Tau accumulation in autosomal dominant Alzheimer's disease: a longitudinal [18F]flortaucipir study.

Cortical tau accumulation is a key pathological event that partly defines Alzheimer's disease (AD) onset and is associated with cognitive decline and future disease progression. However, an improved understanding of the timing and pattern of early tau deposition in AD and how this may be tracked in vivo is needed. Data from 59 participants involved in two longitudinal cohort studies of autosomal dominant AD (ADAD) were used to investigate whether tau PET can detect and track presymptomatic change; seven participants were symptomatic, and 52 were asymptomatic but at a 50% risk of carrying a pathogenic mutation. All had baseline flortaucipir (FTP) PET, MRI and clinical assessments; 26 individuals had more than one FTP PET scan. Standardised uptake value ratios (SUVRs) in prespecified regions of interest (ROIs) were obtained using inferior cerebellar grey matter as the reference region. We compared the changes in FTP SUVRs between presymptomatic carriers, symptomatic carriers and non-carriers, adjusting for age, sex and study site. We also investigated the relationship between regional FTP SUVRs and estimated years to/from symptom onset (EYO). Compared to both non-carriers and presymptomatic carriers, FTP SUVRs were significantly higher in symptomatic carriers in all ROIs tested (p < 0.001). There were no significant regional differences between presymptomatic carriers and non-carriers in FTP SUVRs, or their rates of change (p > 0.05), although increased FTP signal uptake was seen posteriorly in some individuals around the time of expected symptom onset. When we examined the relationship of FTP SUVR with respect to EYO, the earliest significant regional difference between mutation carriers and non-carriers was detected within the precuneus prior to estimated symptom onset in some cases. This study supports preliminary studies suggesting that presymptomatic tau tracer uptake is rare in ADAD. In cases where early uptake was seen, there was often a predilection for posterior regions (the precuneus and post-cingulate) as opposed to the medial temporal lobe, highlighting the importance of examining in vivo tau uptake beyond the confines of traditional Braak staging.

NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data.

Current PET datasets are becoming larger, thereby increasing the demand for fast and reproducible processing pipelines. This paper presents a freely available, open source, Python-based software package called NiftyPAD, for versatile analyses of static, full or dual-time window dynamic brain PET data. The key novelties of NiftyPAD are the analyses of dual-time window scans with reference input processing, pharmacokinetic modelling with shortened PET acquisitions through the incorporation of arterial spin labelling (ASL)-derived relative perfusion measures, as well as optional PET data-based motion correction. Results obtained with NiftyPAD were compared with the well-established software packages PPET and QModeling for a range of kinetic models. Clinical data from eight subjects scanned with four different amyloid tracers were used to validate the computational performance. NiftyPAD achieved [Formula: see text] correlation with PPET, with absolute difference [Formula: see text] for linearised Logan and MRTM2 methods, and [Formula: see text] correlation with QModeling, with absolute difference [Formula: see text] for basis function based SRTM and SRTM2 models. For the recently published SRTM ASL method, which is unavailable in existing software packages, high correlations with negligible bias were observed with the full scan SRTM in terms of non-displaceable binding potential ([Formula: see text]), indicating reliable model implementation in NiftyPAD. Together, these findings illustrate that NiftyPAD is versatile, flexible, and produces comparable results with established software packages for quantification of dynamic PET data. It is freely available ( https://github.com/AMYPAD/NiftyPAD ), and allows for multi-platform usage. The modular setup makes adding new functionalities easy, and the package is lightweight with minimal dependencies, making it easy to use and integrate into existing processing pipelines.

Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL-MRI: proof of concept.

Pharmacokinetic modelling on dynamic positron emission tomography (PET) data is a quantitative technique. However, the long acquisition time is prohibitive for routine clinical use. Instead, the semi-quantitative standardised uptake value ratio (SUVR) from a shorter static acquisition is used, despite its sensitivity to blood flow confounding longitudinal analysis. A method has been proposed to reduce the dynamic acquisition time for quantification by incorporating cerebral blood flow (CBF) information from arterial spin labelling (ASL) magnetic resonance imaging (MRI) into the pharmacokinetic modelling. In this work, we optimise and validate this framework for a study of ageing and preclinical Alzheimer's disease. This methodology adapts the simplified reference tissue model (SRTM) for a reduced acquisition time (RT-SRTM) and is applied to [18F]-florbetapir PET data for amyloid-ß quantification. Evaluation shows that the optimised RT-SRTM can achieve amyloid burden estimation from a 30-min PET/MR acquisition which is comparable with the gold standard SRTM applied to 60 min of PET data. Conversely, SUVR showed a significantly higher error and bias, and a statistically significant correlation with tracer delivery due to the influence of blood flow. The optimised RT-SRTM produced amyloid burden estimates which were uncorrelated with tracer delivery indicating its suitability for longitudinal studies.

Direct Parametric Reconstruction With Joint Motion Estimation/Correction for Dynamic Brain PET Data.

Direct reconstruction of parametric images from raw photon counts has been shown to improve the quantitative analysis of dynamic positron emission tomography (PET) data. However it suffers from subject motion which is inevitable during the typical acquisition time of 1-2 hours. In this work we propose a framework to jointly estimate subject head motion and reconstruct the motion-corrected parametric images directly from raw PET data, so that the effects of distorted tissue-to-voxel mapping due to subject motion can be reduced in reconstructing the parametric images with motion-compensated attenuation correction and spatially aligned temporal PET data. The proposed approach is formulated within the maximum likelihood framework, and efficient solutions are derived for estimating subject motion and kinetic parameters from raw PET photon count data. Results from evaluations on simulated [11C]raclopride data using the Zubal brain phantom and real clinical [18F]florbetapir data of a patient with Alzheimer's disease show that the proposed joint direct parametric reconstruction motion correction approach can improve the accuracy of quantifying dynamic PET data with large subject motion.

Impact of image-based motion correction on dopamine D3/D2 receptor occupancy-comparison of groupwise and frame-by-frame registration approaches.

BACKGROUND: Image registration algorithms are frequently used to align the reconstructed brain PET frames to remove subject head motion. However, in occupancy studies, this is a challenging task where competitive binding of a drug can further reduce the available signal for registration. The purpose of this study is to evaluate two kinds of algorithms-a conventional frame-by-frame (FBF) registration and a recently introduced groupwise image registration (GIR), for motion correction of a dopamine D3/D2 receptor occupancy study. METHODS: The FBF method co-registers all the PET frames to a common reference based on normalised mutual information as the spatial similarity. The GIR method incorporates a pharmacokinetic model and conducts motion correction by maximising a likelihood function iteratively on tracer kinetics and subject motion. Data from eight healthy volunteers scanned with [11C]-(+)-PHNO pre- and post-administration of a range of doses of the D3 antagonist GSK618334 were used to compare the motion correction performance. RESULTS: The groupwise registration achieved improved motion correction results, both by visual inspection of the dynamic PET data and by the reduction of the variability in the outcome measures, and required no additional steps to exclude unsuccessfully realigned PET data for occupancy modelling as compared to frame-by-frame registration. Furthermore, for the groupwise method, the resultant binding potential estimates had reduced variation and bias for individual scans and improved half maximal effective concentration (EC50) estimates were obtained for the study as a whole. CONCLUSIONS: These results indicate that the groupwise registration approach can provide improved motion correction of dynamic brain PET data as compared to frame-by-frame registration approaches for receptor occupancy studies.

Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors.

Positron emission tomography (PET) reconstruction is an ill-posed inverse problem which typically involves fitting a high-dimensional forward model of the imaging process to noisy, and sometimes undersampled photon emission data. To improve the image quality, prior information derived from anatomical images of the same subject has been previously used in the penalised maximum likelihood (PML) method to regularise the model complexity and selectively smooth the image on a voxel basis in PET reconstruction. In this work, we propose a novel perspective of incorporating the prior information by exploring the sparse property of natural images. Instead of a regular voxel grid, the sparse image representation jointly determined by the prior image and the PET data is used in reconstruction to leverage between the image details and smoothness, and this prior is integrated into the PET forward model and has a closed-form expectation maximisation (EM) solution. Simulations show that the proposed approach achieves improved bias versus variance trade-off and higher contrast recovery than the current state-of-the-art methods, and preserves the image details better. Application to clinical PET data shows promising results.

Joint parametric reconstruction and motion correction framework for dynamic PET data.

In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithm's computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction residual with noisy data in the simulation-based validation and reduces the bias of the direct estimation of the kinetic parameter of interest. A preliminary evaluation on clinical brain data using [18F]Choline shows improved contrast for regions of high activity. The proposed method is based on a data-driven kinetic modelling method and is directly applicable to reversible and irreversible PET tracers, covering a range of clinical applications.

Spatio-temporal pharmacokinetic model based registration of 4D PET neuroimaging data.

In dynamic positron emission tomography (PET) neuroimaging studies, where scan durations often exceed 1h, registration of motion-corrupted dynamic PET images is necessary in order to maintain the integrity of the physiological, pharmacological, or biochemical information derived from the tracer kinetic analysis of the scan. In this work, we incorporate a pharmacokinetic model, which is traditionally used to analyse PET data following any registration, into the registration process itself in order to allow for a groupwise registration of the temporal time frames. The new method is shown to achieve smaller registration errors and improved kinetic parameter estimates on validation data sets when compared with image similarity based registration approaches. When applied to measured clinical data from 10 healthy subjects scanned with [(11)C]-(+)-PHNO (a dopamine D3/D2 receptor tracer), it reduces the intra-class variability on the receptor binding outcome measure, further supporting the improvements in registration accuracy. Our method incorporates a generic tracer kinetic model which makes it applicable to different PET radiotracers to remove motion artefacts and increase the integrity of dynamic PET studies.

A generalised spatio-temporal registration framework for dynamic PET data: application to neuroreceptor imaging.

This work presents a novel pharmacokinetic model based registration algorithm for the motion correction of dynamic positron emission tomography (PET) images. The algorithm employs a generalised model that derives the input function from the tomographic data itself to model the PET tracer kinetics and thus eliminates the need of arterial blood sampling. Both the temporal constraint from the tracer kinetic behaviour and spatial constraint from the image similarity are integrated in a joint probabilistic model, in which the subject motion and tracer kinetic parameters are iteratively optimised, leading to a group-wise registration framework of motion corrupted dynamic PET data. The algorithm is evaluated with simulated and measured human dopamine D3 receptor imaging data using [11C]-(+)-PHNO. The simulation-based validation demonstrates that the new algorithm has a subvoxel registration accuracy on average for noisy data with simulated motion artefacts. The algorithm also shows reductions in motion on initial experiments with measured clinical [11C]-(+)-PHNO brain data.