Analysis Of Positron Emission Tomography Data Research Articles

Preclinical Positron Emission Tomography with Body Conforming Animal Molds for Cloud-Based Automated Image Analysis in Mice.

Positron Emission Tomography (PET) is a molecular imaging modality that can be used to investigate a multitude of pharmacological questions, such as biomarker modulation, receptor occupancy, and biodistribution of compounds of interest. In biodistribution studies, experimental subjects are often longitudinally imaged after receiving the test article. The images are then analyzed to derive the compound's distribution profile in various organs at different timepoints. This constitutes a crucial step in drug development to understand the distribution and potentially binding profile of an investigative compound. Standard/manual methods of PET imaging-based biodistribution analyses, however, are labor-intensive and time-consuming and are often associated with high inter-operator variability. Further, it is challenging to keep the animals' positions consistent across different timepoints. To address these shortcomings, a series of mouse Body Conforming Animal Molds (BCAMs) were used to enable rigid and consistent positioning of animals during PET/CT imaging acquisition. Further, a Software-as-a-Service (SaaS) platform consisting of a cloud-based Organ Probability Map (OPM) and an artificial intelligence-powered segmentation tool were employed to enable reliable and automated quantitation of in vivo PET imaging data. The workflow presented here includes (1) prepping mice for imaging with the BCAMs, including the proper implantation of subcutaneous tumors to be compatible with the molds, (2) acquiring PET/CT images with BCAMs using the G8 scanner, and 3) performing automated organ segmentation and biodistribution analysis using the cloud-based SaaS. [18F]FDG was used as an exemplar tracer here, but other biomarkers and/or radio-labeled compounds can be readily adapted into the workflow. This procedure can be executed accurately and effectively with minimal training, and the automated PET data analysis yielded satisfactory results consistent with the manual method.

Reproducible brain PET data analysis: easier said than done.

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future.

Synthesis and Evaluation of 18F-Labeled Phenylpiperazine-like Dopamine D3 Receptor Radioligands for Positron Emission Tomography Imaging.

The dopamine D3 receptor (D3R) is important in the pathophysiology of various neuropsychiatric disorders, such as depression, bipolar disorder, schizophrenia, drug addiction, and Parkinson's disease. Positron emission tomography (PET) with innovative radioligands provides an opportunity to assess D3R in vivo and to elucidate D3R-related disease mechanisms. Herein, we present the synthesis of eight 18F-labeled phenylpiperazine-like D3R-selective radioligands possessing good radiochemical purity (>97%), in vitro stability (>95%), and befitting lipophilicity. Based on in vitro binding assays and static microPET studies, the phenylpiperazine-like radioligands [18F]FBPC01 and [18F]FBPC03 were chosen as lead radioligands targeting D3R. Molecular docking further elucidated their binding mechanism. Radiolabeling conditions were optimized and then applied to an automated radiolabeling process, affording products with high specific activity (>112 GBq/μmol). Dynamic rat PET study demonstrated the specific binding of [18F]FBPC01 and [18F]FBPC03 to D3R in the brain ventricles and the pituitary gland. Validated by dynamic PET data analysis, biodistribution study, and metabolism analysis, [18F]FBPC03 exhibited the highest PET signal-to-noise ratio, good D3R-specific binding in the brain ventricles and pituitary gland of rats with few off-target binding, negligible defluorination, and stable brain metabolism, which indicated that [18F]FBPC03 was a promising D3R radioligand.

A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.

Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.

Cerebellum Hyperactivity and its cortical connectivity in Alzheimer’s Disease (AD) utilizing FDG PET and Resting State Functional Magnetic Resonance.

AbstractBackgroundThe role of the cerebellum in Alzheimer’s disease (AD) is not fully understood. There are a few studies that have demonstrated impaired cortical – cerebellar connectivity in AD. Additionally, transgenic mouse studies clearly show amyloid‐β (Aβ) deposition in the cerebellum affecting synaptic transmission and plasticity, sometimes before plaque formation. To characterize metabolic activity in the cerebellum, we analyzed 18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET) and resting‐state functional MRI (rs‐fMRI) data in AD, mild‐cognitive impaired (MCI), and age‐matched cognitively normal (CN) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our focus was on the intrinsic functional connectivity of the cerebellum with three brain networks: the default mode network (DMN), dorsal attention network (DAN), and the primary olfactory cortex (POC) in the medial temporal lobe. We hypothesized that the resting state functional connectivity of cerebellar regions (that showed differential FDG metabolic activity) would exhibit impaired brain‐wide network connectivity.MethodA group of 272 ADNI subjects (CN = 80, MCI = 149, AD = 43) with FDG‐PET scans were analyzed in this study. All 272 subjects did not have consistent rs‐fMRI data therefore, we analyzed 160 subjects (CN = 80, MCI = 84, AD = 29) that had consistent rs‐fMRI data acquisition parameters. The PET preprocessing was performed in SPM12. Partial volume correction was performed using the Van‐Cittert deconvolution technique. The rs‐fMRI data was preprocessed in DPABI; PET and rs‐fMRI data were normalized to the Montreal Neurological Institute template. DMN and DAN masks were downloaded from neurovault.org. The Statistical analyses were performed in DPABI.ResultThe SPM group analysis of PET data identified crus II, right cerebellum 4,5 and vermis lobule 6 as hyperactive (increased glucose metabolism) in AD and MCI compared to CN (Figure 1). Subsequent analyses identified brain regions within the DMN, DAN, and POC that are correlated with hyperactive cerebellar regions. Similarity analyses detected impaired resting state functional connectivity of hyperactive cerebellar regions with the three brain networks (Figure 2).ConclusionThe hyper‐metabolism in the cerebellum may reflect disruption of local and brain‐wide network connectivity due to neurodegeneration. Observed hyper‐metabolism may be related to inhibitory dynamics of the cerebellum — providing a hypothetical mechanism to explain the susceptibility of brain networks in AD.

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.

Spatiotemporal learning of dynamic positron emission tomography data improves diagnostic accuracy in breast cancer.

Positron emission tomography (PET) can reveal metabolic activity in a voxelwise manner. PET analysis is commonly performed in a static manner by analyzing the standardized uptake value (SUV) obtained from the plateau region of PET acquisitions. A dynamic PET acquisition can provide a map of the spatiotemporal concentration of the tracer in vivo, hence conveying information about radiotracer delivery to tissue, its interaction with the target and washout. Therefore, tissue-specific biochemical properties are embedded in the shape of time activity curves (TACs), which are generally used for kinetic analysis. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function (AIF), and specific compartmental models to obtain a full quantitative analysis of PET data. The main drawback of this approach is the need for invasive procedures requiring arterial blood sample collection during the whole PET scan. In this paper, we address the challenge of improving PET diagnostic accuracy through an alternative approach based on the analysis of time signal intensity patterns. Specifically, we demonstrate the diagnostic potential of tissue TACs provided by dynamic PET acquisition using various deep learning models. Our framework is shown to outperform the discriminative potential of classical SUV analysis, hence paving the way for more accurate PET-based lesion discrimination without additional acquisition time or invasive procedures. Clinical Relevance- The diagnostic accuracy of dynamic PET data exploited by deep-learning based time signal intensity pattern analysis is superior to that of static SUV imaging.

Application of correlated component analysis to dynamic PET time-activity curves denoising.

Positron emission tomography (PET) is a physiological, non-invasive imaging technique, which forms an essential part of nuclear medicine. The data obtained in a PET scan represent the concentration of an administered radiotracer in tissues over time. Quantitative analysis of PET data makes possible the assessments of in-vivo physiological processes. The Logan graphical analysis (LGA) is one of the methods that are used for quantitative analysis of PET data. LGA transforms PET data into a simple linear relationship. The slope of the LGA linear relationship is a physiological quantity denoting receptor availability. This quantity is termed distribution volume ratio (DVR). LGA-based estimates of the DVR are negatively affected by the noise in PET data -leading to the DVR being underestimated. A number of approaches proposed to address this issue have been observed to reduce the bias at the cost precision. An alternative regression method, least-squares cubic (LSC), was recently applied to estimate the DVR in order to reduce the bias. LSC was observed to reduce the bias in the LGA-based estimates. However, slight increases were also observed in the variance of the LSC-based estimates. This calls for methods to act against the variance in the LSC-based estimates. In this study, an alternative method is applied for tTAC denoising. This method is referred to as correlated component analysis (CorrCA). CorrCA transform the data by searching for dimensions of maximum correlation. This technique is closely related to other well-known methods such as principal component analysis and independent component analysis. In this study, the data were denoised by CorrCA (to act against the variance in the estimate) and the DVR was estimated by LSC, which provides for minimal bias. The resulting method LSC-CorrCA, gave less-biased estimated with increased precision. This was observed for both simulation results as well as for clinical data, both for 11C Pittsburgh compound B. Simulation data revealed reduced variances in LCS-CorrCA-based estimates, and the clinical data showed improved contrast between gray and white matter regions.Clinical Relevance-Improved DVR estimates would ease the interpretation of medical images, which will in turn positively influence the clinical processes, from diagnosis to treatment and follow-ups.

Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients.

Simple SummaryRecent works on dynamic amino acid positron emission tomography (PET) imaging of gliomas have highlighted characteristic behaviors of time-activity curves (TACs) extracted from the whole tumor w.r.t. the grade, genotype, and outcome. However, gliomas are known to be highly heterogeneous tumors. Here, we aim at highlighting similar dynamic behaviors at the voxel level within the tumor volume in [S-methyl-11C]methionine PET data of 33 glioma patients using principal component analysis (PCA). The PCA model was derived from TACs of 20 patients and subsequently applied to 13 other patients in whom our approach was shown to outperform classical pharmacokinetic modeling to this end. Our parameter-free approach provides additional parametric maps from dynamic methionine PET scans with little modification of the routine protocol and no arterial sampling. This early methodological work paves the way for various clinical studies on glioma heterogeneity with applications for treatment planning and response evaluation.Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

Pre-Processing Method for Contouring the Uptake Levels of [18F] FDG for Enhanced Specificity of PET Imaging of Solitary Hypermetabolic Pulmonary Nodules.

Background: The paper presents a pre-processing method which, based on positron-emission tomography (PET) images of 18F-fluorodeoxyglucose ([18F] FDG) hypermetabolic pulmonary nodules, makes it possible to obtain additional visual characteristics and use them to enhance the specificity of imaging. Material and Methods: A retrospective analysis of 69 FDG-PET/CT scans of solitary hypermetabolic pulmonary nodules (40 cases of lung cancer and 29 benign tumours), where in each case, the standardised uptake value of the hottest voxel within the defined volume of interest was greater than 2.5 (SUVmax > 2.5). No diagnosis could be made based on these SUVmax values. All of the PET DICOM images were transformed by means of the pre-processing method for contouring the uptake levels of [18F] FDG (PCUL-FDG). Next, a multidimensional comparative analysis was conducted using a synthetic variable obtained by calculating the similarities based on the generalised distance measure for non-metric scaling (GDM2) from the pattern object. The calculations were performed with the use of the R language. Results: The PCUL-FDG method revealed 73.9% hypermetabolic nodules definitively diagnosed as either benign or malignant lesions. As for the other 26.1% of the nodules, there was uncertainty regarding their classification (some had features suggesting malignancy, while the characteristics of others made it impossible to confirm malignancy with a high degree of certainty). Conclusions: Application of the PCUL-FDG method enhances the specificity of PET in imaging solitary hypermetabolic pulmonary nodules. Images obtained using the PCUL-FDG method can serve as point of departure for automatic analysis of PET data based on convolutional neural networks.

18F-meta-fluorobenzylguanidine (18F-mFBG) to monitor changes in norepinephrine transporter expression in response to therapeutic intervention in neuroblastoma models

Targeted radiotherapy with 131I-mIBG, a substrate of the human norepinephrine transporter (NET-1), shows promising responses in heavily pre-treated neuroblastoma (NB) patients. Combinatorial approaches that enhance 131I-mIBG tumour uptake are of substantial clinical interest but biomarkers of response are needed. Here, we investigate the potential of 18F-mFBG, a positron emission tomography (PET) analogue of the 123I-mIBG radiotracer, to quantify NET-1 expression levels in mouse models of NB following treatment with AZD2014, a dual mTOR inhibitor. The response to AZD2014 treatment was evaluated in MYCN amplified NB cell lines (Kelly and SK-N-BE(2)C) by Western blot (WB) and immunohistochemistry. PET quantification of 18F-mFBG uptake post-treatment in vivo was performed, and data correlated with NET-1 protein levels measured ex vivo. Following 72 h AZD2014 treatment, in vitro WB analysis indicated decreased mTOR signalling and enhanced NET-1 expression in both cell lines, and 18F-mFBG revealed a concentration-dependent increase in NET-1 function. AZD2014 treatment failed however to inhibit mTOR signalling in vivo and did not significantly modulate intratumoural NET-1 activity. Image analysis of 18F-mFBG PET data showed correlation to tumour NET-1 protein expression, while further studies are needed to elucidate whether NET-1 upregulation induced by blocking mTOR might be a useful adjunct to 131I-mIBG therapy.

Brain activity detection in single- and multi-subject PET data by Bayesian analysis

Positron emission tomography (PET) is a functional neuroimaging method that maps brain activity non-invasively. Statistical methods play an essential part in understanding and analysing functional PET data. Several Bayesian approaches have been proposed for neuroimaging techniques that arrange information on the brain structure or activity function. In this paper, Bayesian analysis was used to detect functional brain activity in single- and multiple-subject PET data. Free PET dataset analyses for a single subject and five multiple subjects were conducted. A total of 72 functional PET images were processed, 12 for each one of the five multiple subjects and for the single subject. Several ways to design multiple-subject PET data were introduced in this work. Bayesian analysis was performed on the five multiple-subject and the single-subject PET data. A comparison was presented to determine which statistical matrix design is applicable for brain detection activity in PET data. The results of the design matrix and brain activity detection were presented for each selected design matrix. The Bayesian estimation of each case of the PET dataset for all the subjects was plotted. The brain activity was plotted as voxels on a transparent brain image in three orthogonal planes. The voxels were visualised using the maximum intensity projection method. Results showed that brain activity could not be detected easily in single-subject PET data. Finding the activity in multiple subjects depended on the design matrix used for PET data analysis.

MRI-Driven PET Image Optimization for Neurological Applications.

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.

Functional Data Analysis of Dynamic PET Data

ABSTRACT One application of positron emission tomography (PET), a nuclear imaging technique, in neuroscience involves in vivo estimation of the density of various proteins (often, neuroreceptors) in the brain. PET scanning begins with the injection of a radiolabeled tracer that binds preferentially to the target protein; tracer molecules are then continuously delivered to the brain via the bloodstream. By detecting the radioactive decay of the tracer over time, dynamic PET data are constructed to reflect the concentration of the target protein in the brain at each time. The fundamental problem in the analysis of dynamic PET data involves estimating the impulse response function (IRF), which is necessary for describing the binding behavior of the injected radiotracer. Virtually all existing methods have three common aspects: summarizing the entire IRF with a single scalar measure; modeling each subject separately; and the imposition of parametric restrictions on the IRF. In contrast, we propose a functional data analytic approach that regards each subject’s IRF as the basic analysis unit, models multiple subjects simultaneously, and estimates the IRF nonparametrically. We pose our model as a linear mixed effect model in which population level fixed effects and subject-specific random effects are expanded using a B-spline basis. Shrinkage and roughness penalties are incorporated in the model to enforce identifiability and smoothness of the estimated curves, respectively, while monotonicity and nonnegativity constraints impose biological information on estimates. We illustrate this approach by applying it to clinical PET data with subjects belonging to three diagnosic groups. We explore differences among groups by means of pointwise confidence intervals of the estimated mean curves based on bootstrap samples. Supplementary materials for this article are available online.

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.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Brain and Brown Adipose Tissue Metabolism in Transgenic Tg2576 Mice Models of Alzheimer Disease Assessed Using 18F-FDG PET Imaging.

Objective:Imaging animal models of Alzheimer disease (AD) is useful for the development of therapeutic drugs and understanding AD. Transgenic Swedish hAPPswe Tg2576 mice are a good model of β-amyloid plaques. We report 18F-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET) imaging of brain and intrascapular brown adipose tissue (IBAT) in transgenic mice 2576 (Tg2576) and wild-type (WT) mice.Methods:Transgenic Tg2576 mice and WT mice, >18 months were injected intraperitonally with ≈ 25 to 30 MBq 18F-FDG while awake. After 60 minutes, they were anesthetized with isoflurane (2.5%) and imaged with Inveon MicroPET. Select mice were killed, imaged ex vivo, and 20 µm sections cut for autoradiography. 18F-FDG uptake in brain and IBAT PET and brain autoradiographs were analyzed.Results:Fasting blood glucose levels averaged 120 mg/dL for WT and 100 mg/dL for Tg2576. Compared to WT, Tg2576 mice exhibited a decrease in SUVglc in the various brain regions. Average reductions in the cerebrum regions were as high as −20%, while changes in cerebellum were −3%. Uptake of 18F-FDG in IBAT decreased by −60% in Tg2576 mice and was found to be significant. Intrascapular brown adipose tissue findings in Tg2576 mice are new and not previously reported. Use of blood glucose for PET data analysis and corpus callosum as reference region for autoradiographic analysis were important to detect change in Tg2576 mice.Conclusion:Our results suggest that 18F-FDG uptake in the Tg2576 mice brain show 18F-FDG deficits only when blood glucose is taken into consideration.

PETPVE12: an SPM toolbox for Partial Volume Effects correction in brain PET – Application to amyloid imaging with AV45-PET

Positron emission tomography (PET) allows detecting molecular brain changes in vivo. However, the accuracy of PET is limited by partial volume effects (PVE) that affects quantitative analysis and visual interpretation of the images. Although PVE-correction methods have been shown to effectively increase the correspondence of the measured signal with the true regional tracer uptake, these procedures are still not commonly applied, neither in clinical nor in research settings. Here, we present an implementation of well validated PVE-correction procedures as a SPM toolbox, PETPVE12, for automated processing. We demonstrate its utility by a comprehensive analysis of the effects of PVE-correction on amyloid-sensitive AV45-PET data from 85 patients with Alzheimer's disease (AD) and 179 cognitively normal (CN) elderly. Effects of PVE-correction on global cortical standard uptake value ratios (SUVR) and the power of diagnostic group separation were assessed for the region-wise geometric transfer matrix method (PVEc-GTM), as well as for the 3-compartmental voxel-wise “Müller-Gärtner” method (PVEc-MG). Both PVE-correction methods resulted in decreased global cortical SUVRs in the low to middle range of SUVR values, and in increased global cortical SUVRs at the high values. As a consequence, average SUVR of the CN group was reduced, whereas average SUVR of the AD group was increased by PVE-correction. These effects were also reflected in increased accuracies of group discrimination after PVEc-GTM (AUC=0.86) and PVEc-MG (AUC=0.89) compared to standard non-corrected SUVR (AUC=0.84). Voxel-wise analyses of PVEc-MG corrected data also demonstrated improved detection of regionally increased AV45 SUVR values in AD patients. These findings complement the growing evidence for a beneficial effect of PVE-correction in quantitative analysis of amyloid-sensitive PET data. The novel PETPVE12 toolbox significantly facilitates the application of PVE-correction, particularly within SPM-based processing pipelines. This is expected to foster the use of PVE-correction in brain PET for more widespread use. The toolbox is freely available at http://www.fil.ion.ucl.ac.uk/spm/ext/#PETPVE12.

Evaluation of TSPO PET Ligands [18F]VUIIS1009A and [18F]VUIIS1009B: Tracers for Cancer Imaging.

Positron emission tomography (PET) ligands targeting translocator protein (TSPO) are potential imaging diagnostics of cancer. In this study, we report two novel, high-affinity TSPO PET ligands that are 5,7 regioisomers, [18F]VUIIS1009A ([18F]3A) and [18F]VUIIS1009B ([18F]3B), and their initial in vitro and in vivo evaluation in healthy mice and glioma-bearing rats. VUIIS1009A/B was synthesized and confirmed by X-ray crystallography. Interactions between TSPO binding pocket and novel ligands were evaluated and compared with contemporary TSPO ligands using 2D 1H-15N heteronuclear single quantum coherence (HSQC) spectroscopy. In vivo biodistribution of [18F]VUIIS1009A and [18F]VUIIS1009B was carried out in healthy mice with and without radioligand displacement. Dynamic PET imaging data were acquired simultaneously with [18F]VUIIS1009A/B injections in glioma-bearing rats, with binding reversibility and specificity evaluated by radioligand displacement. In vivo radiometabolite analysis was performed using radio-TLC, and quantitative analysis of PET data was performed using metabolite-corrected arterial input functions. Imaging was validated with histology and immunohistochemistry. Both VUIIS1009A (3A) and VUIIS1009B (3B) were found to exhibit exceptional binding affinity to TSPO, with observed IC50 values against PK11195 approximately 500-fold lower than DPA-714. However, HSQC NMR suggested that VUIIS1009A and VUIIS1009B share a common binding pocket within mammalian TSPO (mTSPO) as DPA-714 and to a lesser extent, PK11195. [18F]VUIIS1009A ([18F]3A) and [18F]VUIIS1009B ([18F]3B) exhibited similar biodistribution in healthy mice. In rats bearing C6 gliomas, both [18F]VUIIS1009A and [18F]VUIIS1009B exhibited greater binding potential (k 3/k 4)in tumor tissue compared to [18F]DPA-714. Interestingly, [18F]VUIIS1009B exhibited significantly greater tumor uptake (V T) than [18F]VUIIS1009A, which was attributed primarily to greater plasma-to-tumor extraction efficiency. The novel PET ligand [18F]VUIIS1009B exhibits promising characteristics for imaging glioma; its superiority over [18F]VUIIS1009A, a regioisomer, appears to be primarily due to improved plasma extraction efficiency. Continued evaluation of [18F]VUIIS1009B as a high-affinity TSPO PET ligand for precision medicine appears warranted.

A Conway–Maxwell–Poisson (CMP) model to address data dispersion on positron emission tomography

Positron emission tomography (PET) in medicine exploits the properties of positron-emitting unstable nuclei. The pairs of γ- rays emitted after annihilation are revealed by coincidence detectors and stored as projections in a sinogram. It is well known that radioactive decay follows a Poisson distribution; however, deviation from Poisson statistics occurs on PET projection data prior to reconstruction due to physical effects, measurement errors, correction of deadtime, scatter, and random coincidences. A model that describes the statistical behavior of measured and corrected PET data can aid in understanding the statistical nature of the data: it is a prerequisite to develop efficient reconstruction and processing methods and to reduce noise.The deviation from Poisson statistics in PET data could be described by the Conway-Maxwell-Poisson (CMP) distribution model, which is characterized by the centring parameter λ and the dispersion parameter ν, the latter quantifying the deviation from a Poisson distribution model. In particular, the parameter ν allows quantifying over-dispersion (ν<1) or under-dispersion (ν>1) of data. A simple and efficient method for λ and ν parameters estimation is introduced and assessed using Monte Carlo simulation for a wide range of activity values.The application of the method to simulated and experimental PET phantom data demonstrated that the CMP distribution parameters could detect deviation from the Poisson distribution both in raw and corrected PET data. It may be usefully implemented in image reconstruction algorithms and quantitative PET data analysis, especially in low counting emission data, as in dynamic PET data, where the method demonstrated the best accuracy.