Static Positron Emission Tomography Images Research Articles

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.

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.

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.

4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network

Although convolutional neural networks (CNNs) demonstrate the superior performance in denoising positron emission tomography (PET) images, a supervised training of the CNN requires a pair of large, high-quality PET image datasets. As an unsupervised learning method, a deep image prior (DIP) has recently been proposed; it can perform denoising with only the target image. In this study, we propose an innovative procedure for the DIP approach with a four-dimensional (4D) branch CNN architecture in end-to-end training to denoise dynamic PET images. Our proposed 4D CNN architecture can be applied to end-to-end dynamic PET image denoising by introducing a feature extractor and a reconstruction branch for each time frame of the dynamic PET image. In the proposed DIP method, it is not necessary to prepare high-quality and large patient-related PET images. Instead, a subject’s own static PET image is used as additional information, dynamic PET images are treated as training labels, and denoised dynamic PET images are obtained from the CNN outputs. Both simulation with [18F]fluoro-2-deoxy-D-glucose (FDG) and preclinical data with [18F]FDG and [11C]raclopride were used to evaluate the proposed framework. The results showed that our 4D DIP framework quantitatively and qualitatively outperformed 3D DIP and other unsupervised denoising methods. The proposed 4D DIP framework thus provides a promising procedure for dynamic PET image denoising.

Dynamic PET Image Denoising Using Deep Image Prior Combined With Regularization by Denoising

The quantitative accuracy of positron emission tomography (PET) is affected by several factors, including the intrinsic resolution of the imaging system and inherently noisy data, which result in a low signal-to-noise ratio (SNR) of PET image. To address this problem, in this paper, we proposed a novel deep learning denoising framework aiming to enhance the quantitative accuracy of dynamic PET images via introduction of deep image prior (DIP) combined with Regularization by Denoising (RED), as such the method is labeled as DeepRED denoising. The network structure is based on encoder-decoder architecture and uses skip connections to combine hierarchical features to generate the estimated image. The network input can be random noise or other prior images (such as the patient’s own static PET image), avoiding the need of high quality noiseless images, which is limited in PET clinical practice due to high radiation dose. Based on simulated data and real patient data, the quantitative performance of the proposed method was compared with conventional Gaussian filtering (GF), non-local mean (NLM), block-matching and 3D filtering (BM3D), DIP and stochastic gradient Langevin dynamics (SGLD) method. Overall, the proposed method can outperform other conventional methods in substantial visual as well as quantitative accuracy improvements (in terms of noise versus bias performance) with and without prior images.

Heterogeneous parameters based on 18F-FET PET imaging can non-invasively predict tumor grade and isocitrate dehydrogenase gene 1 mutation in untreated gliomas.

The present study aimed to explore the efficacy of easily obtained intratumoral heterogeneous parameters, other than regular semi-quantitative parameters, based on static O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) positron emission tomography (PET) imaging in glioma grade and isocitrate dehydrogenase (IDH) gene 1 mutation prediction. Fifty-eight adult patients with untreated glioma (grades II-IV) who underwent preoperative 18F-FET PET/computed tomography (CT) imaging were enrolled in the present study. Eight semi-automatically obtained static PET imaging parameters after lesion delineation were chosen for analysis. These were: maximal tumor-to-background ratio (TBRmax), peak tumor-to-background ratio (TBRpeak), mean tumor-to-background ratio (TBRmean), coefficient of variation (COV), heterogeneity index (HI), the standard deviation of lesion standardized uptake value (SUVsd), metabolic tumor volume (MTV), and total lesion tracer standardized uptake (TLU). Pathological and immunohistochemical results were used as a reference. The receiver-operating characteristic analysis was used to investigate the predictive efficacy of these parameters in glioma grade and IDH1 mutation status. TLU [area under the curve (AUC): 0.841, P<0.0001], TBRpeak (AUC: 0.832, P<0.0001), and HI (AUC: 0.826, P<0.0001) had the top 3 single-parameter predictive performance between grade II or III and grade IV glioma patients. Combinations of TBRmax, SUVsd, and TBRmean (AUC: 0.850, P<0.0001); HI, SUVsd, and MTV (AUC: 0.848, P<0.0001); and HI, SUVsd, and TLU (AUC: 0.848, P<0.0001) had the top 3 multiple-parameter predictive performance. SUVsd (AUC: 0.710, P=0.0028), TLU (AUC: 0.698, P=0.0074), and HI (AUC: 0.676, P=0.0159) had the top 3 single-parameter predictive performance in the IDH1 genotype. Combinations of TBRmax, SUVsd, and TBRmean (AUC: 0.821, P<0.0001); SUVsd and TBRmean (AUC: 0.804, P<0.0001); and SUVsd, HI, and TBRmean (AUC: 0.799, P<0.0001) had the top 3 multiple-parameter predictive performance. These easily obtained and highly repetitive heterogeneous parameters based on static 18F-FET PET/CT imaging can non-invasively predict glioma grade and IDH1 mutation, crucial in treatment planning, and prognostic evaluation.

Dynamic 18F-Fluoro-Ethyl-Tyrosine Positron Emission Tomography/ Computed Tomography: A Better Predictor of Isocitrate Dehydrogenase Mutation in Presurgical Evaluations of Glioma.

The recent WHO classification of gliomas has incorporated molecular markers such as isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion into the ambit of morphological diagnosis, and 18F-fluoro-ethyl-tyrosine (18F-FET) positron emission tomography (PET) has shown its utility in noninvasive glioma grading and prognosis. Both dynamic and static FET PET parameters may assist in predicting the IDH mutational status, but time to peak derived from dynamic data may be a better predictor for IDH status. We present a case of left frontal lobe lesion suggestive of high-grade glioma on magnetic resonance imaging and static 18F-FET PET images, however, dynamic FET image was suggestive of low-grade IDH1-mutated glioma which was later confirmed on histology and immunohistochemistry.

The development of a Glypican-3-specific binding peptide using in vivo and in vitro two-step phage display screening for the PET imaging of hepatocellular carcinoma.

Glypican-3 (GPC3) is a diagnostic biomarker for hepatocellular carcinoma (HCC). Although numerous designs targeting GPC3 have been reported, the HCC diagnostic agents with specific tumor accumulation and low background, particularly in normal liver tissue, are still in need. Peptides have attracted considerable attention as an imaging probe due to their low immunogenicity, short in vivo circulation time, and acceptable production cost. Herein, a two-step phage display screening approach was performed against GPC3-high expression tumor xenografts in vivo, followed by human recombinant GPC3 protein in vitro. A GPC3-specific binding peptide, named TJ12P2, with the sequence of Ser-Asn-Asp-Arg-Pro-Pro-Asn-Ile-Leu-Gln-Lys-Arg (SNDRPPNILQKR) was identified. The apparent Kd value between TJ12P2 and the GPC3 protein was measured as 158.2 ± 26.25 nM. After 18F labeling, 18F-AlF-NOTA-TJ12P2 was found accumulated in the tumors by positron emission tomography (PET) imaging in two HCC subcutaneous tumor models (HepG2 and SMMC-7721) with high GPC3 expression. Static PET imaging revealed that 18F-AlF-NOTA-TJ12P2 accumulation in the HepG2 and SMMC-7721 tumors reached 1.825 ± 0.296 %ID g-1 and 1.575 ± 0.520 %ID g-1, with tumor-to-muscle ratios of 4.14 ± 0.50 and 4.25 ± 0.25, respectively, at 30 min post-injection (p.i.). Much less accumulation (0.533 ± 0.078 %ID g-1) of the 18F-AlF-NOTA-TJ12P2 was found in the control PC3 tumors with low GPC3 expression. More importantly, no obvious normal liver uptake of TJ12P2 was observed in the abovementioned animal models. As a result, a novel peptide targeting GPC3, TJ12P2, with strong affinity and specificity was identified using a two-step phage display screening technique in the present study. The 18F-AlF-NOTA-TJ12P2 may be a promising PET imaging probe with translational potential for accurate HCC diagnosis.

A Metal-Free DOTA-Conjugated 18F-Labeled Radiotracer: [18F]DOTA-AMBF3-LLP2A for Imaging VLA-4 Over-Expression in Murine Melanoma with Improved Tumor Uptake and Greatly Enhanced Renal Clearance.

DOTA is commonly used for radiometal chelation in molecular imaging. Yet in the absence of a radiometal, DOTA is hypothesized to promote renal clearance of 18F-labeled peptide tracers. In light of an increasing interest in the use of F18 for PET, here the effect of DOTA is evaluated for the first time with an 18F-labeled tracer and is found to significantly improve the quality of images acquired through positron emission tomography (PET). We chose to image the peptide LLP2A that recognizes the transmembrane protein very-late antigen 4 (VLA-4) that is overexpressed by many cancers. Since it is known that [18F]RBF3-PEG2-LLP2A derivatives gave low tumor uptake values and significant GI tract accumulation, this ligand thus represents an ideal means of testing the additive effects of a DOTA group on clearance while permitting a facile, user-friendly, one-step 18F-labeling. A newly designed RBF3-LLP2A bioconjugate with an appended DOTA moiety increased tumor uptake nearly 3-fold and reduced GI accumulation by more than 10-fold. The DOTA-AMBF3-PEG2-LLP2A was radiolabeled by isotope exchange and was purified by semiprep HPLC and C18 cartridge elution. Male C57BL/6J mice bearing B16-F10 melanoma tumors that overexpress the VLA-4 target were used to evaluate [18F]DOTA-AMBF3-PEG2-LLP2A using a combination of static and dynamic PET scans, biodistribution studies, and blocking controls at 1 h post injection (p.i.). The precursor peptide was synthesized and 18F-labeled to provide formulations with mean (±SD) radiochemical purities of 95.9 ± 1.8%, in radiochemical yields of 4.8 ± 2.9% having molar activities of 131.7 ± 50.3 GBq/μmol. In vivo static PET images of [18F]DOTA-AMBF3-PEG2-LLP2A provided clear tumor visualization, and biodistribution studies showed that tumor uptake was 9.46 ± 2.19% injected dose per gram of tissue (%ID/g) with high tumor/muscle and tumor/blood contrast ratios of ∼8 and ∼10, respectively. Blocking confirmed the specificity of [18F]DOTA-AMBF3-PEG2-LLP2A to VLA-4 in the tumor and the bone marrow. Dynamic PET showed clearance of [18F]DOTA-AMBF3-PEG2-LLP2A mainly via the renal pathway, wherein accumulation in the intestines was reduced 10-fold compared to our previously investigated LLP2A's, while spleen uptake was at levels similar to previously reported LLP2A-chelator radiotracers. [18F]DOTA-AMBF3-PEG2-LLP2A represents a promising VLA-4 radiotracer and provides key evidence as to how a DOTA appendage can significantly reduce GI uptake in favor of urinary excretion. Implications for the development of dual-isotope theranostics that exploit the use fluorine-18 for imaging and DOTA to chelate therapeutic metal cations for therapy are discussed.

Visualization and quantification of in vivo homing kinetics of myeloid-derived suppressor cells in primary and metastatic cancer.

Myeloid-derived suppressor cells (MDSCs) are immunosuppressive cells of the myeloid compartment and major players in the tumor microenvironment (TME). With increasing numbers of studies describing MDSC involvement in cancer immune escape, cancer metastasis and the dampening of immunotherapy responses, MDSCs are of high interest in current cancer therapy research. Although heavily investigated in the last decades, the in vivo migration dynamics of MDSC subpopulations in tumor- or metastases-bearing mice have not yet been studied extensively. Therefore, we have modified our previously reported intracellular cell labeling method and applied it to in vitro generated MDSCs for the quantitative in vivo monitoring of MDSC migration in primary and metastatic cancer. MDSC migration to primary cancers was further correlated to the frequency of endogenous MDSCs. Methods: Utilizing a 64Cu-labeled 1,4,7-triazacyclononane-triacetic acid (NOTA)-modified CD11b-specific monoclonal antibody (mAb) (clone M1/70), we were able to label in vitro generated polymorphonuclear (PMN-) and monocytic (M-) MDSCs for positron emission tomography (PET) imaging. Radiolabeled PMN- and M-MDSCs ([64Cu]PMN-MDSCs and [64Cu]M-MDSCs, respectively) were then adoptively transferred into primary and metastatic MMTV-PyMT-derived (PyMT-) breast cancer- and B16F10 melanoma-bearing experimental animals, and static PET and anatomical magnetic resonance (MR) images were acquired 3, 24 and 48 h post cell injection. Results: The internalization of the [64Cu]NOTA-mAb-CD11b-complex was completed within 3 h, providing moderately stable radiolabeling with little detrimental effect on cell viability and function as determined by Annexin-V staining and T cell suppression in flow cytometric assays. Further, we could non-invasively and quantitatively monitor the migration and tumor homing of both [64Cu]NOTA-αCD11b-mAb-labeled PMN- and M-MDSCs in mouse models of primary and metastatic breast cancer and melanoma by PET. We were able to visualize and quantify an increased migration of adoptively transferred [64Cu]M-MDSCs than [64Cu]PMN-MDSCs to primary breast cancer lesions. The frequency of endogenous MDSCs in the PyMT breast cancer and B16F10 melanoma model correlated to the uptake values of adoptively transferred MDSCs with higher frequencies of PMN- and M-MDSCs in the more aggressive B16F10 melanoma tumors. Moreover, aggressively growing melanomas and melanoma-metastatic lesions recruited higher percentages of both [64Cu]PMN- and [64Cu]M-MDSCs than primary and metastatic breast cancer lesions as early as 24 h post adoptive MDSC transfer, indicating an overall stronger recruitment of cancer-promoting immunosuppressive MDSCs. Conclusion: Targeting of the cell surface integrin CD11b with a radioactive mAb is feasible for labeling of murine MDSCs for PET imaging. Fast internalization of the [64Cu]NOTA-αCD11b-mAb provides presumably enhanced stability while cell viability and functionality was not significantly affected. Moreover, utilization of the CD11b-specific mAb allows for straightforward adaptation of the labeling approach for in vivo molecular imaging of other myeloid cells of interest in cancer therapy, including monocytes, macrophages or neutrophils.

Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity.

Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information. Here, we combine both PET temporal and MRI information adaptively to improve the quality of direct Patlak reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. Computer simulations and hybrid real-patient data acquired on a simultaneous PET/MR scanner were used to evaluate the proposed methods. Results show that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.

Development and Characterization of an 18F-labeled Ghrelin Peptidomimetic for Imaging the Cardiac Growth Hormone Secretagogue Receptor.

One-third of patients with heart disease develop heart failure, which is diagnosed through imaging and detection of circulating biomarkers. Imaging strategies reveal morphologic and functional changes but fall short of detecting molecular abnormalities that can lead to heart failure, and circulating biomarkers are not cardiac specific. Thus, there is critical need for biomarkers that are endogenous to myocardial tissues. The cardiac growth hormone secretagogue receptor 1a (GHSR1a), which binds the hormone ghrelin, is a potential biomarker for heart failure. We have synthesized and characterized a novel ghrelin peptidomimetic tracer, an 18F-labeled analogue of G-7039, for positron emission tomography (PET) imaging of cardiac GHSR1a. In vitro analysis showed enhanced serum stability compared to natural ghrelin and significantly increased cellular uptake in GHSR1a-expressing OVCAR cells. Biodistribution studies in mice showed that tissue uptake of the tracer was independent of circulating ghrelin levels, and there was negligible cardiac uptake and high uptake in the liver, intestines, and kidneys. Specificity of tracer uptake was assessed using ghsr −/− mice; both static and dynamic PET imaging revealed no difference in cardiac uptake, and there was no significant correlation between cardiac standardized uptake values and GHSR1a expression. Our study lays the groundwork for further refinement of peptidomimetic PET tracers targeting cardiac GHSR1a.

PET Imaging of Human Brown Adipose Tissue with the TSPO Tracer [11C]PBR28.

Brown adipose tissue (BAT) in adult humans has been recently rediscovered and intensively investigated as a new potential therapeutic target for obesity and type 2 diabetes (T2D). However, reliable assessment of BAT mass in vivo represents a considerable challenge. The purpose of this investigation is to demonstrate for the first time that human BAT depots can be imaged with a translocator protein (TSPO)-specific positron emission tomography (PET) tracer [11C]PBR28 under thermoneutral conditions. In this retrospective analysis, we analyzed the images of three healthy volunteers who underwent PET/magnetic resonance (MR) imaging after injection of 14mCi of [11C]PBR28 at room temperature. Thirty-minute static PET images were reconstructed from the data obtained 60-90min after the injection of the tracer. [11C]PBR28 uptake in the neck/supraclavicular regions was identified, which was parallel to the known distribution pattern of human BAT depots. These areas co-localized with the areas of hyperintensity and corresponded to fat on T1-weighted MR images. Standardized uptake value (SUV) was used to quantify [11C]PBR28 signal in BAT depots. The average (±SD) SUV(mean) and SUVmax for BAT depots was 2.13 (±0.33) and 3.19 (±0.34), respectively, while the average SUV(mean) for muscle and subcutaneous adipose tissue was 0.79 (±0.1) and 0.18 (±0.04), respectively. In this brief article, we provide the first evidence suggesting that [11C]PBR28, a widely available TSPO-specific PET tracer, can be used for imaging human BAT mass under thermoneutral conditions.

Hypoxia PET imaging techniques: data acquisition and analysis

Hypoxia is associated with poor treatment outcome in several tumor entities. Positron emission tomography (PET) offers the possibility to visualize tumor hypoxia in a spatially resolved manner using dedicated hypoxia PET tracers. The aim of this article is to review different tracers, PET acquisition methods and data analysis strategies that have been used in previous studies. A literature research has been performed in the database PubMed using the keywords “FMISO”, “FAZA”, “HX4”, “EF3/5” or “Cu-ATSM” in combination with “hypoxia”, “PET”, and “radiotherapy” in order to review the current status of hypoxia PET acquisition and data analysis strategies. A total of 141 articles were identified during the literature search. However, after exclusion of preclinical or simulation studies, 51 remained. Hypoxia PET imaging using different PET tracers was shown to have prognostic power in order to stratify patients according to outcome after chemoradiotherapy. However, most studies present small patient groups and use a variety of different PET acquisition protocols and data analysis strategies. Hypoxia PET acquisition can be performed using either static or dynamic PET acquisition. Due to the slow diffusive transport of the tracer, image acquisition needs to be performed several (2–4) h post-injection. Motivated by the low intrinsic contrast of hypoxia PET imaging, most studies use tumor-to-muscle ratios (TMR) to define areas of tumor hypoxia from static PET images, whereas dynamic scans are quantitatively analyzed using kinetic modeling. Data acquisition and analysis for hypoxia PET imaging requires standardization and harmonization, in order to empower large multicenter trials in the future, which are needed to proof the clinical benefit of personalized treatments on the basis of hypoxia PET imaging.

18F]Fluoromisonidazole PET in rectal cancer

BackgroundThere is an increasing interest in developing predictive biomarkers of tissue hypoxia using functional imaging for personalised radiotherapy in patients with rectal cancer that are considered for neoadjuvant chemoradiotherapy (CRT). The study explores [18F]fluoromisonidazole ([18F]FMISO) positron emission tomography (PET) scans for predicting clinical response in rectal cancer patients receiving neoadjuvant CRT.MethodsPatients with biopsy-proven rectal adenocarcinoma were imaged at 0–45 min, 2 and 4 h, at baseline and after 8–10 fractions of CRT (week 2). The first 6 patients did not receive an enema (the non-enema group) and the last 4 patients received an enema before PET-CT scan (the enema group). [18F]FMISO production failed on 2 occasions. Static PET images at 4 h were analysed using tumour-to-muscle (T:M) SUVmax and tumour-to-blood (T:B) SUVmax. The 0–45 min dynamic PET scans were analysed using Casciari model to report hypoxia and perfusion. Akaike information criteria (AIC) were used to compare data fittings for different pharmacokinetic models. Pathological tumour regression grade was scored using American Joint Committee on Cancer (AJCC) 7.0. Shapiro-Wilk test was used to evaluate the normality of the data.ResultsFive out of eleven (5/11) patients were classed as good responders (AJCC 0/1 or good clinical response) and 6/11 as poor responders (AJCC 2/3 or poor clinical response). The median T:M SUVmax was 2.14 (IQR 0.58) at baseline and 1.30 (IQR 0.19) at week 2, and the corresponding median tumour hypoxia volume was 1.08 (IQR 1.31) cm3 and 0 (IQR 0.15) cm3, respectively. The median T:B SUVmax was 2.46 (IQR 1.50) at baseline and 1.61 (IQR 0.14) at week 2, and the corresponding median tumour hypoxia volume was 5.68 (IQR 5.86) cm3 and 0.76 (IQR 0.78) cm3, respectively. For 0–45 min tumour modelling, the median hypoxia was 0.92 (IQR 0.41) min−1 at baseline and 0.70 (IQR 0.10) min−1 at week 2. The median perfusion was 4.10 (IQR 1.71) ml g−1 min−1 at baseline and 2.48 (IQR 3.62) ml g−1 min−1 at week 2. In 9/11 patients with both PET scans, tumour perfusion decreased in non-responders and increased in responders except in one patient. None of the changes in other PET parameters showed any clear trend with clinical outcome.ConclusionsThis pilot study with small number of datasets revealed significant challenges in delivery and interpretation of [18F]FMISO PET scans of rectal cancer. There are two principal problems namely spill-in from non-tumour tracer activity from rectal and bladder contents. Emphasis should be made on reducing spill-in effects from the bladder to improve data quality. This preliminary study has shown fundamental difficulties in the interpretation of [18F]FMISO PET scans for rectal cancer, limiting its clinical applicability.

Tumor Delineation and Quantitative Assessment of Glucose Metabolic Rate within Histologic Subtypes of Non-Small Cell Lung Cancer by Using Dynamic 18F Fluorodeoxyglucose PET.

Purpose To assess whether dynamic fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) has added value over static 18F-FDG PET for tumor delineation in non-small cell lung cancer (NSCLC) radiation therapy planning by using pathology volumes as the reference standard and to compare pharmacokinetic rate constants of 18F-FDG metabolism, including regional variation, between NSCLC histologic subtypes. Materials and Methods The study was approved by the institutional review board. Patients gave written informed consent. In this prospective observational study, 1-hour dynamic 18F-FDG PET/computed tomographic examinations were performed in 35 patients (36 resectable NSCLCs) between 2009 and 2014. Static and parametric images of glucose metabolic rate were obtained to determine lesion volumes by using three delineation strategies. Pathology volume was calculated from three orthogonal dimensions (n = 32). Whole tumor and regional rate constants and blood volume fraction (VB) were computed by using compartment modeling. Results Pathology volumes were larger than PET volumes (median difference, 8.7-25.2 cm3; Wilcoxon signed rank test, P < .001). Static fuzzy locally adaptive Bayesian (FLAB) volumes corresponded best with pathology volumes (intraclass correlation coefficient, 0.72; P < .001). Bland-Altman analyses showed the highest precision and accuracy for static FLAB volumes. Glucose metabolic rate and 18F-FDG phosphorylation rate were higher in squamous cell carcinoma (SCC) than in adenocarcinoma (AC), whereas VB was lower (Mann-Whitney U test or t test, P = .003, P = .036, and P = .019, respectively). Glucose metabolic rate, 18F-FDG phosphorylation rate, and VB were less heterogeneous in AC than in SCC (Friedman analysis of variance). Conclusion Parametric images are not superior to static images for NSCLC delineation. FLAB-based segmentation on static 18F-FDG PET images is in best agreement with pathology volume and could be useful for NSCLC autocontouring. Differences in glycolytic rate and VB between SCC and AC are relevant for research in targeting agents and radiation therapy dose escalation. © RSNA, 2016 Online supplemental material is available for this article.