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Incorporation of MRI-AIF information for improved kinetic modeling of dynamic PET data

Sari, H; Erlandsson, K; Thielemans, K; Atkinson, D; Ourselin, S; Arridge, S; Hutton, BF; (2015) Incorporation of MRI-AIF information for improved kinetic modeling of dynamic PET data. IEEE Transactions on Nuclear Science , 62 (3) pp. 612-618. 10.1109/TNS.2015.2426952. Green open access

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Abstract

In the analysis of dynamic PET data, compartmental kinetic analysis methods require an accurate knowledge of the arterial input function (AIF). Although arterial blood sampling is the gold standard of the methods used to measure the AIF, it is usually not preferred as it is an invasive method. An alternative method is the simultaneous estimation method (SIME), where physiological parameters and the AIF are estimated together, using information from different anatomical regions. Due to the large number of parameters to estimate in its optimisation, SIME is a computationally complex method and may sometimes fail to give accurate estimates. In this work, we try to improve SIME by utilising an input function derived from a simultaneously obtained DSC-MRI scan. With the assumption that the true value of one of the six parameter PET-AIF model can be derived from an MRI-AIF, the method is tested using simulated data. The results indicate that SIME can yield more robust results when the MRI information is included with a significant reduction in absolute bias of Ki estimates.

Type: Article
Title: Incorporation of MRI-AIF information for improved kinetic modeling of dynamic PET data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TNS.2015.2426952
Publisher version: http://dx.doi.org/10.1109/TNS.2015.2426952
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Keywords: blood vessels, magnetic resonance imaging, optimisation, positron emission tomography, Blood, Data models, Kinetic theory, Magnetic resonance imaging, Mathematical model, Noise level, Positron emission tomography, DSC-MRI scan, MRI information, MRI-AIF information, PET-AIF model, anatomical regions, arterial blood sampling, dynamic PET data, improved kinetic modelling, optimisation, pharmacokinetic analysis, simultaneous estimation method, Arterial input function, magnetic resonance imaging, noninvasive measurement, positron emission tomography
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Metabolism and Experi Therapeutics
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1463390
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