ASL-incorporated pharmacokinetic modelling of PET data with reduced acquisition time: Application to amyloid imaging

Pharmacokinetic analysis of Positron Emission Tomography (PET) data typically requires at least one hour of image acquisition, which poses a great disadvantage in clinical practice. In this work, we propose a novel approach for pharmacokinetic modelling with significantly reduced PET acquisition time, by incorporating the blood flow information from simultaneously acquired arterial spin labelling (ASL) magnetic resonance imaging (MRI). A relationship is established between blood flow, measured by ASL, and the transfer rate constant from plasma to tissue of the PET tracer, leading to modified PET kinetic models with ASL-derived flow information. Evaluation on clinical amyloid imaging data from an Alzheimer’s disease (AD) study shows that the proposed approach with the simplified reference tissue model can achieve amyloid burden estimation from 30 min [18F]florbetapir PET data and 5 min simultaneous ASL MR data, which is comparable with the estimation from 60 min PET data (mean error=−0.03). Conversely, standardised uptake value ratio (SUVR), the alternative measure from the data showed a positive bias in areas of higher amyloid burden (mean error=0.07)

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

International audiencePharmacokinetic 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 [ 18 F]-florbetapir PET data for amyloid-b 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

Spatio-temporal registration of dynamic PET data

Medical imaging plays an essential role in current clinical research and practice. Among the wealth of available imaging modalities, Positron Tomography Emission (PET) reveals functional processes in vivo by providing information on the interaction between a biological target and its tracer at the molecular level. A time series of PET images obtained from a dynamic scan depicts the spatio-temporal distribution of the PET tracer. Analysing the dynamic PET data then enables the quantification of the functional processes of interest for disease understanding and drug development. Given the time duration of a dynamic PET scan, which is usually 1-2 hours, any subject motion inevitably corrupts the tissue-tovoxel mapping during PET imaging, resulting in an unreliable analysis of the data for clinical decision making. Image registration has been applied to perform motion correction on misaligned dynamic PET frames, however, the current methods are solely based on spatial similarity. By ignoring the temporal changes due to PET tracer kinetics they can lead to inaccurate registration. In this thesis, a spatio-temporal registration framework of dynamic PET data is developed to overcome such limits. There are three scientific contributions made in this thesis. Firstly, the likelihood of dynamic PET data is formulated based on the generative model with both tracer kinetics and subject motion, providing a novel objective function. Secondly, the solution to the optimisation based on the generic plasma-input model is given, leading to the availability of a variety of biological targets. Thirdly, reference-input models are also incorporated to avoid blood sampling and thus extend the coverage of PET studies of the proposed framework. In the simulation-based validation, the proposed method achieves sub-voxel accuracy and its impact on clinical studies is evaluated on dopamine receptor data from an occupancy study, as well as breast cancer data from a reproducibility study. By successfully eliminating the motion artifacts as shown by visual inspection, the proposed method reduces the variability in clinical PET data and improves the confidence of deriving outcome measures on a study level. The motion correction algorithms developed in this thesis do not require any additional computational resources for a PET research centre, and they facilitate cost reduction by eliminating the need of acquiring extra PET scans in cases of motion corruption.</p

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

Detail-preserving PET reconstruction with sparse image representation and anatomical priors

International audiencePositron 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

Biomedical and health research

In this paper we propose a unified framework for joint motion estimation/kinetic image reconstruction from gated dynamic PET data. The method is a generalisation of previous work to include gated data. The kinetic and motion parameters are estimated jointly by maximisation of the penalised likelihood. Kinetic parameters are estimated with an optimisation transfer approach, and the non-rigid motion is estimated with a quasi-Newton algorithm. Results on synthetic phantom data show that there is an advantage in jointly estimating motion and kinetics compared to pre-estimating the motion field for motion-compensated kinetic image reconstruction

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\u27s 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