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

  • Duan, Chong1
  • Kallehauge, Jesper F.2, 3
  • Pérez-Torres, Carlos J.4, 5
  • Bretthorst, G. Larry6
  • Beeman, Scott C.4
  • Tanderup, Kari3, 6, 7
  • Ackerman, Joseph J. H.1, 4, 8, 9
  • Garbow, Joel R.4, 9
  • 1 Washington University, Department of Chemistry, Saint Louis, MO, USA , Saint Louis (United States)
  • 2 Aarhus University, Department of Medical Physics, Aarhus, Denmark , Aarhus (Denmark)
  • 3 Aarhus University, Department of Oncology, Aarhus, Denmark , Aarhus (Denmark)
  • 4 Washington University, Department of Radiology, Saint Louis, MO, USA , Saint Louis (United States)
  • 5 Purdue University, School of Health Sciences, West Lafayette, IN, 47907, USA , West Lafayette (United States)
  • 6 Washington University, Department of Radiation Oncology, Saint Louis, MO, USA , Saint Louis (United States)
  • 7 Aarhus University, Institute of Clinical Medicine, Aarhus, Denmark , Aarhus (Denmark)
  • 8 Washington University, Department of Medicine, Saint Louis, MO, USA , Saint Louis (United States)
  • 9 Washington University, Alvin J Siteman Cancer Center, Saint Louis, MO, USA , Saint Louis (United States)
Published Article
Molecular Imaging and Biology
Publication Date
May 23, 2017
DOI: 10.1007/s11307-017-1090-x
Springer Nature


PurposeThis 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.ProceduresBayesian 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.ResultsWhen 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.ConclusionsThe 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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