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Conference papers

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

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 (R1R1R\₁) used in the PET PK model. This was performed regionally using linear regression between population R1R1R\₁ and ASL values. In this paper we propose a novel technique to synthesise R1R1R\₁ maps from ASL data using a database with both R1R1R\₁ and CBF maps. The local similarity between the candidate ASL image and those in the database is used to weight the propagation of R1R1R\₁ values to obtain the optimal patient specific R1R1R\₁ 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 R1R1R\₁ estimation (p\textless0.001p\textless0.001p\textless0.001), 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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Submitted on : Sunday, July 1, 2018 - 10:42:11 PM
Last modification on : Tuesday, August 24, 2021 - 8:42:01 AM

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Catherine J. Scott, Jieqing Jiao, Jorge M. Cardoso, Andrew Melbourne, Enrico de Vita, et al.. Short acquisition time PET quantification using MRI-based pharmacokinetic parameter synthesis. Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, 2017, Québec, Canada. pp.737--744, ⟨10.1007/978-3-319-66185-8_83⟩. ⟨hal-01827190⟩



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