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  • Meeting abstract
  • Open Access

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

  • 1,
  • 1,
  • 1,
  • 2,
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EJNMMI Physics20141 (Suppl 1) :A54

https://doi.org/10.1186/2197-7364-1-S1-A54

  • Published:

Keywords

  • Injection Protocol
  • Partial Volume Correction
  • Injection Duration
  • Rigid Registration
  • Post Contrast

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.
(1)
where
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
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
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
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.

Declarations

Acknowledgements

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

(1)
Institute of Nuclear Medicine, University College London, University College London Hospitals, London, NW1 2BU, UK
(2)
Center for Medical Imaging, University College London, London, NW1 2PG, UK
(3)
Center for Medical Image Computing, University College London, London, WC1E 6BT, UK

References

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