Volume 1 Supplement 1

Proceedings of the 3rd PSMR Conference on PET/MR and SPECT/MR

Open Access

Modelling the impact of injection time on the bolus shapes in PET-MRI AIF Conversion

  • Hasan Sari1,
  • Kjell Erlandsson1,
  • Anna Barnes1,
  • David Atkinson2,
  • Simon Arridge3,
  • Sebastien Ourselin3 and
  • Brian Hutton1
EJNMMI Physics20141(Suppl 1):A54

DOI: 10.1186/2197-7364-1-S1-A54

Published: 29 July 2014

With the introduction of combined PET/MRI systems, AIF conversion can be made under certain circumstances (see [1]). We propose a model that allows modification of the injection parameters in the AIF fit to account for differences caused by different injection durations [2].

Brain 18F-Choline PET and DSC-MRI data were obtained using Siemens mMR. The MR contrast agent was injected with a rate of 4ml/sec and the PET tracer was injected manually. Perfusion Mismatch Analyzer [3] was used to extract the MRI-AIF. Carotid arteries were segmented on a post contrast MPRAGE image. PET frames were registered onto this MPRAGE image using rigid registration and partial volume correction was done using the iterative Yang method [4]. The AIFs were fitted using a convolution of a ‘double Butterworth’ function, representing the injection, with a tri-exponential function representing the elimination [Eq. 1]. The bolus shape can be adjusted by changing Δτ (τ2 - τ1). This was tested with a population based MRI AIF [5], as well as with clinical data.
For the population based input function, Figure 1 shows that when Δτ was increased, lower and wider peaks were seen, and with decreased Δτ, higher but narrower peaks were observed. Figure 2 shows that the function fits both clinical PET and MRI AIFs well. Values of τ1 and τ2 were changed to modify the MRI-AIF and Figure 3 shows the modified MRI-AIF together with the original fitted PET-AIF, normalized to their peaks. Two AIFs have similar peak shapes but start to differ at the elimination phase as Gd-DOTA and 18F-Choline have different tissue uptake rates.
Figure 1

Simulated MRI-AIFs using Parker’s population-based input function refitted with the developed function. AIF shapes with different injection durations, Δτ is shown.

Figure 2

The double Butterworth convolution function used to fit (a) DSC-MRI data and (b) 18F-Choline PET data together with a plot where the timescale of PET-AIF was limited to MRI-AIF’s to show different bolus widths.

Figure 3

The MRI-AIF with modified τ1 and τ2 values plotted together with the PET-AIF. The MRI-AIF peak is scaled to PET-AIF’s peak.

This enables conversion of the early part of the AIFs from one modality to another even if different injection protocols are used.



This work was supported by an IMPACT studentship funded jointly by Siemens and the UCL Faculty of Engineering Sciences. K.E. is funded by a grant from EPSRC (EP/K005278/1). UCL/UCLH research is supported by the NIHR Biomedical Research Centres funding scheme. We thank Celia O’Meara for her help with the PET and MRI datasets and Alex Bousse for his valuable comments.

Authors’ Affiliations

Institute of Nuclear Medicine, University College London, University College London Hospitals
Center for Medical Imaging, University College London
Center for Medical Image Computing, University College London


  1. Poulin E, Lebel R, Croteau E, Blanchette M, Tremblay L, Lecomte R, Bentourkia M, Lepage M: Conversion of arterial input functions for dual pharmacokinetic modeling using Gd-DTPA/MRI and 18F-FDG/PET. Magn. Reson. Med. 2013, 69: 781–92. 10.1002/mrm.24318PubMedView ArticleGoogle Scholar
  2. Holt A, Pasca E, Heijmink SW, Teertstra J, Muller SH, van der Heide UA: The impact of overall injection time on the arterial input function and pharmaco-kinetic analysis using the Tofts model in DCE-MRI for prostate cancer patients. Proc. Intl. Soc. Mag. Reson. Med. 2012, 20: 238.Google Scholar
  3. Perfusion Mismatch Analyzer, version [January 20, 2014] ASIST-Japan Web site 2006. updated June 2011 [http://asist.umin.jp/data-e.shtml]
  4. Erlandsson K, Buvat I, Pretorius PH, Thomas B, Hutton BF: A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys. Med. Biol. 2012,57(21):R119–59. 10.1088/0031-9155/57/21/R119PubMedView ArticleGoogle Scholar
  5. Parker GJM, Roberts C, Macdonald A, Buonaccorsi GA, Cheung S, Buckley DL, Jackson A, Watson Y, Davies K, G C, Jayson GC: Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn. Reson. Med. 2006, 56: 993–1000. 10.1002/mrm.21066PubMedView ArticleGoogle Scholar


© Sari et al; licensee Springer 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.