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Short acquisition time PET quantification using MRI-based pharmacokinetic parameter synthesis

Research output: Contribution to journalConference paper

Catherine J. Scott, Jieqing Jiao, M. Jorge Cardoso, Andrew Melbourne, Enrico De Vita, David L. Thomas, Ninon Burgos, Pawel Markiewicz, Jonathan M. Schott, Brian F. Hutton, Sébastien Ourselin

Original languageEnglish
Pages (from-to)737-744
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10434 LNCS
DOIs
Publication statusPublished - 1 Jan 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 11 Sep 201713 Sep 2017

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Abstract

Positron Emission Tomography (PET) with pharmacokinetic (PK) modelling is a quantitative molecular imaging technique, however the long data acquisition time is prohibitive in clinical practice. An approach has been proposed to incorporate blood flow information from Arterial Spin Labelling (ASL) Magnetic Resonance Imaging (MRI) into PET PK modelling to reduce the acquisition time. This requires the conversion of cerebral blood flow (CBF) maps, measured by ASL, into the relative tracer delivery parameter (R1) used in the PET PK model. This was performed regionally using linear regression between population R1 and ASL values. In this paper we propose a novel technique to synthesise R1 maps from ASL data using a database with both R1 and CBF maps. The local similarity between the candidate ASL image and those in the database is used to weight the propagation of R1 values to obtain the optimal patient specific R1 map. Structural MRI data is also included to provide information within common regions of artefact in ASL data. This methodology is compared to the linear regression technique using leave one out analysis on 32 subjects. The proposed method significantly improves regional R1 estimation (p < 0.001), reducing the error in the pharmacokinetic modelling. Furthermore, it allows this technique to be extended to a voxel level, increasing the clinical utility of the images.

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