuroinflammatory response to CRT occurs in approximately 40% of patients and is indistinguishable from disease progression by Gd-MRI, potentially causing unnecessary termination of effective therapy in patients with PSP. Additionally, delayed second line-therapy can occur in patients with resistance to CRT. This research evaluated whether [18F]-fluoromisonidazole (FMISO) positron emission tomography (PET), a noninvasive metric of tissue hypoxia, improves diagnostic accuracy in this context. Clinically, PET quantification employs the standardized uptake value (SUV), representing summed counts over an acquisition period. To add accuracy to this assessment, static 40-minute PET data acquired starting 90 minutes after FMISO injection was reconstructed into 20x2-minute frames and a relative Patlak model was applied. This technique forgoes blood sampling and extensive examination times required by traditional dynamic PET studies. The model produces two parameters that separately characterize the FMISO relative influx rate (Ki') and blood volume (VB'). In a cohort of 16 patients (3 IDH-mutated) imaged at a time of presumed disease progression, results showed that Ki' within the Gd-MRI enhancing lesion predicts future diagnosis of true progression (n = 11) or inflammation (n =5 ) with a sensitivity and specificity of 91% and 80% respectively. This outperforms diagnosis made with Gd-MRI alone, achieving ~70% for the same metrics. A t-test assuming unequal variance of cohort-wide mean Ki' tended toward significance (p = 0.07) for differentiating progressive disease (0.0036 ± 0.0016 min-1) from PSP (0.0011 ± 0.0017 min-1). RESULTS: from this study suggest relative Patlak analysis adds specificity to Gd-MRI and provides clinically relevant information regarding disease status.

Glucose uptake and distribution across the human skeleton using state-of-the-art total-body PET/CT

A growing number of studies have demonstrated that the skeleton is an endocrine organ that is involved in glucose metabolism and plays a significant role in human glucose homeostasis. However, there is still a limited understanding of the in vivo glucose uptake and distribution across the human skeleton. To address this issue, we aimed to elucidate the detailed profile of glucose uptake across the skeleton using a total-body positron emission tomography (PET) scanner. A total of 41 healthy participants were recruited. Two of them received a 1-hour dynamic total-body 18F-fluorodeoxyglucose (18F-FDG) PET scan, and all of them received a 10-minute static total-body 18F-FDG PET scan. The net influx rate (Ki) and standardized uptake value normalized by lean body mass (SUL) were calculated as indicators of glucose uptake from the dynamic and static PET data, respectively. The results showed that the vertebrae, hip bone and skull had relatively high Ki and SUL values compared with metabolic organs such as the liver. Both the Ki and SUL were higher in the epiphyseal, metaphyseal and cortical regions of long bones. Moreover, trends associated with age and overweight with glucose uptake (SULmax and SULmean) in bones were uncovered. Overall, these results indicate that the skeleton is a site with significant glucose uptake, and skeletal glucose uptake can be affected by age and dysregulated metabolism.

Spatiotemporal Learning of Dynamic Positron Emission Tomography Data Improves Diagnostic Accuracy in Breast Cancer

Positron emission tomography (PET) is a noninvasive imaging technology able to assess the metabolic or functional state of healthy and/or pathological tissues. In clinical practice, PET data are usually acquired statically and normalized for the evaluation of the standardized uptake value (SUV). In contrast, dynamic PET acquisitions provide information about radiotracer delivery to tissue, its interaction with the target and its physiological washout. The shape of the time activity curves (TACs) embeds tissue-specific biochemical properties. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function, and tracer-specific compartmental models to obtain a full quantitative analysis of PET data. This method’s primary disadvantage is the requirement for invasive arterial blood sample collection throughout the whole PET scan. In this study, we employ a variety of deep learning models to illustrate the diagnostic potential of dynamic PET acquisitions of varying lengths for discriminating breast cancer lesions in the absence of arterial blood sampling compared to static PET only. Our findings demonstrate that the use of TACs, even in the absence of arterial blood sampling and even when using only a share of all timeframes available, outperforms the discriminative ability of conventional SUV analysis.

Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction.

Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies.

Diagnostic value of dynamic 18F-FDG PET/CT imaging in non-small cell lung cancer and FDG hypermetabolic lymph nodes.

We aimed to investigate the diagnostic value of dynamic 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography in non-small cell lung cancer and fluoro-D-glucose hypermetabolic lymph nodes. Patients who made an active appointment for positron emission tomography-computed tomography were randomly enrolled by referring to previous imaging data and clinical information. Finally, 34 histopathologically confirmed non-small cell lung cancers (18 adenocarcinoma and 16 squamous cell carcinoma cases) were prospectively studied using dynamic and static 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography imaging (the diagnostic study has not yet been registered on a clinical trial platform). In dynamic positron emission tomography images, a volume of interest, defined by the thoracic aorta, was selected for estimating the arterial input function. Patlak and irreversible two-tissue compartment model analyses were performed based on the pixel points to obtain first-order characteristic kinetic parameters for each lesion and hypermetabolic lymph node. The first-order characteristic kinetic parameters were obtained based on the basic data of dynamic positron emission tomography images in the corresponding model and the lesion delineation of region-of-interest based on computed tomography images, such as V_Median (the median gray intensity of V), k3_Entropy, VB_Entropy, K1_Uniformity, and ki_Uniformity. The first-order characteristic kinetic parameters were also modeled by logistic regression for the differential diagnosis of non-small cell lung cancer and hypermetabolic lymph nodes. Maximum and mean standard uptake values (SUVmax and SUVmean, respectively) were obtained from static positron emission tomography images. The diagnostic efficacy of the parameters was evaluated using the receiver operating characteristic curve and the DeLong test. There was a significant difference in the V_Median values of adenocarcinoma and squamous cell carcinoma. The regression models for K1, k3, and V provided good predictions of adenocarcinoma and squamous cell carcinoma typology. Significant differences were observed in k3_Entropy, VB_Entropy, K1_Uniformity, and ki_Uniformity between benign and malignant lymph nodes. The regression model of Ki, VB, and k3 could make a good prediction for identifying benign and malignant lymph nodes. Dynamic 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography imaging showed high diagnostic value in the staging of non-small cell lung cancer and fluoro-D-glucose hypermetabolic lymph nodes, and can be of great use in non-small cell lung cancer lymph node staging and surgical decision-making.

Novel Approach for Assessing Postinfarct Myocardial Injury and Inflammation Using Hybrid Somatostatin Receptor Positron Emission Tomography/Magnetic Resonance Imaging.

Novel Approach for Assessing Postinfarct Myocardial Injury and Inflammation Using Hybrid Somatostatin Receptor Positron Emission Tomography/Magnetic Resonance Imaging.

Increased uptake of the P2X7 receptor radiotracer 18 F-JNJ-64413739 in the brain and peripheral organs according to the severity of status epilepticus in male mice.

The P2X7 receptor (P2X7R) is an important contributor to neuroinflammation, responding to extracellularly released adenosine triphosphate. Expression of the P2X7R is increased in the brain in experimental and human epilepsy, and genetic or pharmacologic targeting of the receptor can reduce seizure frequency and severity in preclinical models. Experimentally induced seizures also increase levels of the P2X7R in blood. Here, we tested 18 F-JNJ-64413739, a positron emission tomography (PET) P2X7R antagonist, as a potential noninvasive biomarker of seizure-damage and epileptogenesis. Status epilepticus was induced via an intra-amygdala microinjection of kainic acid. Static PET studies (30 min duration, initiated 30 min after tracer administration) were conducted 48 h after status epilepticus via an intravenous injection of 18 F-JNJ-64413739. PET images were coregistered with a brain magnetic resonance imaging atlas, tracer uptake was determined in the different brain regions and peripheral organs, and values were correlated to seizure severity during status epilepticus. 18 F-JNJ-64413739 was also applied to ex vivo human brain slices obtained following surgical resection for intractable temporal lobe epilepsy. P2X7R radiotracer uptake correlated strongly with seizure severity during status epilepticus in brain structures including the cerebellum and ipsi- and contralateral cortex, hippocampus, striatum, and thalamus. In addition, a correlation between radiotracer uptake and seizure severity was also evident in peripheral organs such as the heart and the liver. Finally, P2X7R radiotracer uptake was found elevated in brain sections from patients with temporal lobe epilepsy when compared to control. Taken together, our data suggest that P2X7R-based PET imaging may help to identify seizure-induced neuropathology and temporal lobe epilepsy patients with increased P2X7R levels possibly benefitting from P2X7R-based treatments.

Intramuscular [18F]F-FDG Administration for Successful PET Imaging of Golden Hamsters in a Maximum Containment Laboratory Setting.

Positron emission tomography (PET) is becoming an important tool for the investigation of emerging infectious diseases in animal models. Usually, PET imaging is performed after intravenous (IV) radiotracer administration. However, IV injections are difficult to perform in some small animals, such as golden hamsters. This challenge is particularly evident in longitudinal imaging studies, and even more so in maximum containment settings used to study high-consequence pathogens. We propose the use of intramuscular (IM) administration of 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) for PET imaging of hamsters in a biosafety level 4 (BSL-4) laboratory setting. After [18F]F-FDG administration via IM or IV (through surgically implanted vascular access ports), eight hamsters underwent static or dynamic PET scans. Time-activity curves (TACs) and standardized uptake values (SUVs) in major regions of interest (ROIs) were used to compare the two injection routes. Immediately after injection, TACs differed between the two routes. At 60 min post-injection, [18F]F-FDG activity for both routes reached a plateau in most ROIs except the brain, with higher accumulation in the liver, lungs, brain, and nasal cavities observed in the IM group. IM delivery of [18F]F-FDG is an easy, safe, and reliable alternative for longitudinal PET imaging of hamsters in a BSL-4 laboratory setting.

Respiratory-gated 18F Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging in Evaluation of Primary Gastric Lesions and Gastric Lymph Nodes in Patients with Gastric Cancer

AimsTo evaluate the added value of respiratory-gated positron emission tomography (PET) in 18F fluorodeoxyglucose (FDG) PET/magnetic resonance imaging (MRI) in the visual and semi-quantitative assessment of primary gastric lesions and gastric lymph nodes for patients with gastric cancer. Materials and methodsIn total, 102 upper abdominal respiratory-gated and whole-body 18F FDG PET/MRI of 88 patients with gastric cancer were evaluated visually and semi-quantitatively. For 41 patients who underwent surgery, histopathological and PET findings were compared. Three PET images were obtained from upper abdominal PET data: non-Q static (non-QS) PET from all counts, respiratory-gated Q static (QS) PET from counts in the end-expiration phase of breathing, shortened 4 min (S4min) PET that was reconstructed to obtain similar counts to QS PET. The semi-quantitative parameters (standardised uptake values, metabolic tumour volume, total lesion glycolysis) of primary lesions for each PET image, the sizes of primary lesions and the patient's body mass index were recorded. According to lymph node stations, the presence and numbers of positive lymph nodes and visual scores of lymph nodes for each PET image were recorded. ResultsThe patients with smaller gastric lesions (≤30 mm) or higher body mass index (>30) had significantly higher standardised uptake value percentage changes in QS PET compared with non-QS PET (all P < 0.05). The third (lesser curvature), fourth (greater curvature) and sixth (infra-pyloric) lymph node stations had significantly higher visual scores in the QS PET than in the others. The fourth lymph node station had a significantly higher number of FDG-positive lymph node in the QS PET than in the non-QS and the whole-body PET images. In the fourth station, sensitivity, positive predictive value, negative predictive value and accuracy increased in the QS PET compared with the others. ConclusionRespiratory-gated PET/MRI was found to be significantly superior in the evaluation of especially the fourth lymph node station, smaller gastric lesions and in the patients with a higher BMI compared with the non-respiratory-gated PET images.

Virtual high-count PET image generation using a deep learning method.

Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PETimages. The training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHCimages. The cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significantdifference. A DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinicaldiagnosis.

Immuno-PET imaging of PD-L1 expression in patient-derived lung cancer xenografts with [68Ga]Ga-NOTA-Nb109.

Accurate evaluation of programmed death-ligand 1 (PD-L1) expression levels in cancer patients may be useful in the identification of potential candidates for anti-programmed death-1/PD-L1 (anti-PD-1/PD-L1) immune checkpoint therapy to improve the response rate of immune checkpoint blockade therapy. This study evaluated the feasibility of the nanobody-based positron emission tomography (PET) tracer [68Ga]Ga-NOTA-Nb109 for immuno-PET imaging of PD-L1 in lung cancer patient-derived xenograft (PDX). We constructed 2 PDXs of lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SCC) and used them for immuno-PET imaging. A 2-hour dynamic PET scanning was performed on the samples and the in vivo biodistribution and metabolism of [68Ga]Ga-NOTA-Nb109 were investigated using region of interest (ROI) analysis. The ex vivo biodistribution of [68Ga]Ga-NOTA-Nb109 in the 2 PDXs was investigated by static PET scanning. In addition, tumor PD-L1 expression in the 2 PDXs was evaluated by autoradiography, western blot, and immunohistochemical (IHC) analysis. Noninvasive PET imaging showed that [68Ga]Ga-NOTA-Nb109 can accurately and sensitively assess the PD-L1 expression in non-small cell lung cancer (NSCLC) PDX models. The maximum [68Ga]Ga-NOTA-Nb109 uptake by the ADC PDX LU6424 and the SCC PDX LU6437 were 3.13%±0.35% and 2.60%±0.32% injected dose per milliliter of tissue volume (ID/mL), respectively, at 20 min post injection. In vivo and ex vivo biodistribution analysis showed that [68Ga]Ga-NOTA-Nb109 was rapidly cleared through renal excretion and an enhanced signal-to-noise ratio (SNR) was achieved. Ex vivo PD-L1 expression analysis showed good agreement with in vivo PET imaging results. This study demonstrated that [68Ga]Ga-NOTA-Nb109 could be applied with PET imaging to noninvasively and accurately monitor PD-L1 expression in vivo for screening patients who may be responsive to immunotherapy and to guide the development of appropriate treatment strategies for such patients.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging for Resting-State Functional and Metabolic Imaging in Human Brain: What Is Correlated and What Is Impacted.

Integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) could simultaneously obtain both functional MRI (fMRI) and 18F-fluorodeoxyglucose (FDG) PET and thus provide multiparametric information for the analysis of brain metabolism. In this study, we aimed to, for the first time, investigate the interplay of simultaneous fMRI and FDG PET scan using a randomized self-control protocol. In total, 24 healthy volunteers underwent PET/MRI scan for 30–40 min after the injection of FDG. A 22-min brain scan was separated into MRI-off mode (without fMRI pulsing) and MRI-on mode (with fMRI pulsing), with each one lasting for 11 min. We calculated the voxel-wise fMRI metrics (regional homogeneity, amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and degree centrality), resting networks, relative standardized uptake value ratios (SUVr), SUVr slope, and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, SUVr slope, correlation coefficients of fMRI metrics, and rCMRGlu between MRI-off and MRI-on modes, respectively. The voxel-wise whole-brain SUVr revealed no statistical difference (P > 0.05), while the SUVr slope was significantly elevated in sensorimotor, dorsal attention, ventral attention, control, default, and auditory networks (P < 0.05) during fMRI scan. The task-based group independent-component analysis revealed that the most active network components derived from the combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus, and occipital pole. High correlation coefficients were found among fMRI metrics with rCMRGlu in both MRI-off and MRI-on mode (P < 0.05). Our results systematically evaluated the impact of simultaneous fMRI scan on the quantification of human brain metabolism from an integrated PET/MRI system.

Dual Function of a in vivo Albumin-Labeling Tracer for Assessment of Blood Perfusion and Vascular Permeability in Peripheral Arterial Disease by PET.

BackgroundPeripheral arterial disease (PAD) leads to tissue ischemia in the extremities. Enhanced vascular permeability plays a critical role in targeted delivery of drugs for effective therapeutic angiogenesis and resultant blood perfusion recovery. However, optimal tracers for evaluating this process in PAD patients are lacking. At this time, we employed a novel in vivo albumin-labeling tracer of dual function, termed as 18F-NEB, to assess blood perfusion as well as vascular permeability by positron emission tomography (PET).Methods and ResultsAfter successful establishment of mouse hindlimb ischemia (HI) model, static PET imaging was performed 15 min and 2 h post injection (p.i.) of 18F-NEB at 1, 3, 5, 7, 10 and 14 days post-surgery respectively. Gradual recovery of blood supply was detected by PET scan 15 min p.i. and collaborated by serial Laser Doppler. In addition, the highest vascular permeability observed by high local uptake of 18F-NEB at 2 h p.i. was consistent with histological examinations. Furthermore, we quantitatively evaluated the effect of vascular endothelial growth factor (VEGF) stimulus on vascular permeability and blood perfusion by PET scan using 18F-NEB probe in HI model, which were also confirmed by immunohistological results.ConclusionThe application of 18F-NEB probe alone by PET can successfully achieve dual imaging of blood perfusion as well as vascular permeability at different time points p.i. and monitor their responses to therapy in PAD model. The simple labeling approach and multipurpose feature suggest the great promise of using this imaging probe in theranostic applications for treating ischemic disease.

Cortical abnormalities of synaptic vesicle protein 2A in focal cortical dysplasia type II identified in vivo with 18F-SynVesT-1 positron emission tomography imaging

PurposeThe loss of synaptic vesicle glycoprotein 2A (SV2A) is well established as the major correlate of epileptogenesis in focal cortical dysplasia type II (FCD II), but this has not been directly tested in vivo. In this positron emission tomography (PET) study with the new tracer 18F-SynVesT-1, we evaluated SV2A abnormalities in patients with FCD II and compared the pattern to 18F-fluorodeoxyglucose (18F-FDG).MethodsSixteen patients with proven FCD II and 16 healthy controls were recruited. All FCD II patients underwent magnetic resonance imaging (MRI) and static PET imaging with both 18F-SynVesT-1 and 18F-FDG, while the controls underwent MRI and PET with only 18F-SynVesT-1. Visual assessment of PET images was undertaken. The standardized uptake values (SUVs) of 18F-SynVesT-1 were computed for regions of interest (ROIs), along with SUV ratio (SUVr) between ROI and centrum semiovale (white matter). Asymmetry indices (AIs) were analyzed between the lesion and the contralateral hemisphere for intersubject comparisons.ResultsLesions in the brains of FCD II patients had significantly reduced 18F-SynVesT-1 uptake compared with contralateral regions, and brains of the controls. 18F-SynVesT-1 PET indicated low lesion uptake in 14 patients (87.5%), corresponding to hypometabolism detected by 18F-FDG PET, with higher accuracy for lesion localization than MRI (43.8%) (P < 0.05). AI analyses demonstrated that in the lesions, SUVr for each of the radiotracers were not significantly different (P > 0.05), and 18F-SynVesT-1 SUVr correlated with that of 18F-FDG across subjects (R2 = 0.41, P = 0.008). Subsequent visual ratings indicated that 18F-SynVesT-1 uptake had a more restricted pattern of reduction than 18F-FDG uptake in FCD II lesions (P < 0.05).ConclusionSV2A PET with 18F-SynVesT-1 shows a higher accuracy for the localization of FCD II lesions than MRI and a more restricted pattern of abnormality than 18F-FDG PET.

Imaging of Tumor-Associated Vascular Prostate-Specific Membrane Antigen in Woodchuck Model of Hepatocellular Carcinoma.

BACKGROUND AND AIMS:Radiolabeled short peptide ligands targeting prostate-specific membrane antigen (PSMA) were developed initially for imaging and treatment of prostate cancers. While many nonprostate solid tumors including hepatocellular carcinoma (HCC) express little PSMA, their neovasculature expresses a high level of PSMA, which is avid for Gallium-68-labeled PSMA-targeting radio-ligand (68Ga-PSMA-11) for positron emission tomography (PET). However, the lack of a spontaneous animal model of tumor-associated vascular PSMA overexpression has hindered the development and assessment of PSMA-targeting radioligands for imaging and therapy of the nonprostatic cancers. We identified detectable indigenous PSMA expression on tumor neovascular endothelia in a naturally occurring woodchuck model of HCC.METHODS:Molecular docking was performed with 3 bait PSMA ligands and compared between human and woodchuck PSMA. Initially, PET images were acquired dynamically after intravenously injecting 37 MBq (1.0 mCi) of 68Ga-PSMA-11 into woodchuck models of HCC. Subsequently, 10-minute static PET scans were conducted for other animals 1-hour after injection due to HCC and liver background uptake stabilization at 30–45 minutes after injection. Liver tissue samples were harvested after imaging, fresh-frozen for quantitative reverse transcription polymerase chain reaction and western blot for validation, or fixed for histology for correlation.RESULTS:Our preclinical studies confirmed the initial clinical findings of 68Ga-PSMA-11 uptake in HCC. The agents (ligands and antibodies) developed against human PSMA were found to be reactive against the woodchuck PSMA.CONCLUSION:This animal model offers a unique opportunity for investigating the biogenesis of tumor-associated vascular PSMA, its functional role(s), and potentials for future treatment strategies targeting tumor vascular PSMA using already developed PSMA-targeting agents.

Assessing different approaches to estimate single-subject metabolic connectivity from dynamic [18F]fluorodeoxyglucose Positron Emission Tomography data.

Metabolic connectivity is conventionally calculated in terms of correlation of static positron emission tomography (PET) measurements across subjects. There is increasing interest in deriving metabolic connectivity at the single-subject level from dynamic PET data, in a similar way to functional magnetic resonance imaging. However, the strong multicollinearity among region-wise PET time-activity curves (TACs), their non-Gaussian distribution, and the choice of the best strategy for TAC standardization before metabolic connectivity estimation, are non-trivial methodological issues to be tackled.In this work we test four different approaches to estimate sparse inverse covariance matrices, as well as three similarity-based methods to derive adjacency matrices. These approaches, combined with three different TAC standardization strategies, are employed to quantify metabolic connectivity from dynamic [18F]fluorodeoxyglucose ([18F]FDG) PET data in four healthy subjects.