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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 [F]-florbetapir PET data for amyloid-β 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.

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 [ 18 F]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). Abstract. 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 study shows that the proposed approach with the simplified reference tissue model can achieve amyloid burden estimation from 30-min [18 F]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).

Survival following very preterm birth is associated with cognitive and behavioral sequelae, which may have identifiable neural correlates. Many survivors of modern neonatal care in the 1990s are now young adults and the evolution of MRI findings into adult life has rarely been evaluated. We have investigated a cohort of 19-year-old adolescents without severe impairments born between 22 and 26 weeks of gestation in 1995 (extremely preterm: EP). Using T2 data derived from magnetic resonance imaging we investigate differences between the brains of 46 EP participants (n = 46) and the brains of a group of term-born controls (n = 20). Despite EP adolescents having significantly reduced gray and white matter volumes, the composition of these tissues, assessed by both single and multi-component relaxometry, appears to be unrelated to either preterm status or gender. This may represent either insensitivity of the imaging technique or reflect that there are only subtle differences between EP subjects and their term-born peers.

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