Improved kinetic analysis of dynamic PET data with optimized HYPR-LR

The role of [18 F]FDOPA PET as an adjunct to conventional MRI in the diagnosis of aggressive glial lesions

BACKGROUND

Amino acid PET is recommended for the initial diagnosis of brain lesions, but its value for identifying aggressive lesions remains to be established. The current study therefore evaluates the added-value of dynamic [18 F]FDOPA PET as an adjunct to conventional MRI for determining the aggressiveness of presumed glial lesions at diagnosis.

METHODS

Consecutive patients, with a minimal 1 year-follow-up, underwent contrast-enhanced MRI (CE MRI) and dynamic [18 F]FDOPA PET to characterize their suspected glial lesion. Lesions were classified semi-automatically by their CE MRI (MRI-/+), and PET parameters (static tumor-to-background ratio, TBR; dynamic time-to-peak ratio, TTPratio). Diagnostic accuracies of MRI and PET parameters for the differentiation of tumor aggressiveness were evaluated by chi-square test or receiver operating characteristic analyses. Aggressive lesions were either defined as lesions with dismal molecular characteristics based on the WHO 2021 classification of brain tumors or with compatible clinico-radiological profiles. Time-to-treatment failure (TTF) and overall survival (OS) were evaluated.

RESULTS

Of the 109 patients included, 46 had aggressive lesions (45 confirmed by histo-molecular analyses). CE MRI identified aggressive lesions with an accuracy of 73%. TBRmax (threshold of 3.2), and TTPratio (threshold of 5.4 min) respectively identified aggressive lesions with an accuracy of 83% and 76% and were independent of CE MRI and clinical factors in the multivariate analysis. Among the MRI-lesions, 11/56 (20%) were aggressive and respectively 55% and 50% of these aggressive lesions showed high TBRmax and short TTPratio in PET. High TBRmax and short TTPratio in PET were significantly associated to poorer survivals (p ≤ 0.009).

CONCLUSION

Dynamic [18 F]FDOPA PET provides a similar diagnostic accuracy as contrast enhancement in MRI to identify the aggressiveness of suspected glial lesions at diagnosis. Both methods, however, are complementary and [18 F]FDOPA PET may be a useful additional tool in equivocal cases.

Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma

This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[18F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744-0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716-0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548-0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512-0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts.

Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PET

BACKGROUND

Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data.

METHODS

Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach.

RESULTS

Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns.

CONCLUSIONS

A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks

Objective. Reducing dose in positron emission tomography (PET) imaging increases noise in reconstructed dynamic frames, which inevitably results in higher noise and possible bias in subsequently estimated images of kinetic parameters than those estimated in the standard dose case. We report the development of a spatiotemporal denoising technique for reduced-count dynamic frames through integrating a cascade artificial neural network (ANN) with the highly constrained back-projection (HYPR) scheme to improve low-dose parametric imaging.Approach. We implemented and assessed the proposed method using imaging data acquired with11C-UCB-J, a PET radioligand bound to synaptic vesicle glycoprotein 2A (SV2A) in the human brain. The patch-based ANN was trained with a reduced-count frame and its full-count correspondence of a subject and was used in cascade to process dynamic frames of other subjects to further take advantage of its denoising capability. The HYPR strategy was then applied to the spatial ANN processed image frames to make use of the temporal information from the entire dynamic scan.Main results. In all the testing subjects including healthy volunteers and Parkinson's disease patients, the proposed method reduced more noise while introducing minimal bias in dynamic frames and the resulting parametric images, as compared with conventional denoising methods.Significance. Achieving 80% noise reduction with a bias of -2% in dynamic frames, which translates into 75% and 70% of noise reduction in the tracer uptake (bias, -2%) and distribution volume (bias, -5%) images, the proposed ANN+HYPR technique demonstrates the denoising capability equivalent to a 11-fold dose increase for dynamic SV2A PET imaging with11C-UCB-J.

Implementing the Point Spread Function Deconvolution for Better Molecular Characterization of Newly Diagnosed Gliomas: A Dynamic 18F-FDOPA PET Radiomics Study

Purpose: This study aims to investigate the effects of applying the point spread function deconvolution (PSFd) to the radiomics analysis of dynamic L-3,4-dihydroxy-6-[18F]-fluoro-phenyl-alanine (18F-FDOPA) positron emission tomography (PET) images, to non-invasively identify isocitrate dehydrogenase (IDH) mutated and/or 1p/19q codeleted gliomas. Methods: Fifty-seven newly diagnosed glioma patients underwent dynamic 18F-FDOPA imaging on the same digital PET system. All images were reconstructed with and without PSFd. An L1-penalized (Lasso) logistic regression model, with 5-fold cross-validation and 20 repetitions, was trained with radiomics features extracted from the static tumor-to-background-ratio (TBR) and dynamic time-to-peak (TTP) parametric images, as well as a combination of both. Feature importance was assessed using Shapley additive explanation values. Results: The PSFd significantly modified 95% of TBR, but only 79% of TTP radiomics features. Applying the PSFd significantly improved the ability to identify IDH-mutated and/or 1p/19q codeleted gliomas, compared to PET images not processed with PSFd, with respective areas under the curve of 0.83 versus 0.79 and 0.75 versus 0.68 for a combination of static and dynamic radiomics features (p < 0.001). Without the PSFd, four and eight radiomics features contributed to 50% of the model for detecting IDH-mutated and/or 1p/19q codeleted gliomas, respectively. Application of the PSFd reduced this to three and seven contributive radiomics features. Conclusion: Application of the PSFd to dynamic 18F-FDOPA PET imaging significantly improves the detection of molecular parameters in newly diagnosed gliomas, most notably by modifying TBR radiomics features.

Unsupervised PET logan parametric image estimation using conditional deep image prior

Recently, deep learning-based denoising methods have been gradually used for PET images denoising and have shown great achievements. Among these methods, one interesting framework is conditional deep image prior (CDIP) which is an unsupervised method that does not need prior training or a large number of training pairs. In this work, we combined CDIP with Logan parametric image estimation to generate high-quality parametric images. In our method, the kinetic model is the Logan reference tissue model that can avoid arterial sampling. The neural network was utilized to represent the images of Logan slope and intercept. The patient's computed tomography (CT) image or magnetic resonance (MR) image was used as the network input to provide anatomical information. The optimization function was constructed and solved by the alternating direction method of multipliers (ADMM) algorithm. Both simulation and clinical patient datasets demonstrated that the proposed method could generate parametric images with more detailed structures. Quantification results showed that the proposed method results had higher contrast-to-noise (CNR) improvement ratios (PET/CT datasets: 62.25%±29.93%; striatum of brain PET datasets : 129.51%±32.13%, thalamus of brain PET datasets: 128.24%±31.18%) than Gaussian filtered results (PET/CT datasets: 23.33%±18.63%; striatum of brain PET datasets: 74.71%±8.71%, thalamus of brain PET datasets: 73.02%±9.34%) and nonlocal mean (NLM) denoised results (PET/CT datasets: 37.55%±26.56%; striatum of brain PET datasets: 100.89%±16.13%, thalamus of brain PET datasets: 103.59%±16.37%).

Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes

This study evaluates the relevance of ¹⁸F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic ¹⁸F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features-radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both-in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBR_mean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values.

Dynamic PET image reconstruction incorporating a median nonlocal means kernel method

In dynamic positron emission tomography (PET) imaging, the reconstructed image of a single frame often exhibits high noise due to limited counting statistics of projection data. This study proposed a median nonlocal means (MNLM)-based kernel method for dynamic PET image reconstruction. The kernel matrix is derived from median nonlocal means of pre-reconstructed composite images. Then the PET image intensities in all voxels were modeled as a kernel matrix multiplied by coefficients and incorporated into the forward model of PET projection data. Then, the coefficients of each feature were estimated by the maximum likelihood method. Using simulated low-count dynamic data of Zubal head phantom, the quantitative performance of the proposed MNLM kernel method was investigated and compared with the maximum-likelihood method, conventional kernel method with and without median filter, and nonlocal means (NLM) kernel method. Simulation results showed that the MNLM kernel method achieved visual and quantitative accuracy improvements (in terms of the ensemble mean squared error, bias versus variance, and contrast versus noise performances). Especially for frame 2 with the lowest count level of a single frame, the MNLM kernel method achieves lower ensemble mean squared error (10.43%) than the NLM kernel method (13.68%), conventional kernel method with and without median filter (11.88% and 23.50%), and MLEM algorithm (24.77%). The study on real low-dose ¹⁸F-FDG rat data also showed that the MNLM kernel method outperformed other methods in visual and quantitative accuracy improvements (in terms of regional noise versus intensity mean performance).

Dynamic PET image reconstruction utilizing intrinsic data-driven HYPR4D denoising kernel

PURPOSE

Reconstructed PET images are typically noisy, especially in dynamic imaging where the acquired data are divided into several short temporal frames. High noise in the reconstructed images translates to poor precision/reproducibility of image features. One important role of "denoising" is therefore to improve the precision of image features. However, typical denoising methods achieve noise reduction at the expense of accuracy. In this work, we present a novel four-dimensional (4D) denoised image reconstruction framework, which we validate using 4D simulations, experimental phantom, and clinical patient data, to achieve 4D noise reduction while preserving spatiotemporal patterns/minimizing error introduced by denoising.

METHODS

Our proposed 4D denoising operator/kernel is based on HighlY constrained backPRojection (HYPR), which is applied either after each update of OSEM reconstruction of dynamic 4D PET data or within the recently proposed kernelized reconstruction framework inspired by kernel methods in machine learning. Our HYPR4D kernel makes use of the spatiotemporal high frequency features extracted from a 4D composite, generated within the reconstruction, to preserve the spatiotemporal patterns and constrain the 4D noise increment of the image estimate.

RESULTS

Results from simulations, experimental phantom, and patient data showed that the HYPR4D kernel with our proposed 4D composite outperformed other denoising methods, such as the standard OSEM with spatial filter, OSEM with 4D filter, and HYPR kernel method with the conventional 3D composite in conjunction with recently proposed High Temporal Resolution kernel (HYPRC3D-HTR), in terms of 4D noise reduction while preserving the spatiotemporal patterns or 4D resolution within the 4D image estimate. Consequently, the error in outcome measures obtained from the HYPR4D method was less dependent on the region size, contrast, and uniformity/functional patterns within the target structures compared to the other methods. For outcome measures that depend on spatiotemporal tracer uptake patterns such as the nondisplaceable Binding Potential (BP_ND ), the root mean squared error in regional mean of voxel BP_ND values was reduced from ~8% (OSEM with spatial or 4D filter) to ~3% using HYPRC3D-HTR and was further reduced to ~2% using our proposed HYPR4D method for relatively small target structures (~10 mm in diameter). At the voxel level, HYPR4D produced two to four times lower mean absolute error in BP_ND relative to HYPRC3D-HTR.

CONCLUSION

As compared to conventional methods, our proposed HYPR4D method can produce more robust and accurate image features without requiring any prior information.

Denoising non-steady state dynamic PET data using a feed-forward neural network

The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromised by high levels of statistical noise, particularly at the voxel level. Many denoising strategies have been proposed, both in the temporal and spatial domain, which substantially improve the signal to noise ratio of the reconstructed dynamic images. However, although most filtering approaches are fairly successful in reducing the spatio-temporal inter-voxel variability, they may also average out or completely eradicate the critically important temporal signature of a transient neurotransmitter activation response that may be present in a non-steady state dynamic PET study. In this work, we explore an approach towards temporal denoising of non-steady state dynamic PET images using an artificial neural network, which was trained to identify the temporal profile of a time-activity curve, while preserving any potential activation response. We evaluated the performance of a feed-forward perceptron neural network to improve the signal to noise ratio of dynamic [¹¹C]raclopride activation studies and compared it with the widely used highly constrained back projection (HYPR) filter. Results on both simulated Geant4 Application for Tomographic Emission data of a realistic rat brain phantom and experimental animal data of a freely moving animal study showed that the proposed neural network can efficiently improve the noise characteristics of dynamic data in the temporal domain, while it can lead to a more reliable estimation of voxel-wise activation response in target region. In addition, improvements in signal-to-noise ratio achieved by denoising the dynamic data using the proposed neural network led to improved accuracy and precision of the estimated model parameters of the lp-ntPET model, compared to the HYPR filter. The performance of the proposed denoising approach strongly depends on the amount of noise in the dynamic PET data, with higher noise leading to substantially higher variability in the estimated parameters of the activation response. Overall, the feed-forward network led to a similar performance as the HYPR filter in terms of spatial denoising, but led to notable improvements in terms of temporal denoising, which in turn improved the estimation activation parameters.

A Monte Carlo approach for improving transient dopamine release detection sensitivity

Current methods using a single PET scan to detect voxel-level transient dopamine release-using F-test (significance) and cluster size thresholding-have limited detection sensitivity for clusters of release small in size and/or having low release levels. Specifically, simulations show that voxels with release near the peripheries of such clusters are often rejected-becoming false negatives and ultimately distorting the F-distribution of rejected voxels. We suggest a Monte Carlo method that incorporates these two observations into a cost function, allowing erroneously rejected voxels to be accepted under specified criteria. In simulations, the proposed method improves detection sensitivity by up to 50% while preserving the cluster size threshold, or up to 180% when optimizing for sensitivity. A further parametric-based voxelwise thresholding is then suggested to better estimate the release dynamics in detected clusters. We apply the Monte Carlo method to a pilot scan from a human gambling study, where additional parametrically unique clusters are detected as compared to the current best methods-results consistent with our simulations.

Brain Glucose Metabolism, Cognition, and Cardiorespiratory Fitness Following Exercise Training in Adults at Risk for Alzheimer's Disease

Aerobic exercise has been associated with reduced burden of brain and cognitive changes related to Alzheimer's disease (AD). However, it is unknown whether exercise training in asymptomatic individuals harboring risk for AD improves outcomes associated with AD. We investigated the effect of 26 weeks of supervised aerobic treadmill exercise training on brain glucose metabolism and cognition among 23 late-middle-aged adults from a cohort enriched with familial and genetic risk of AD. They were randomized to Usual Physical Activity (PA) or Enhanced PA conditions. Usual PA received instruction about maintaining an active lifestyle. Enhanced PA completed a progressive exercise training program consisting of 3 sessions of treadmill walking per week for 26 weeks. By week seven, participants exercised at 70- 80% heart rate reserve for 50 minutes per session to achieve 150 minutes of moderate intensity activity per week in accordance with public health guidelines. Before and after the intervention, participants completed a graded treadmill test to assess VO2peak as a measure of cardiorespiratory fitness (CRF), wore an accelerometer to measure free-living PA, underwent 18F-fluorodeoxyglucose positron emission tomography imaging to assess brain glucose metabolism, and a neuropsychological battery to assess episodic memory and executive function. VO2peak increased, sedentary behavior decreased, and moderate-to-vigorous PA increased significantly in the Enhanced PA group as compared to Usual PA. Glucose metabolism in the posterior cingulate cortex (PCC) did not change significantly in Enhanced PA relative to Usual PA. However, change in PCC glucose metabolism correlated positively with change in VO2peak. Executive function, but not episodic memory, was significantly improved after Enhanced PA relative to Usual PA. Improvement in executive function correlated with increased VO2peak. Favorable CRF adaptation after 26 weeks of aerobic exercise training was associated with improvements in PCC glucose metabolism and executive function, important markers of AD.

Reversal of heart failure in a chemogenetic model of persistent cardiac redox stress

We previously described a novel "chemogenetic" animal model of heart failure that recapitulates a characteristic feature commonly found in human heart failure: chronic oxidative stress. This heart failure model uses a chemogenetic approach to activate a recombinant yeast d-amino acid oxidase in rat hearts in vivo to generate oxidative stress, which then rapidly leads to the development of a dilated cardiomyopathy. Here we apply this new model to drug testing by studying its response to treatment with the angiotensin II (ANG II) receptor blocker valsartan, administered either alone or with the neprilysin inhibitor sacubitril. Echocardiographic and [¹⁸F]fluorodeoxyglucose positron emission tomographic imaging revealed that valsartan in the presence or absence of sacubitril reverses the anatomical and metabolic remodeling induced by chronic oxidative stress. Markers of oxidative stress, mitochondrial function, and apoptosis, as well as classical heart failure biomarkers, also normalized following drug treatments despite the persistence of cardiac fibrosis. These findings provide evidence that chemogenetic heart failure is rapidly reversible by drug treatment, setting the stage for the study of novel heart failure therapeutics in this model. The ability of ANG II blockade and neprilysin inhibition to reverse heart failure induced by chronic oxidative stress identifies a central role for cardiac myocyte angiotensin receptors in the pathobiology of cardiac dysfunction caused by oxidative stress.NEW & NOTEWORTHY The chemogenetic approach allows us to distinguish cardiac myocyte-specific pathology from the pleiotropic changes that are characteristic of other "interventional" animal models of heart failure. These features of the chemogenetic heart failure model facilitate the analysis of drug effects on the progression and regression of ventricular remodeling, fibrosis, and dysfunctional signal transduction. Chemogenetic approaches will be highly informative in the study of the roles of redox stress in heart failure providing an opportunity for the identification of novel therapeutic targets.

KLOTHO heterozygosity attenuates APOE4-related amyloid burden in preclinical AD

OBJECTIVE

To examine whether the KLOTHO gene variant KL-VS attenuates APOE4-associated β-amyloid (Aβ) accumulation in a late-middle-aged cohort enriched with Alzheimer disease (AD) risk factors.

METHODS

Three hundred nine late-middle-aged adults from the Wisconsin Registry for Alzheimer's Prevention and the Wisconsin Alzheimer's Disease Research Center were genotyped to determine KL-VS and APOE4 status and underwent CSF sampling (n = 238) and/or 11C-Pittsburgh compound B (PiB)-PET imaging (n = 183). Covariate-adjusted regression analyses were used to investigate whether APOE4 exerted expected effects on Aβ burden. Follow-up regression analyses stratified by KL-VS genotype (i.e., noncarrier vs heterozygous; there were no homozygous individuals) evaluated whether the influence of APOE4 on Aβ was different among KL-VS heterozygotes compared to noncarriers.

RESULTS

APOE4 carriers exhibited greater Aβ burden than APOE4-negative participants. This effect was stronger in CSF (t = -5.12, p < 0.001) compared with PiB-PET (t = 3.93, p < 0.001). In the stratified analyses, this APOE4 effect on Aβ load was recapitulated among KL-VS noncarriers (CSF: t = -5.09, p < 0.001; PiB-PET: t = 3.77, p < 0 .001). In contrast, among KL-VS heterozygotes, APOE4-positive individuals did not exhibit higher Aβ burden than APOE4-negative individuals (CSF: t = -1.03, p = 0.308; PiB-PET: t = 0.92, p = 0.363). These differential APOE4 effects remained after KL-VS heterozygotes and noncarriers were matched on age and sex.

CONCLUSION

In a cohort of at-risk late-middle-aged adults, KL-VS heterozygosity was associated with an abatement of APOE4-associated Aβ aggregation, suggesting KL-VS heterozygosity confers protections against APOE4-linked pathways to disease onset in AD.

Partial-volume correction in dynamic PET-CT: effect on tumor kinetic parameter estimation and validation of simplified metrics

Background

Partial-volume effects generally result in an underestimation of tumor tracer uptake on PET-CT for small lesions, necessitating partial-volume correction (PVC) for accurate quantification. However, investigation of PVC in dynamic oncological PET studies to date is scarce. The aim of this study was to investigate PVC’s impact on tumor kinetic parameter estimation from dynamic PET-CT acquisitions and subsequent validation of simplified semi-quantitative metrics. Ten patients with EGFR-mutated non-small cell lung cancer underwent dynamic ¹⁸F-fluorothymidine PET-CT before, 7 days after, and 28 days after commencing treatment with a tyrosine kinase inhibitor. Parametric PVC was applied using iterative deconvolution without and with highly constrained backprojection (HYPR) denoising, respectively. Using an image-derived input function with venous parent plasma calibration, we estimated full kinetic parameters V_T, K₁, and k₃/k₄ (BP_ND) using a reversible two-tissue compartment model, and simplified metrics (SUV and tumor-to-blood ratio) at 50–60 min post-injection.

Results

PVC had a non-linear effect on measured activity concentrations per timeframe. PVC significantly changed each kinetic parameter, with a median increase in V_T of 11.8% (up to 25.1%) and 10.8% (up to 21.7%) without and with HYPR, respectively. Relative changes in kinetic parameter estimates vs. simplified metrics after applying PVC were poorly correlated (correlations 0.36–0.62; p < 0.01). PVC increased correlations between simplified metrics and V_T from 0.82 and 0.81 (p < 0.01) to 0.90 and 0.88 (p < 0.01) for SUV and TBR, respectively, albeit non-significantly. PVC also increased correlations between treatment-induced changes in simplified metrics vs. V_T at 7 (SUV) and 28 (SUV and TBR) days after treatment start non-significantly. Delineation on partial-volume corrected PET images resulted in a median decrease in metabolic tumor volume of 14.3% (IQR − 22.1 to − 7.5%), and increased the effect of PVC on kinetic parameter estimates.

Conclusion

PVC has a significant impact on tumor kinetic parameter estimation from dynamic PET-CT data, which differs from its effect on simplified metrics. However, it affected validation of these simplified metrics both as single measurements and as biomarkers of treatment response only to a small extent. Future dynamic PET studies should preferably incorporate PVC.

Trial registration

Dutch Trial Register, NTR3557.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0483-z) contains supplementary material, which is available to authorized users.

Moderate Physical Activity is Associated with Cerebral Glucose Metabolism in Adults at Risk for Alzheimer's Disease

The objective of this study was to investigate the relationship between accelerometer-measured physical activity (PA) and glucose metabolism in asymptomatic late-middle-aged adults. Ninety-three cognitively healthy late-middle-aged adults from the Wisconsin Registry for Alzheimer's Prevention participated in this cross-sectional study. They underwent 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging and wore an accelerometer (ActiGraph GT3X+) to measure free-living PA. Accelerometer data yielded measures of light (LPA), moderate (MPA), and vigorous (VPA) intensity PA. FDG-PET images were scaled to the cerebellum and pons, and cerebral glucose metabolic rate was extracted from specific regions of interest (ROIs) known to be hypometabolic in AD, i.e., hippocampus, posterior cingulate, inferior temporal cortex, and angular gyrus. Regression analyses were utilized to examine the association between PA and glucose metabolism, while adjusting for potential confounds. There were associations between MPA and glucose metabolism in all ROIs examined. In contrast, LPA was not associated with glucose uptake in any ROI and VPA was only associated with hippocampal FDG uptake. Secondary analyses did not reveal associations between sedentary time and glucose metabolism in any of the ROIs. Exploratory voxel-wise analysis identified additional regions where MPA was significantly associated with glucose metabolism including the precuneus, supramarginal gyrus, amygdala, and middle frontal gyrus. These findings suggest that the intensity of PA is an important contributor to neuronal function in a late-middle-aged cohort, with MPA being the most salient. Prospective studies are necessary for fully elucidating the link between midlife engagement in PA and later life development of AD.

Direct reconstruction of parametric images for brain PET with event-by-event motion correction: evaluation in two tracers across count levels

Parametric images for dynamic positron emission tomography (PET) are typically generated by an indirect method, i.e. reconstructing a time series of emission images, then fitting a kinetic model to each voxel time activity curve. Alternatively, 'direct reconstruction', incorporates the kinetic model into the reconstruction algorithm itself, directly producing parametric images from projection data. Direct reconstruction has been shown to achieve parametric images with lower standard error than the indirect method. Here, we present direct reconstruction for brain PET using event-by-event motion correction of list-mode data, applied to two tracers. Event-by-event motion correction was implemented for direct reconstruction in the Parametric Motion-compensation OSEM List-mode Algorithm for Resolution-recovery reconstruction. The direct implementation was tested on simulated and human datasets with tracers [11C]AFM (serotonin transporter) and [11C]UCB-J (synaptic density), which follow the 1-tissue compartment model. Rigid head motion was tracked with the Vicra system. Parametric images of K 1 and distribution volume (V T  =  K 1/k 2) were compared to those generated by the indirect method by regional coefficient of variation (CoV). Performance across count levels was assessed using sub-sampled datasets. For simulated and real datasets at high counts, the two methods estimated K 1 and V T with comparable accuracy. At lower count levels, the direct method was substantially more robust to outliers than the indirect method. Compared to the indirect method, direct reconstruction reduced regional K 1 CoV by 35-48% (simulated dataset), 39-43% ([11C]AFM dataset) and 30-36% ([11C]UCB-J dataset) across count levels (averaged over regions at matched iteration); V T CoV was reduced by 51-58%, 54-60% and 30-46%, respectively. Motion correction played an important role in the dataset with larger motion: correction increased regional V T by 51% on average in the [11C]UCB-J dataset. Direct reconstruction of dynamic brain PET with event-by-event motion correction is achievable and dramatically more robust to noise in V T images than the indirect method.

BDNF Val66Met predicts cognitive decline in the Wisconsin Registry for Alzheimer's Prevention

OBJECTIVE

To examine the influence of the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism on longitudinal cognitive trajectories in a large, cognitively healthy cohort enriched for Alzheimer disease (AD) risk and to understand whether β-amyloid (Aβ) burden plays a moderating role in this relationship.

METHODS

One thousand twenty-three adults (baseline age 54.94 ± 6.41 years) enrolled in the Wisconsin Registry for Alzheimer's Prevention underwent BDNF genotyping and cognitive assessment at up to 5 time points (average follow-up 6.92 ± 3.22 years). A subset (n = 140) underwent ¹¹C-Pittsburgh compound B (PiB) scanning. Covariate-adjusted mixed-effects regression models were used to elucidate the effect of BDNF on cognitive trajectories in 4 cognitive domains, including verbal learning and memory, speed and flexibility, working memory, and immediate memory. Secondary mixed-effects regression models were conducted to examine whether Aβ burden, indexed by composite PiB load, modified any observed BDNF-related cognitive trajectories.

RESULTS

Compared to BDNF Val/Val homozygotes, Met carriers showed steeper decline in verbal learning and memory (p = 0.002) and speed and flexibility (p = 0.017). In addition, Aβ burden moderated the relationship between BDNF and verbal learning and memory such that Met carriers with greater Aβ burden showed even steeper cognitive decline (p = 0.033).

CONCLUSIONS

In a middle-aged cohort with AD risk, carriage of the BDNF Met allele was associated with steeper decline in episodic memory and executive function. This decline was exacerbated by greater Aβ burden. These results suggest that the BDNF Val66Met polymorphism may play an important role in cognitive decline and could be considered as a target for novel AD therapeutics.

[18 F]Nifene test-retest reproducibility in first-in-human imaging of α4β2* nicotinic acetylcholine receptors

The aim of this study was to examine the suitability of [¹⁸ F]nifene, a novel α4β2* nicotinic acetylcholine receptor (nAChR) radiotracer, for in vivo brain imaging in a first-in-human study.

METHODS

Eight healthy subjects (4 M,4 F;21-69,44 ± 21 yrs) underwent a [¹⁸ F]nifene positron emission tomography scan (200 ± 3.7 MBq), and seven underwent a second scan within 58 ± 31 days. Regional estimates of DVR were measured using the multilinear reference tissue model (MRTM2) with the corpus callosum as reference region. DVR reproducibility was evaluated with test-retest variability (TRV) and intraclass correlation coefficient (ICC).

RESULTS

The DVR ranged from 1.3 to 2.5 across brain regions with a TRV of 0-7%, and did not demonstrate a systematic difference between test and retest. The ICCs ranged from 0.2 to 0.9. DVR estimates were stable after 40 min.

CONCLUSION

The binding profile and tracer kinetics of [¹⁸ F]nifene make it a promising α4β2* nAChR radiotracer for scientific research in humans, with reliable DVR test-retest reproducibility.

Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

Background

Accurate quantification of PET studies depends on the spatial resolution of the PET data. The commonly limited PET resolution results in partial volume effects (PVE). Iterative deconvolution methods (IDM) have been proposed as a means to correct for PVE. IDM improves spatial resolution of PET studies without the need for structural information (e.g. MR scans). On the other hand, deconvolution also increases noise, which results in lower signal-to-noise ratios (SNR). The aim of this study was to implement IDM in combination with HighlY constrained back-PRojection (HYPR) denoising to mitigate poor SNR properties of conventional IDM.

Methods

An anthropomorphic Hoffman brain phantom was filled with an [¹⁸F]FDG solution of ~25 kBq mL⁻¹ and scanned for 30 min on a Philips Ingenuity TF PET/CT scanner (Philips, Cleveland, USA) using a dynamic brain protocol with various frame durations ranging from 10 to 300 s. Van Cittert IDM was used for PVC of the scans. In addition, HYPR was used to improve SNR of the dynamic PET images, applying it both before and/or after IDM. The Hoffman phantom dataset was used to optimise IDM parameters (number of iterations, type of algorithm, with/without HYPR) and the order of HYPR implementation based on the best average agreement of measured and actual activity concentrations in the regions. Next, dynamic [¹¹C]flumazenil (five healthy subjects) and [¹¹C]PIB (four healthy subjects and four patients with Alzheimer’s disease) scans were used to assess the impact of IDM with and without HYPR on plasma input-derived distribution volumes (V_T) across various regions of the brain.

Results

In the case of [¹¹C]flumazenil scans, Hypr-IDM-Hypr showed an increase of 5 to 20% in the regional V_T whereas a 0 to 10% increase or decrease was seen in the case of [¹¹C]PIB depending on the volume of interest or type of subject (healthy or patient). References for these comparisons were the V_Ts from the PVE-uncorrected scans.

Conclusions

IDM improved quantitative accuracy of measured activity concentrations. Moreover, the use of IDM in combination with HYPR (Hypr-IDM-Hypr) was able to correct for PVE without increasing noise.

Electronic supplementary material

The online version of this article (doi:10.1186/s13550-017-0284-1) contains supplementary material, which is available to authorized users.

In Vivo Comparison of Tau Radioligands 18F-THK-5351 and 18F-THK-5317

This study compared the in vivo imaging characteristics of tau PET ligands ¹⁸F-THK-5351 and ¹⁸F-THK-5317 in the context of Alzheimer disease (AD). Additionally, reference tissue distribution volume ratio (DVR) estimation methods and SUV ratio (SUVR) timing windows were evaluated to determine the optimal strategy for specific binding quantification. Methods: Twenty-eight subjects (mean age ± SD, 71 ± 7 y) underwent either dynamic 90-min ¹⁸F-THK-5317 or ¹⁸F-THK-5351 PET scans. Bland-Altman plots were used to compare the simplified reference tissue method, multilinear reference tissue method (MRTM2), and Logan reference tissue DVR estimates and to assess temporal stability of SUVR windows using cerebellar gray matter as a reference region. In vivo kinetics and DVR estimates were directly compared for 10 subjects who underwent both ¹⁸F-THK-5317 and ¹⁸F-THK-5351 PET scans. Results: THK-5351 exhibited faster cerebellar gray matter clearance, faster cortical white matter clearance, and higher DVR estimates in AD tau-associated regions of interest than THK-5317. The MRTM2 method produced the most reliable DVR estimates for both tracers, particularly when scan duration was shortened to 60 min. SUVR stability was observed 50-70 min after injection for both tracers. Parametric images revealed differences between MRTM2, Logan, and SUVR binding in white matter regions for THK-5317. Conclusion: THK-5317 and THK-5351 show promise for in vivo detection of AD tau. THK-5351 has more favorable pharmacokinetics and imaging characteristics than THK-5317.

Cardiorespiratory Fitness Attenuates the Influence of Amyloid on Cognition

The aim of this study was to examine cross-sectionally whether higher cardiorespiratory fitness (CRF) might favorably modify amyloid-β (Aβ)-related decrements in cognition in a cohort of late-middle-aged adults at risk for Alzheimer's disease (AD). Sixty-nine enrollees in the Wisconsin Registry for Alzheimer's Prevention participated in this study. They completed a comprehensive neuropsychological exam, underwent 11C Pittsburgh Compound B (PiB)-PET imaging, and performed a graded treadmill exercise test to volitional exhaustion. Peak oxygen consumption (VO2peak) during the exercise test was used as the index of CRF. Forty-five participants also underwent lumbar puncture for collection of cerebrospinal fluid (CSF) samples, from which Aβ42 was immunoassayed. Covariate-adjusted regression analyses were used to test whether the association between Aβ and cognition was modified by CRF. There were significant VO2peak*PiB-PET interactions for Immediate Memory (p=.041) and Verbal Learning & Memory (p=.025). There were also significant VO2peak*CSF Aβ42 interactions for Immediate Memory (p<.001) and Verbal Learning & Memory (p<.001). Specifically, in the context of high Aβ burden, that is, increased PiB-PET binding or reduced CSF Aβ42, individuals with higher CRF exhibited significantly better cognition compared with individuals with lower CRF. In a late-middle-aged, at-risk cohort, higher CRF is associated with a diminution of Aβ-related effects on cognition. These findings suggest that exercise might play an important role in the prevention of AD.

Clustering-initiated factor analysis application for tissue classification in dynamic brain positron emission tomography

The goal is to quantify the fraction of tissues that exhibit specific tracer binding in dynamic brain positron emission tomography (PET). It is achieved using a new method of dynamic image processing: clustering-initiated factor analysis (CIFA). Standard processing of such data relies on region of interest analysis and approximate models of the tracer kinetics and of tissue properties, which can degrade accuracy and reproducibility of the analysis. Clustering-initiated factor analysis allows accurate determination of the time-activity curves and spatial distributions for tissues that exhibit significant radiotracer concentration at any stage of the emission scan, including the arterial input function. We used this approach in the analysis of PET images obtained using (11)C-Pittsburgh Compound B in which specific binding reflects the presence of β-amyloid. The fraction of the specific binding tissues determined using our approach correlated with that computed using the Logan graphical analysis. We believe that CIFA can be an accurate and convenient tool for measuring specific binding tissue concentration and for analyzing tracer kinetics from dynamic images for a variety of PET tracers. As an illustration, we show that four-factor CIFA allows extraction of two blood curves and the corresponding distributions of arterial and venous blood from PET images even with a coarse temporal resolution.

Physical activity attenuates age-related biomarker alterations in preclinical AD

OBJECTIVE

To examine whether engagement in physical activity might favorably alter the age-dependent evolution of Alzheimer disease (AD)-related brain and cognitive changes in a cohort of at-risk, late-middle-aged adults.

METHODS

Three hundred seventeen enrollees in the Wisconsin Registry for Alzheimer's Prevention underwent T1 MRI; a subset also underwent (11)C-Pittsburgh compound B-PET (n = 186) and (18)F-fluorodeoxyglucose-PET (n = 152) imaging. Participants' responses on a self-report measure of current physical activity were used to classify them as either physically active or physically inactive based on American Heart Association guidelines. They also completed a comprehensive neuropsychological battery. Covariate-adjusted regression analyses were used to test whether the adverse effect of age on imaging and cognitive biomarkers was modified by physical activity.

RESULTS

There were significant age × physical activity interactions for β-amyloid burden (p = 0.014), glucose metabolism (p = 0.015), and hippocampal volume (p = 0.025) such that, with advancing age, physically active individuals exhibited a lesser degree of biomarker alterations compared with the physically inactive. Similar age × physical activity interactions were also observed on cognitive domains of Immediate Memory (p = 0.042) and Visuospatial Ability (p = 0.016). In addition, the physically active group had higher scores on Speed and Flexibility (p = 0.002) compared with the inactive group.

CONCLUSIONS

In a middle-aged, at-risk cohort, a physically active lifestyle is associated with an attenuation of the deleterious influence of age on key biomarkers of AD pathophysiology. However, because our observational, cross-sectional design cannot establish causality, randomized controlled trials/longitudinal studies will be necessary for determining whether midlife participation in structured physical exercise forestalls the development of AD and related disorders in later life.

Amyloid burden, neuronal function, and cognitive decline in middle-aged adults at risk for Alzheimer's disease

The relative influence of amyloid burden, neuronal structure and function, and prior cognitive performance on prospective memory decline among asymptomatic late middle-aged individuals at risk for Alzheimer's disease (AD) is currently unknown. We investigated this using longitudinal cognitive data from 122 middle-aged adults (21 "Decliners" and 101 "Stables") enrolled in the Wisconsin Registry for Alzheimer's Prevention who underwent multimodality neuroimaging [11C-Pittsburgh Compound B (PiB), 18F-fluorodeoxyglucose (FDG), and structural/functional magnetic resonance imaging (fMRI)] 5.7 ± 1.4 years (range = 2.9-8.9) after their baseline cognitive assessment. Covariate-adjusted regression analyses revealed that the only imaging measure that significantly distinguished Decliners from Stables (p = .027) was a Neuronal Function composite derived from FDG and fMRI. In contrast, several cognitive measures, especially those that tap episodic memory, significantly distinguished the groups (p's<.05). Complementary receiver operating characteristic curve analyses identified the Brief Visuospatial Memory Test-Revised (BVMT-R) Total (.82 ± .05, p < .001), the BVMT-R Delayed Recall (.73 ± .06, p = .001), and the Reading subtest from the Wide-Range Achievement Test-III (.72 ± .06, p = .002) as the top three measures that best discriminated the groups. These findings suggest that early memory test performance might serve a more clinically pivotal role in forecasting future cognitive course than is currently presumed.

Amyloid burden and neural function in people at risk for Alzheimer's Disease

To determine the relationship between amyloid burden and neural function in healthy adults at risk for Alzheimer's Disease (AD), we used multimodal imaging with [C-11]Pittsburgh compound B positron emission tomography, [F-18]fluorodeoxyglucose, positron emission tomography , and magnetic resonance imaging, together with cognitive measurement in 201 subjects (mean age, 60.1 years; range, 46-73 years) from the Wisconsin Registry for Alzheimer's Prevention. Using a qualitative rating, 18% of the samples were strongly positive Beta-amyloid (Aβ+), 41% indeterminate (Aβi), and 41% negative (Aβ-). Aβ+ was associated with older age, female sex, and showed trends for maternal family history of AD and APOE4. Relative to the Aβ- group, Aβ+ and Aβi participants had increased glucose metabolism in the bilateral thalamus; Aβ+ participants also had increased metabolism in the bilateral superior temporal gyrus. Aβ+ participants exhibited increased gray matter in the lateral parietal lobe bilaterally relative to the Aβ- group, and no areas of significant atrophy. Cognitive performance and self report cognitive and affective symptoms did not differ between groups. Amyloid burden can be identified in adults at a mean age of 60 years and is accompanied by glucometabolic increases in specific areas, but not atrophy or cognitive loss. This asymptomatic stage may be an opportune window for intervention to prevent progression to symptomatic AD.

Creating dynamic images of short-lived dopamine fluctuations with lp-ntPET: dopamine movies of cigarette smoking

We describe experimental and statistical steps for creating dopamine movies of the brain from dynamic PET data. The movies represent minute-to-minute fluctuations of dopamine induced by smoking a cigarette. The smoker is imaged during a natural smoking experience while other possible confounding effects (such as head motion, expectation, novelty, or aversion to smoking repeatedly) are minimized.

We present the details of our unique analysis. Conventional methods for PET analysis estimate time-invariant kinetic model parameters which cannot capture short-term fluctuations in neurotransmitter release. Our analysis - yielding a dopamine movie - is based on our work with kinetic models and other decomposition techniques that allow for time-varying parameters ^1-7. This aspect of the analysis - temporal-variation - is key to our work. Because our model is also linear in parameters, it is practical, computationally, to apply at the voxel level. The analysis technique is comprised of five main steps: pre-processing, modeling, statistical comparison, masking and visualization. Preprocessing is applied to the PET data with a unique 'HYPR' spatial filter ⁸ that reduces spatial noise but preserves critical temporal information. Modeling identifies the time-varying function that best describes the dopamine effect on ¹¹C-raclopride uptake. The statistical step compares the fit of our (lp-ntPET) model ⁷ to a conventional model ⁹. Masking restricts treatment to those voxels best described by the new model. Visualization maps the dopamine function at each voxel to a color scale and produces a dopamine movie. Interim results and sample dopamine movies of cigarette smoking are presented.

Nonlinear spatio-temporal filtering of dynamic PET data using a four-dimensional Gaussian filter and expectation-maximization deconvolution

We introduce a method for denoising dynamic PET data, spatio-temporal expectation-maximization (STEM) filtering, that combines four-dimensional Gaussian filtering withEMdeconvolution. The initial Gaussian filter suppresses noise at a broad range of spatial and temporal frequencies and EM deconvolution quickly restores the frequencies most important to the signal. We aim to demonstrate that STEM filtering can improve variance in both individual time frames and in parametric images without introducing significant bias. We evaluate STEM filtering with a dynamic phantom study, and with simulated and human dynamic PET studies of a tracer with reversible binding behaviour, [C-11]raclopride, and a tracer with irreversible binding behaviour, [F-18]FDOPA. STEM filtering is compared to a number of established three and four-dimensional denoising methods. STEM filtering provides substantial improvements in variance in both individual time frames and in parametric images generated with a number of kinetic analysis techniques while introducing little bias. STEM filtering does bias early frames, but this does not affect quantitative parameter estimates. STEM filtering is shown to be superior to the other simple denoising methods studied. STEM filtering is a simple and effective denoising method that could be valuable for a wide range of dynamic PET applications.