Arridge, S

Atkinson, D

Erlandsson, K

Hutton, B

Ourselin, S

Sari, H

Thielemans, K

UCL Discovery

MEETING ABSTRACT Open AccessIncorporation of MRI-AIF information forimproved kinetic modelling of dynamic PET dataHasan Sari1*, Kjell Erlandsson1, Kris Thielemans1, David Atkinson2, Simon Arridge3, Sebastien Ourselin3,Brian Hutton1From PSMR14: 3rd Conference in PET/MR and SPECT/MRKos Island, Greece. 19-21 May 20141Institute of Nuclear Medicine,University College London,London, UKPharmacokinetic analysis of dynamic PET data requires estimation of the arterial inputfunction (AIF). An alternative method to collecting blood samples during scanning isthe simultaneous estimation approach in which AIF and kinetic parameters are esti-mated at once [1]. We have investigated how information from a simultaneous MRI-AIF can be incorporated into this model and evaluated its effect on estimated kineticparameters by comparing absolute bias and coefficient of variation (CV) values oninflux constants (Ki). As the delivery of the tracer or contrast agent is dominated byvascular flow dynamics, PET-AIF and MRI-AIF boluses will have similar peak shapes ifthe same injection speed is used [2,3]. Hence it can be assumed that the values of l1of eq. 1 [1] can be fixed to reduce the number of parameters in fitting. Simulated datawas generated using two tissue compartment model with rate constants from a clinicalbrain study [5]. Noise was added using eq. 2 where a set to 0.5,2 and 4. AIF scalingwas done using one blood sample obtained at the end of the study, or by fixing A3 andl3 to their true values assuming they can be determined using multiple blood samples.C t A t A A e A e A ep ( ) ( )      1 2 3 2 31 2 3   (1)  a dt2.(2)As shown in Figure 1 and 2, when l1 is fixed, larger improvements (from 13.84% to5.38%, averaged on all noise levels) were seen on the bias of estimates with one bloodsample compared to the method with multiple samples (from 13.54% to 10.60%). ForCV, improvement was slightly higher on scaling with multiple samples when l1 wasfixed.We have shown that information from simultaneous MRI-AIF can reduce the biason Ki estimates and multiple blood samples might not be necessary, as the scalingmethod with one blood sample performed better when a parameter reflecting the earlypart of the MRI-AIF is included.Sari et al. EJNMMI Physics 2014, 1(Suppl 1):A43http://www.ejnmmiphys.com/content/1/S1/A43© 2014 Sari et al; licensee Springer This is an Open Access article distributed under the terms of the Creative Commons AttributionLicense (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly cited.AcknowledgementsThis work was supported by an IMPACT studentship funded jointly by Siemens and the UCL Faculty of EngineeringSciences. KE receives funding from EPSRC (EP/K005278/1). UCL/UCLH research is supported by the NIHR BiomedicalResearch Centres funding scheme.Authors’ details1Institute of Nuclear Medicine, University College London, London, UK. 2Center for Medical Imaging, University CollegeLondon, London, UK. 3Center for Medical Image Computing, University College London, London, UK.Published: 29 July 2014References1. Feng D, Wong KP, Wu CM, Siu WC: A technique for extracting physiological parameters and the required inputfunction simultaneously from PET image measurements: theory and simulation study. IEEE Trans. Inf. Technol.Biomed. 1997, 1(4):243-54.2. Poulin E, Lebel R, Croteau E, Blanchette M, Tremblay L, Lecomte R, Bentourkia M, Lepage M: Conversion of arterialinput functions for dual pharmacokinetic modeling using Gd-D TPA/MRI and 18F-FDG/PET. Magn. Reson. Med. 2013,69:781-92.3. Ibaraki IKM, Shimosegawa E, Toyoshima H, Ishigame K, Sugawara S, Takahashi K, Miura S: Evaluation of arterial inputfunction for Perfusion MRI: comparison with that of PET study. Proc. Intl. Soc. Mag. Reson. Med. 2005, 13:1125.4. Muzic RF, Cornelius S: COMKAT: compartment model kinetic analysis tool. J. Nucl. Med. 2001, 42(4):636-45.Figure 1 Percentage absolute bias on Ki estimates, averaged over three ROIs, when noise level was set to(a) 0.5, (b) 2, (c) 4. Blue bars show the bias with the scaling method with one blood sample at the end ofthe study, green bars show the bias with the scaling method where the tail is fit to the multiple bloodsamples towards the end of study and red bars show the bias which would be present when all sixparameters of the AIF was set to their true values.Figure 2 Coefficient of variation values over 50 Ki estimates, averaged over three ROIs, when noise levelwas set to (a) 0.5, (b) 2, (c) 4. Blue bars show the CV with the scaling method with one blood sample atthe end of the study, green bars show the CV with the scaling method where the tail is fit to the multipleblood samples towards the end of study and red bars show the CV which would be present when all sixparameters of the AIF was set to their true values.Sari et al. EJNMMI Physics 2014, 1(Suppl 1):A43http://www.ejnmmiphys.com/content/1/S1/A43Page 2 of 35. Hawkins RA, M E, Phelps ME, Huang SC: Effects of temporal sampling, glucose metabolic rates, and disruptions ofthe blood-brain barrier on the FDG model with and without a vascular compartment: studies in human braintumors with PET. J. Cereb. Blood Flow Metab. 1986, 6(2):170-83.doi:10.1186/2197-7364-1-S1-A43Cite this article as: Sari et al.: Incorporation of MRI-AIF information for improved kinetic modelling of dynamicPET data. EJNMMI Physics 2014 1(Suppl 1):A43.Submit your manuscript to a journal and benefi t from:7 Convenient online submission7 Rigorous peer review7 Immediate publication on acceptance7 Open access: articles freely available online7 High visibility within the fi eld7 Retaining the copyright to your article    Submit your next manuscript at 7 springeropen.comSari et al. EJNMMI Physics 2014, 1(Suppl 1):A43http://www.ejnmmiphys.com/content/1/S1/A43Page 3 of 3

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

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

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