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Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF

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Abstract

Purpose

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF.

Procedures

Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data.

Results

When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach.

Conclusions

The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

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Acknowledgments

This project was supported by funding from the Alvin J. Siteman Comprehensive Cancer Center (P30 CA091842).

Author information

Authors and Affiliations

  1. Department of Chemistry, Washington University, Saint Louis, MO, USA

    Chong Duan & Joseph J. H. Ackerman

  2. Department of Medical Physics, Aarhus University, Aarhus, Denmark

    Jesper F. Kallehauge

  3. Department of Oncology, Aarhus University, Aarhus, Denmark

    Jesper F. Kallehauge & Kari Tanderup

  4. Department of Radiology, Washington University, Saint Louis, MO, USA

    Carlos J. Pérez-Torres, Scott C. Beeman, Joseph J. H. Ackerman & Joel R. Garbow

  5. School of Health Sciences, Purdue University, West Lafayette, IN, 47907, USA

    Carlos J. Pérez-Torres

  6. Department of Radiation Oncology, Washington University, Saint Louis, MO, USA

    G. Larry Bretthorst & Kari Tanderup

  7. Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Kari Tanderup

  8. Department of Medicine, Washington University, Saint Louis, MO, USA

    Joseph J. H. Ackerman

  9. Alvin J Siteman Cancer Center, Washington University, Saint Louis, MO, USA

    Joseph J. H. Ackerman & Joel R. Garbow

Authors
  1. Chong Duan
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  2. Jesper F. Kallehauge
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  3. Carlos J. Pérez-Torres
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  4. G. Larry Bretthorst
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  5. Scott C. Beeman
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  6. Kari Tanderup
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  7. Joseph J. H. Ackerman
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  8. Joel R. Garbow
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Corresponding author

Correspondence to Joel R. Garbow.

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The authors declare that they have no conflict of interest.

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Duan, C., Kallehauge, J.F., Pérez-Torres, C.J. et al. Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF. Mol Imaging Biol 20, 150–159 (2018). https://doi.org/10.1007/s11307-017-1090-x

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  • DOI: https://doi.org/10.1007/s11307-017-1090-x

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