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Short Acquisition Time PET Quantification Using MRI-Based Pharmacokinetic Parameter Synthesis

  • 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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

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 (\(R_1\)) used in the PET PK model. This was performed regionally using linear regression between population \(R_1\) and ASL values. In this paper we propose a novel technique to synthesise \(R_1\) maps from ASL data using a database with both \(R_1\) and CBF maps. The local similarity between the candidate ASL image and those in the database is used to weight the propagation of \(R_1\) values to obtain the optimal patient specific \(R_1\) 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 \(R_1\) 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.

Notes

Acknowledgements

This work was supported by the EPSRC UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1), UCL Leonard Wolfson Experimental Neurology Centre (PR/ylr/18575), EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278), MRC (MR/J01107X/1), NIHR UCLH Biomedical Research Centre (inc. High Impact Initiative, BW.mn.BRC10269). Insight 1946 receives funding from Alzheimer’s Research UK (ARUK-PG2014-1946), MRC Dementia Platform UK (CSUB19166) and The Wolfson Foundation, and support from Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly. We are grateful to the Insight 46 participants for their involvement in this study.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Catherine J. Scott
    • 1
  • Jieqing Jiao
    • 1
  • M. Jorge Cardoso
    • 1
  • Andrew Melbourne
    • 1
  • Enrico De Vita
    • 2
    • 3
  • David L. Thomas
    • 1
    • 2
    • 4
  • Ninon Burgos
    • 1
    • 5
  • Pawel Markiewicz
    • 1
  • Jonathan M. Schott
    • 6
  • Brian F. Hutton
    • 7
    • 8
  • Sébastien Ourselin
    • 1
    • 6
  1. 1.Translational Imaging Group, CMICUniversity College LondonLondonUK
  2. 2.Neuroradiological Academic UnitUCL Institute of NeurologyLondonUK
  3. 3.Lysholm Department of NeuroradiologyNational Hospital for Neurology and Neurosurgery, UCL Hospitals Foundation TrustLondonUK
  4. 4.Leonard Wolfson Experimental Neurology CentreUCL Institute of NeurologyLondonUK
  5. 5.Inria Paris, Aramis Project-Team, Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle épinière (ICM) – Pitié-Salpêtrière HospitalParisFrance
  6. 6.Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
  7. 7.Institute of Nuclear MedicineUniversity College LondonLondonUK
  8. 8.Centre for Medical Radiation PhysicsUniversity of WollongongWollongongAustralia

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