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

Chong Duan, Jesper F. Kallehauge, Carlos J. Pérez-Torres, G. Larry Bretthorst, Scott C. Beeman, Kari Tanderup, Joseph J.H. Ackerman, Joel R. Garbow

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)150-159
Number of pages10
JournalMolecular Imaging and Biology
Volume20
Issue number1
DOIs
StatePublished - Feb 1 2018

Keywords

  • Accuracy and precision
  • Arterial input function
  • Bayesian inference
  • Cancer
  • Dynamic contrast-enhanced (DCE)
  • Quantitation

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