Literature DB >> 26501643

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, Brian Hutton1.   

Abstract

Entities:  

Year:  2014        PMID: 26501643      PMCID: PMC4545457          DOI: 10.1186/2197-7364-1-S1-A54

Source DB:  PubMed          Journal:  EJNMMI Phys        ISSN: 2197-7364


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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. 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

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.

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

Review 1.  A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology.

Authors:  Kjell Erlandsson; Irène Buvat; P Hendrik Pretorius; Benjamin A Thomas; Brian F Hutton
Journal:  Phys Med Biol       Date:  2012-10-16       Impact factor: 3.609

2.  Conversion of arterial input functions for dual pharmacokinetic modeling using Gd-DTPA/MRI and 18F-FDG/PET.

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Journal:  Magn Reson Med       Date:  2012-05-08       Impact factor: 4.668

3.  Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI.

Authors:  Geoff J M Parker; Caleb Roberts; Andrew Macdonald; Giovanni A Buonaccorsi; Sue Cheung; David L Buckley; Alan Jackson; Yvonne Watson; Karen Davies; Gordon C Jayson
Journal:  Magn Reson Med       Date:  2006-11       Impact factor: 4.668

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Authors:  Daniel Stocker; Stefanie Hectors; Octavia Bane; Naik Vietti-Violi; Daniela Said; Paul Kennedy; Jordan Cuevas; Guilherme M Cunha; Claude B Sirlin; Kathryn J Fowler; Sara Lewis; Bachir Taouli
Journal:  Eur Radiol       Date:  2021-05-27       Impact factor: 5.315

2.  Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammation.

Authors:  Laurence D Vass; Sarah Lee; Frederick J Wilson; Marie Fisk; Joseph Cheriyan; Ian Wilkinson
Journal:  EJNMMI Phys       Date:  2019-12-16
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