Positron Emission Tomography Imaging Data Research Articles

Partial volume correction of PET image data using geometric transfer matrices based on uniform B-splines

Objective. Most methods for partial volume correction (PVC) of positron emission tomography (PET) data employ anatomical segmentation of images into regions of interest. This approach is not optimal for exploratory functional imaging beyond regional hypotheses. Here, we describe a novel method for unbiased voxel-wise PVC. Approach. B-spline basis functions were combined with geometric transfer matrices to enable a method (bsGTM) that provides PVC or alternatively provides smoothing with minimal regional crosstalk. The efficacy of the proposed method was evaluated using Monte Carlo simulations, human PET data, and murine functional PET data. Main results. In simulations, bsGTM provided recovery of partial volume signal loss comparable to iterative deconvolution, while demonstrating superior resilience to noise. In a real murine PET dataset, bsGTM yielded much higher sensitivity for detecting amphetamine-induced reduction of [11C]raclopride binding potential. In human PET data, bsGTM smoothing enabled increased signal-to-noise ratios with less degradation of binding potentials relative to Gaussian convolution or non-local means. Significance. bsGTM offers improved performance for PVC relative to iterative deconvolution, the current method of choice for voxel-wise PVC, especially in the common PET regime of low signal-to-noise ratio. The new method provides an anatomically unbiased way to compensate partial volume errors in cases where anatomical segmentation is unavailable or of questionable relevance or accuracy.

Feasibility of noise-reduction reconstruction technology based on non-local-mean principle in SiPM-PET/CT

Quantitative values of positron emission tomography (PET) images using non-local-mean in a silicon photomultiplier (SiPM)-PET/computed tomography (CT) system with phantom and clinical images.The evaluation was conducted on a National Electrical Manufacturers Association body phantom with micro-spheres (4, 5, 6, 8, 10, 13 mm) and clinical images using the SiPM-PET/CT system.The signal-to-background ratio of the phantom was set to 4, and all PET image data was obtained and reconstructed using three-dimensional ordered subset expectation maximization, time-of-flight, point-spread function, and a 4-mm Gaussian filter (GF) and clear adaptive low-noise method (CaLM) in mild, standard, and strong intensities.The evaluation included the standardized uptake value (SUV), percent contrast (QH), coefficient of variation of the background area (CVbackground) clinical imaging for SUV of lung nodules, liver signal-to-noise ratio (SNR), and visual evaluation.SUVmax for 8-mm sphere in phantom images at 2 min for GF and CaLM (mild, standard, strong) were 2.11, 2.32, 2.02, and 1.72; the QH, 8 mm was 27.33 %, 27.47 %, 21.81 %, and 16.09 %; and CVbackground was 12.78, 11.35, 7.86, and 4.71, respectively.CaLM demonstrated higher SUVmax in clinical images than GF for all lung nodule sizes. The average SUVmax for nodules with a diameter of ≤ 1 cm were 5.9 ± 2.4, 9.9 ± 4.9, 9.9 ± 5.0, and 9.9 ± 5.0 for GF and CaLM-mild, standard, and strong intensities, respectively.Liver SNRs were higher for CaLM (mild, standard, strong) compared to GF, with increasing CaLM intensity causing higher liver SNR. CaLM-mild and standard demonstrated suitability for diagnosis in visual evaluation.

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.

Location-Aware Encoding for Lesion Detection in 68Ga-DOTATATE Positron Emission Tomography Images.

Lesion detection with positron emission tomography (PET) imaging is critical for tumor staging, treatment planning, and advancing novel therapies to improve patient outcomes, especially for neuroendocrine tumors (NETs). Current lesion detection methods often require manual cropping of regions/volumes of interest (ROIs/VOIs) a priori, or rely on multi-stage, cascaded models, or use multi-modality imaging to detect lesions in PET images. This leads to significant inefficiency, high variability and/or potential accumulative errors in lesion quantification. To tackle this issue, we propose a novel single-stage lesion detection method using only PET images. We design and incorporate a new, plug-and-play codebook learning module into a U-Net-like neural network and promote lesion location-specific feature learning at multiple scales. We explicitly regularize the codebook learning with direct supervision at the network's multi-level hidden layers and enforce the network to learn multi-scale discriminative features with respect to predicting lesion positions. The network automatically combines the predictions from the codebook learning module and other layers via a learnable fusion layer. We evaluate the proposed method on a real-world clinical 68Ga-DOTATATE PET image dataset, and our method produces significantly better lesion detection performance than recent state-of-the-art approaches. We present a novel deep learning method for single-stage lesion detection in PET imaging data, with no ROI/VOI cropping in advance, no multi-stage modeling and no multi-modality data. This study provides a new perspective for effective and efficient lesion identification in PET, potentially accelerating novel therapeutic regimen development for NETs and ultimately improving patient outcomes including survival.

Imaging Neuroinflammation: Quantification of Astrocytosis in a Multitracer PET Approach.

The recent progress in the development of in vivo biomarkers is rapidly changing how neurodegenerative diseases are conceptualized and diagnosed and how clinical trials are designed today. Alzheimer's disease (AD) - the most common neurodegenerative disorder - is characterized by a complex neuropathology involving the deposition of extracellular amyloid-β (Aβ) plaques and intracellular neurofibrillary tangles (NFTs) of hyperphosphorylated tau proteins, accompanied by the activation of glial cells, i.e., astrocytes and microglia, and neuroinflammatory response, leading to neurodegeneration and cognitive dysfunction. An increasing diversity of positron emission tomography (PET) imaging radiotracers is available to selectively target the different pathophysiological processes of AD. Along with the success of Aβ PET and the more recent tau PET imaging, there is a great interest to develop PET tracers to image glial reactivity and neuroinflammation. While most research to date has focused on imaging microgliosis, there is an upsurge of interest in imaging reactive astrocytes in the AD continuum. There is increasing evidence that reactive astrocytes are morphologically and functionally heterogeneous, with different subtypes that express different markers and display various homeostatic or detrimental roles across disease stages. Therefore, multiple biomarkers are desirable to unravel the complex phenomenon of reactive astrocytosis. In the field of in vivo PET imaging in AD, the research concerning reactive astrocytes has predominantly focused on targeting monoamine oxidase B (MAO-B), most often using either 11C-deuterium-L-deprenyl (11C-DED) or 18F-SMBT-1 PET tracers. Additionally, imidazoline2 binding (I2BS) sites have been imaged using 11C-BU99008 PET. Recent studies in our group using 11C-DED PET imaging suggest that astrocytosis may be present from the early stages of disease development in AD. This chapter provides a detailed description of the practical approach used for the analysis of 11C-DED PET imaging data in a multitracer PET paradigm including 11C-Pittsburgh compound B (11C-PiB) and 18F-fluorodeoxyglucose (18F-FDG). The multitracer PET approach allows investigating the comparative regional and temporal patterns of in vivo brain astrocytosis, fibrillar Aβ deposition, glucose metabolism, and brain structural changes. It may also contribute to understanding the potential role of novel plasma biomarkers of reactive astrocytes, in particular the glial fibrillary acidic protein (GFAP), at different stages of disease progression. This chapter attempts to stimulate further research in the field, including the development of novel PET tracers that may allow visualizing different aspects of the complex astrocytic and microglial response in neurodegenerative diseases. Progress in the field will contribute to the incorporation of PET imaging of glial reactivity and neuroinflammation as biomarkers with clinical application and motivate further investigation on glial cells as therapeutic targets in AD and other neurodegenerative diseases.

BIOGUIDE-X: A First-in-Human Study of the Performance of Positron Emission Tomography-Guided Radiation Therapy

Positron emission tomography (PET)-guided radiation therapy is a novel tracked dose delivery modality that uses real-time PET to guide radiation therapy beamlets. The BIOGUIDE-X study was performed with sequential cohorts of participants to (1) identify the fluorodeoxyglucose (FDG) dose for PET-guided therapy and (2) confirm that the emulated dose distribution was consistent with a physician-approved radiation therapy plan. This prospective study included participants with at least 1 FDG-avid targetable primary or metastatic tumor (2-5 cm) in the lung or bone. For cohort I, a modified 3 + 3 design was used to determine the FDG dose that would result in adequate signal for PET-guided therapy. For cohort II, PET imaging data were collected on the X1 system before the first and last fractions among patients undergoing conventional stereotactic body radiation therapy. PET-guided therapy dose distributions were modeled on the patient's computed tomography anatomy using the collected PET data at each fraction as input to an "emulated delivery" and compared with the physician-approved plan. Cohort I demonstrated adequate FDG activity in 6 of 6 evaluable participants (100.0%) with the first injected dose level of 15 mCi FDG. In cohort II, 4 patients with lung tumors and 5 with bone tumors were enrolled, and evaluable emulated delivery data points were collected for 17 treatment fractions. Sixteen of the 17 emulated deliveries resulted in dose distributions that were accurate with respect to the approved PET-guided therapy plan. The 17th data point was just below the 95% threshold for accuracy (dose-volume histogram score = 94.6%). All emulated fluences were physically deliverable. No toxicities were attributed to multiple FDG administrations. PET-guided therapy is a novel radiation therapy modality in which a radiolabeled tumor can act as its own fiducial for radiation therapy targeting. Emulated therapy dose distributions calculated from continuously acquired real-time PET data were accurate and machine-deliverable in tumors that were 2 to 5 cm in size with adequate FDG signal characteristics.

Towards a standardized pipeline for tau PET analysis under a GAAIN/LONI and CPAD collaborative framework

AbstractBackgroundImaging tau burden in vivo using Positron Emission Tomography (PET) has tremendous potential for advancing our biological understanding of Alzheimer’s disease. Towards this direction, there is a need for post‐acquisition imaging data ‘harmonization’ tools and analysis methods that reduce variance across studies, sites, and scanners, while retaining the power to study complex disease related effects. The Global Alzheimer’s Association Interactive Network (GAAIN), the Laboratory of Neuro Imaging (LONI), and the Critical Path for Alzheimer’s Disease (CPAD) Consortium have undertaken a collaborative effort to provide a harmonized approach to the analysis of tau PET images.MethodThe GAAIN/LONI Pipeline is a software suite that allows the design and execution of neuroimaging workflows through flexible integration of different image preprocessing software and parallel processing via a computer grid. Each workflow has a distinct fingerprint that can be executed locally on different platforms or remotely via GAAIN, thus increasing interoperability and allowing the use of “harmonized” workflows across different datasets. Tau PET workflows were designed and deployed on the GAAIN/LONI Pipeline based on existing literature. In turn, these tau PET workflows were executed on CPAD tau PET to derive tau PET burden. Clinical validation was performed by quantifying tau PET burden on different clinical groups. Analytical validation was performed by correlating the LONI Pipeline results with independently developed pipelines.ResultWe first show feasibility results as the tau PET workflows are deployed and executed on CPAD data. For clinical validation, we tested whether derived metrics can distinguish between clinical groups. For analytical validation we tested correlations of results with existing methods that have been independently developed. These results provide a unified analysis model that can be utilized from GAAIN/LONI and CPAD to conduct harmonized analyses on multiple datasets. The full capabilities of this model will be explored in subsequent work.ConclusionThe proposed pipeline, which is going to be part of the publicly available GAAIN/LONI platform, should provide researchers with an optimized roadmap that describes how tau PET imaging data should be processed and analyzed for reliability and reproducibility in the context of drug development and regulatory decision making.

Combination of tumor asphericity and an extracellular matrix-related prognostic gene signature in non-small cell lung cancer patients

One important aim of precision oncology is a personalized treatment of patients. This can be achieved by various biomarkers, especially imaging parameters and gene expression signatures are commonly used. So far, combination approaches are sparse. The aim of the study was to independently validate the prognostic value of the novel positron emission tomography (PET) parameter tumor asphericity (ASP) in non small cell lung cancer (NSCLC) patients and to investigate associations between published gene expression profiles and ASP. This was a retrospective evaluation of PET imaging and gene expression data from three public databases and two institutional datasets. The whole cohort comprised 253 NSCLC patients, all treated with curative intent surgery. Clinical parameters, standard PET parameters and ASP were evaluated in all patients. Additional gene expression data were available for 120 patients. Univariate Cox regression and Kaplan–Meier analysis was performed for the primary endpoint progression-free survival (PFS) and additional endpoints. Furthermore, multivariate cox regression testing was performed including clinically significant parameters, ASP, and the extracellular matrix-related prognostic gene signature (EPPI). In the whole cohort, a significant association with PFS was observed for ASP (p < 0.001) and EPPI (p = 0.012). Upon multivariate testing, EPPI remained significantly associated with PFS (p = 0.018) in the subgroup of patients with additional gene expression data, while ASP was significantly associated with PFS in the whole cohort (p = 0.012). In stage II patients, ASP was significantly associated with PFS (p = 0.009), and a previously published cutoff value for ASP (19.5%) was successfully validated (p = 0.008). In patients with additional gene expression data, EPPI showed a significant association with PFS, too (p = 0.033). The exploratory combination of ASP and EPPI showed that the combinatory approach has potential to further improve patient stratification compared to the use of only one parameter. We report the first successful validation of EPPI and ASP in stage II NSCLC patients. The combination of both parameters seems to be a very promising approach for improvement of risk stratification in a group of patients with urgent need for a more personalized treatment approach.

Imaging Performance of the PET Scan on a Novel Ring Gantry-Based PET/CT Linear Accelerator System in the First-in-Human Study of Biology-Guided Radiotherapy

Biology-guided radiotherapy (BgRT) is a novel tracked dose delivery modality using real-time positron emission tomography (PET) to guide radiotherapy beamlets. The present study was performed with sequential cohorts of participants to evaluate the performance and safety of BgRT. Primary endpoints were previously reported. We hereby report on one of the secondary endpoints assessing a novel treatment planning machine with integrated dual kVCT/PET imaging ("novel device") performance in comparison to a third-party diagnostic PET/CT scan. This single-arm, open-label, prospective study included participants with at least 1 FDG-avid targetable primary or metastatic tumor (≥2cm and ≤5cm) in the lung or bone. PET imaging data were collected on the novel device and on a third-party diagnostic PET/CT performed in sequence once at the planning timepoint in Cohort I, and immediately before the last fraction among patients undergoing stereotactic radiotherapy in Cohort II. Three central read radiation oncologists (CRRO) provided an interpretation of the novel device PET scans which were compared to an agreement standard based on 3 central radiologists' review of the paired diagnostic PET/CT scan. Positive percent agreement for localization of the target tumor within the biology-tracking zone (BTZ) was the key metric because it reflects whether advancing patients to subsequent steps in the BgRT workflow based on the novel device's imaging was ultimately appropriate. In Cohort 1, 6 image comparisons were performed. The positive (%) agreement for the aggregate radiation oncologist review was 100% (5/5), reflecting that in all 5 cases where the aggregate radiation oncologists deemed the tumor to fall within the BTZ based upon the novel device PET images, the central radiologists came to the same conclusion upon review of the paired diagnostic PET/CT images. The overall (%) agreement for the aggregate radiation oncologist review was 83.3% (5/6): localization was not established on the novel device in 1 case, even though it was established on the diagnostic PET/CT. This would not pose risk in real world practice as BgRT candidacy would be aborted for tumors not visible on the novel device. In Cohort II, among the 7 image comparisons, there was 100% positive percent agreement between the aggregate CRRO and the agreement standard as the localization criteria was met in both scans for all 7 patients. This was concordant with a 100% overall percent agreement. This investigation demonstrated a 100% positive percent agreement between central review of this novel device images by radiation oncologists and central review of the accompanying third-party PET/CT images by radiologists. There were no cases where a positive localization by the aggregate CRRO was not confirmed by the third-party PET/CT standard, providing evidence against the likelihood of falsely positive localizations on the novel device that would inappropriately advance patients in the workflow.

Feasibility of Simultaneous Quantification of Myocardial and Renal Perfusion With Cardiac Positron Emission Tomography.

Given the central importance of cardiorenal interactions, mechanistic tools for evaluating cardiorenal physiology are needed. In the heart and kidneys, shared pathways of neurohormonal activation, hypertension, and vascular and interstitial fibrosis implicate the relevance of systemic vascular health. The availability of a long axial field of view positron emission tomography (PET)/computed tomography (CT) system enables simultaneous evaluation of cardiac and renal blood flow. This study evaluated the feasibility of quantification of renal blood flow using data acquired during routine, clinically indicated 13N-ammonia myocardial perfusion PET/CT. Dynamic PET image data were used to calculate renal blood flow. Reproducibility was assessed by the intraclass correlation coefficient among 3 independent readers. PET-derived renal blood flow was correlated with imaging and clinical parameters in the overall cohort and with histopathology in a small companion study of patients with a native kidney biopsy. Among 386 consecutive patients with myocardial perfusion PET/CT, 296 (76.7%) had evaluable images to quantify renal perfusion. PET quantification of renal blood flow was highly reproducible (intraclass correlation coefficient 0.98 [95% CI, 0.93-0.99]) and was correlated with the estimated glomerular filtration rate (r=0.64; P<0.001). Compared across vascular beds, resting renal blood flow was correlated with maximal stress myocardial blood flow and myocardial flow reserve (stress/rest myocardial blood flow), an integrated marker of endothelial health. In patients with kidney biopsy (n=12), resting PET renal blood flow was strongly negatively correlated with histological interstitial fibrosis (r=-0.85; P<0.001). Renal blood flow can be reliably measured from cardiac 13N-ammonia PET/CT and allows for simultaneous assessment of myocardial and renal perfusion, opening a potential novel avenue to interrogate the mechanisms of emerging therapies with overlapping cardiac and renal benefits.

Clinical validation of translational antibody PBPK model using tissue distribution data generated with 89Zr-immuno-PET imaging.

The main objective of this manuscript was to validate the ability of the monoclonal antibody physiologically-based pharmacokinetic (PBPK) model to predict tissue concentrations of antibodies in the human. To accomplish this goal, preclinical and clinical tissue distribution and positron emission tomography imaging data generated using zirconium-89 (89Zr) labeled antibodies were obtained from the literature. First, our previously published translational PBPK model for antibodies was expanded to describe the whole-body biodistribution of 89Zr labeled antibody and the free 89Zr, as well as residualization of free 89Zr. Subsequently, the model was optimized using mouse biodistribution data, where it was observed that free 89Zr mainly residualizes in the bone and the extent of antibody distribution in certain tissues (e.g., liver and spleen) may be altered by labeling with 89Zr. The mouse PBPK model was scaled to rat, monkey, and human by simply changing the physiological parameters, and a priori simulations performed by the model were compared with the observed PK data. It was found that model predicted antibody PK in majority of the tissues in all the species superimposed over the observed data, and the model was also able to predict the PK of antibody in human tissues reasonably well. As such, the work presented here provides unprecedented evaluation of the antibody PPBK model for its ability to predict tissue PK of antibodies in the clinic. This model can be used for preclinical-to-clinical translation of antibodies and for prediction of antibody concentrations at the site-of-action in the clinic.

Impact of list-mode reconstruction and image-space point spread function correction on PET image contrast and quantitative value using SiPM-based PET/CT system.

We evaluate the effects of list-mode reconstruction and the image-space point spread function (iPSF) on the contrast and quantitative values of positron emission tomography (PET) images using a SiPM-PET/CT system. The evaluation is conducted on an NEMA body phantom and clinical images using a Cartesion Prime SiPM-PET/CT system. The signal-to-background ratio (SBR) of the phantom is set to 2, 4, 6, and 8, and all the PET image data are obtained and reconstructed using 3D-OSEM, time-of-flight, iPSF (-/ +), and a 4-mm Gaussian filter with several iterations. The evaluation criteria include % background variability (NB,10mm), % contrast (QH,10mm), iPSF change in QH,10mm (ΔQH,10mm) for edge artifact evaluation, profile curves, visual evaluation of edge artifacts, clinical imaging for the standardized uptake value (SUV) of lung nodules, and SNRliver. NB,10mm demonstrates no significant difference in all SBRs with and without iPSF, whereas QH,10mm is higher based on the SBR with and without iPSF. ΔQH,10mm indicates increased iterations and a larger rate of change (> 5%) for small spheres of < 17mm. The profile curves portrayed almost real concentrations, except for the 10-mm sphere of SBR2 without iPSF; however, with iPSF, an overshoot was observed in the 13-mm sphere of all SBRs. The degree of overshoot increased with increasing iteration and SBR. Edge artifacts were detected at values ≥ 17-22mm in SBRs other than SBR2 with iPSF. Irrespective of the nodal size, SUV and SNRliver improved considerably after iPSF adjustment. Therefore, the effects of list-mode reconstruction and iPSF on PET image contrast were limited, and the overcorrection of the quantitative values was validated using iPSF.

Permutation-based inference for function-on-scalar regression with an application in PET brain imaging

The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data analysis is most commonly performed on data that are aggregated into several discrete a priori regions of interest, in this study, primary interest is on measures of 5-HTT binding potential that are made at many locations along a continuous anatomically defined tract, one that was chosen to follow serotonergic axons. Our goal is to characterise the binding patterns along this tract and also to determine how such patterns differ between control subjects and depressed patients. Due to the nature of our data, we utilise function-on-scalar regression modelling to make optimal use of our data. Inference on both main effects (position along the tract; diagnostic group) and their interactions are made using permutation testing strategies that do not require distributional assumptions. Also, to investigate the question of homogeneity we implement a permutation testing strategy, which adapts a ‘block bootstrapping’ approach from time series analysis to the functional data setting.

The MAPT p.P301L mutation presents as a rare early-onset corticobasal syndrome: A case report

Corticobasal syndrome (CBS) is an uncommon movement disorder with a heterogeneous clinical presentation. CBS is generally recognized as a sporadic disorder, although rare familial and isolated genetic cases have been reported among Westerners. We describe a 49-year-old Taiwanese woman that presented with a three-year history of progressive right-hand dystonia, akinetic-rigidity, ideomotor apraxia, and slowness of gait. Her family history included autosomal-dominant parkinsonism with cognitive decline. A genetic analysis revealed a pathogenic heterozygous missense variant, c.902C&gt;T (p.P301L), on exon 10 of the MAPT gene. The antemortem diagnosis of CBS was supported by clinical, structural, brain glucose metabolism, and tau protein molecular positron emission tomography imaging data. These findings expanded the phenotypic spectrum of the MAPT p.P301L mutation. A literature review illustrated the genetic pleiotropy of MAPT mutations in tau-related neurodegenerative disorders. This study was the first to describe a patient in an Asian family with a MAPT mutation that presented as young-onset CBS. Our findings demonstrated the heterogenous phenotypic spectrum of this rare genetic variant. MAPT mutations should be considered in patients with early-onset or familial CBS in the Asian population.

Sex-specific radiomic features of L-[S-methyl-11C] methionine PET in patients with newly-diagnosed gliomas in relation to IDH1 predictability.

Amino-acid positron emission tomography (PET) is a validated metabolic imaging approach for the diagnostic work-up of gliomas. This study aimed to evaluate sex-specific radiomic characteristics of L-[S-methyl-11Cmethionine (MET)-PET images of glioma patients in consideration of the prognostically relevant biomarker isocitrate dehydrogenase (IDH) mutation status. MET-PET of 35 astrocytic gliomas (13 females, mean age 41 ± 13 yrs. and 22 males, mean age 46 ± 17 yrs.) and known IDH mutation status were included. All patients underwent radiomic analysis following imaging biomarker standardization initiative (IBSI)-conform guidelines both from standardized uptake value (SUV) and tumor-to-background ratio (TBR) PET values. Aligned Monte Carlo (MC) 100-fold split was utilized for SUV and TBR dataset pairs for both sex and IDH-specific analysis. Borderline and outlier scores were calculated for both sex and IDH-specific MC folds. Feature ranking was performed by R-squared ranking and Mann-Whitney U-test together with Bonferroni correction. Correlation of SUV and TBR radiomics in relation to IDH mutational status in male and female patients were also investigated. There were no significant features in either SUV or TBR radiomics to distinguish female and male patients. In contrast, intensity histogram coefficient of variation (ih.cov) and intensity skewness (stat.skew) were identified as significant to predict IDH +/-. In addition, IDH+ females had significant ih.cov deviation (0.031) and mean stat.skew (-0.327) differences compared to IDH+ male patients (0.068 and -0.123, respectively) with two-times higher standard deviations of the normal brain background MET uptake as well. We demonstrated that female and male glioma patients have significantly different radiomic profiles in MET PET imaging data. Future IDH prediction models shall not be built on mixed female-male cohorts, but shall rely on sex-specific cohorts and radiomic imaging biomarkers.

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

BackgroundSimultaneous dual-tracer positron emission tomography (PET) imaging can observe two molecular targets in a single scan, which is conducive to disease diagnosis and tracking. Since the signals emitted by different tracers are the same, it is crucial to separate each single tracer from the mixed signals. The current study proposed a novel deep learning-based method to reconstruct single-tracer activity distributions from the dual-tracer sinogram.MethodsWe proposed the Multi-task CNN, a three-dimensional convolutional neural network (CNN) based on a framework of multi-task learning. One common encoder extracted features from the dual-tracer dynamic sinogram, followed by two distinct and parallel decoders which reconstructed the single-tracer dynamic images of two tracers separately. The model was evaluated by mean squared error (MSE), multiscale structural similarity (MS-SSIM) index and peak signal-to-noise ratio (PSNR) on simulated data and real animal data, and compared to the filtered back-projection method based on deep learning (FBP-CNN).ResultsIn the simulation experiments, the Multi-task CNN reconstructed single-tracer images with lower MSE, higher MS-SSIM and PSNR than FBP-CNN, and was more robust to the changes in individual difference, tracer combination and scanning protocol. In the experiment of rats with an orthotopic xenograft glioma model, the Multi-task CNN reconstructions also showed higher qualities than FBP-CNN reconstructions.ConclusionsThe proposed Multi-task CNN could effectively reconstruct the dynamic activity images of two single tracers from the dual-tracer dynamic sinogram, which was potential in the direct reconstruction for real simultaneous dual-tracer PET imaging data in future.

Blood immune response genes are correlated with brain glucose metabolism

AbstractBackgroundImaging techniques such as positron emission tomography (PET) have been fundamental to develop our understanding about Alzheimer's disease (AD) underlying features. Evidence suggests that neuroinflammation plays a complex role in the pathophysiology of AD. Nevertheless, whether peripheral inflammation affects brain function remains to be defined. In this context, the integration of blood‐based measures and imaging modalities can facilitate the understanding of the periphery‐brain interface in AD. Here, we developed a method to integrate immune response‐related genes, derived from blood transcriptomics, with [18F] Fluorodeoxyglucose ([18F]FDG)‐PET imaging data. We hypothesized that the differential expression of immune‐related genes would be associated with brain metabolism as indexed by [18F]FDG‐PET signal.MethodBlood transcriptome profiles and imaging data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Differential gene expression analysis was performed in 99 Cognitively Unimpaired (CU) and 218 Cognitively Impaired (CI) individuals, whose differentially expressed genes (DEGs) underwent functional enrichment analysis Gene Ontology (GO). Immune‐related GO terms were clustered by semantic similarity using the GOSemSim method for biological processes ontology. All computations analysis were performed in the R statistical environment. Integration with [18F]FDG‐PET images was performed using voxel generalized linear regression (GLR) models considering the covariates age, gender, education, and APOE ε4 status (RMINC package).ResultsPeripheral blood cells transcriptome analysis identified 10 enriched GO terms associated with the immune response. For each term, the GLR models were used to calculate voxel‐wise associations between [18F]FDG and gene expression, resulting in a t‐statistical map. Figure 1 demonstrated the uncorrected statistically significant correlated voxels (t‐value &gt; 2.0). At the voxel level, biological processes related to regulatory mechanisms of innate immunity presented a 32% Correspondence with [18F]FDG‐PET signal in the gray matter of the medial frontal‐orbital gyrus.ConclusionHere, we demonstrated a significant association between brain glucose metabolism and immune‐related blood DEGs. Our findings suggest that peripheral inflammation that occurs in AD patients might have an impact on [18F]FDG‐PET metabolism.

Blood protein kinase activity regulating genes are associated with brain glucose metabolism

AbstractBackgroundThe diagnosis of Alzheimer’s disease (AD) has been greatly improved due to the fundamental role of positron emission tomography (PET) imaging. Also, predicting PET brain imaging alterations using blood‐based biomarkers is of high interest. This way, integrating PET and omics data can provide new insights into AD pathophysiology. Here, we aimed to develop a framework that combines blood transcriptomics with PET data. We hypothesized that integrating omics and PET data will help advance our understanding of AD neurobiology and may reveal relevant new peripheral biomarkers.Method[18F]Fluorodeoxyglucose ([18F]FDG)‐PET imaging and transcriptomics data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Microarray gene expression profiling from blood samples of 99 Cognitively Unimpaired (CU) and 218 Cognitively Impaired (CI) individuals were submitted to differential expression (DE) analysis. Differentially expressed genes (DEGs) were submitted to Gene Ontology (GO) analysis. The GO terms were clustered by semantic similarity using the GOSemSim method for biological processes ontology. All computations analyses were performed in the R statistical environment. Gene clusters obtained in the previous step were selected to undergo integration with [18F]FDG‐PET images using voxel‐wise generalized linear regression (GLR) models adjusted for age, gender, years of education, and APOEε4 (RMINC package).ResultThe GO semantic similarity resulted in 16 GO clusters. The voxel‐wise correlation between [18F]FDG‐PET and GO clusters resulted in t‐statistical maps. Afterwards, only statistically significant correlated voxels (uncorrected t‐value &gt; 2.0) were retained (Fig. 1A). A cluster related to the regulation of protein serine/threonine kinase activity showed a strong correlation with the [18F]FDG‐PET signal in the brain. At the voxel level, this cluster has a positive correlation in the precuneus’ gray matter (95.7% left, 81.1% right) as well in the medial frontal gyrus (79.6% left, 44.66% right) and cingulate region (76.9% left, 56.4% right) (Fig. 1B).ConclusionWe identified several peripheral biological processes associated with [18F]FDG‐PET metabolism in the brain of CU and CI individuals. Furthermore, the prominent enrichment of protein serine/threonine kinase activity‐related genes highlights its potential as novel AD biomarkers and as a proxy of [18F]FDG‐PET metabolism.

Molecular Imaging of Matrix Metalloproteinase-2 in Atherosclerosis Using a Smart Multifunctional PET/MRI Nanoparticle.

Matrix metalloproteinases from macrophages are important intraplaque components that play pivotal roles in plaque progression and regression. This study sought to develop a novel multifunctional positron emission tomography (PET) and magnetic resonance imaging (MRI) contrast agents based on MMP-2 cleavable nanoparticles to noninvasive assessment of MMP-2 activity in mouse carotid atherosclerotic plaques. Macrophage-rich vascular lesions were induced by carotid ligation plus high-fat diet and streptozotocin-induced diabetes in CL57/BL6 mice. To render iron oxide nanoparticles (IONP) specific for the extracellular MMP-2, the magnetic nanoparticle base materialhas been derivatized with 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) for the nuclear tracer 64Cu labeling and the MMP-2-cleavable peptide modified with polyethylene glycol 2000, yielding a multi-modality reporter (64Cu-NOTA-IONP@MMP2c-PEG2K, MMP2cNPs) for PET/MR imaging. Small animal PET imaging and biodistribution data revealed that MMP2cNPs exhibited remarkable plaque uptake (3.06 ± 0.87% ID/g and 1.83 ± 0.28% ID/g at 4 and 12 h, respectively). And MMP2cNPs were rapidly cleared from the contralateral normal carotid artery, resulting in excellent plaque-to-normal carotid artery contrasts. Furthermore, in vivo MRI showed a preferential accumulation of MMP2cNPs in atherosclerotic lesions compared with the non-cleavable reference compound, MMP2ncNPs. In addition, histological analyses revealed iron accumulations in the carotid atherosclerotic plaque, in colocalization with MMP-2 expression and macrophages. Using a combination of innovative imaging modalities, in this study, we demonstrate the feasibility of applying the novel smart MMP2cNPs as a PET/MR hybrid imaging contrast agent for detection of MMP-2 in atherosclerotic plaque in vivo.

Identification of pre-synaptic density networks using [11C]UCB-J PET imaging and ICA in mice

BackgroundSynaptic vesicle glycoprotein 2A (SV2A) is a vesicle glycoprotein involved in neurotransmitter release. SV2A is located on the pre-synaptic terminals of neurons and visualized using the radioligand [11C]UCB-J and positron emission tomography (PET) imaging. Thus, SV2A PET imaging can provide a proxy for pre-synaptic density in health and disease. This study aims to apply independent component analysis (ICA) to SV2A PET data acquired in mice to identify pre-synaptic density networks (pSDNs), explore how ageing affects these pSDNs, and determine the impact of a neurological disorder on these networks. MethodsWe used [11C]UCB-J PET imaging data (n = 135) available at different ages (3, 7, 10, and 16 months) in wild-type (WT) C57BL/6J mice and in diseased mice (mouse model of Huntington's disease, HD) with reported synaptic deficits. First, ICA was performed on a healthy dataset after it was split into two equal-sized samples (n = 36 each) and the analysis was repeated 50 times in different partitions. We tested different model orders (8, 12, and 16) and identified the pSDNs. Next, we investigated the effect of age on the loading weights of the identified pSDNs. Additionally, the identified pSDNs were compared to those of diseased mice to assess the impact of disease on each pSDNs. ResultsModel order 12 resulted in the preferred choice to provide six reliable and reproducible independent components (ICs) as supported by the cluster-quality index (IQ) and regression coefficients (β) values. Temporal analysis showed age-related statistically significant changes on the loading weights in four ICs. ICA in an HD model revealed a statistically significant disease-related effect on the loading weights in several pSDNs in line with the progression of the disease. ConclusionThis study validated the use of ICA on SV2A PET data acquired with [11C]UCB-J for the identification of cerebral pre-synaptic density networks in mice in a rigorous and reproducible manner. Furthermore, we showed that different pSDNs change with age and are affected in a disease condition. These findings highlight the potential value of ICA in understanding pre-synaptic density networks in the mouse brain.