TY - GEN
T1 - Spatial-temporal pharmacokinetic model based registration of 4D brain PET data
AU - Jiao, Jieqing
AU - Searle, Graham E.
AU - Tziortzi, Andri C.
AU - Salinas, Cristian A.
AU - Gunn, Roger N.
AU - Schnabel, Julia A.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84867862923
U2 - 10.1007/978-3-642-33555-6_9
DO - 10.1007/978-3-642-33555-6_9
M3 - Conference contribution
AN - SCOPUS:84867862923
SN - 9783642335549
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 100
EP - 112
BT - Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - Second International Workshop, STIA 2012, Held in Conjunction with MICCAI 2012, Proceedings
T2 - 2nd International Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2012, Held in Conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 1 October 2012
ER -