Spatial-temporal pharmacokinetic model based registration of 4d brain PET data

Authors:
Jieqing Jiao;Graham E. Searle;Andri C. Tziortzi;Cristian A. Salinas;Roger N. Gunn;Julia A. Schnabel
Affiliations:
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK,Imanova Limited, Hammersmith Hospital, London, UK;Imanova Limited, Hammersmith Hospital, London, UK;Imanova Limited, Hammersmith Hospital, London, UK,FMRIB Centre, Department of Clinical Neurology, University of Oxford, UK;Imanova Limited, Hammersmith Hospital, London, UK;Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK,Imanova Limited, Hammersmith Hospital, London, UK,Department of Medicine, Imperial College, London, ...;Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
Venue:
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
Year:
2012

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Motion correction and parameter estimation in dceMRI sequences: application to colorectal cancer

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I

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Abstract

In dynamic positron emission tomography (PET), where scan durations often exceed 1 hour, registration of motion-corrupted dynamic PET images is necessary in order to maintain the integrity of the physiological/pharmacological/biochemical information derived from the tracer kinetic analysis of the scan. A pharmacokinetic model, which is traditionally used to analyse PET data following any registration, was incorporated into the registration process itself in order to allow for a groupwise registration of the temporal time frames. The new method achieved smaller registration errors and improved kinetic parameter estimates on validation data sets as compared with the traditional image based similarity registration approach. When applied to measured clinical data from 10 healthy subjects scanned with [11C]-(+)-PHNO (a dopamine D3/D2 receptor tracer), it reduced the intra-class variability on the tracer kinetics, suggesting a successful registration. Our new method which incorporates a generic tracer kinetic model could be applied widely to dynamic PET data as part of an automated tool to remove motion artefacts and increase the integrity and statistical power of these data.