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- Title
Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.
- Authors
Duan, Chong; Kallehauge, Jesper F.; Pérez-Torres, Carlos J.; Bretthorst, G. Larry; Beeman, Scott C.; Tanderup, Kari; Ackerman, Joseph J. H.; Garbow, Joel R.
- Abstract
<bold>Purpose: </bold>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.<bold>Procedures: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.
- Subjects
CONTRAST-enhanced magnetic resonance imaging; CONTRAST media; PROBABILITY theory; PARAMETER estimation; CHEMICAL kinetics; DYNAMIC models; ALGORITHMS; BIOLOGICAL models; COMPUTER simulation; DYNAMICS; MAGNETIC resonance imaging; RESEARCH funding; TIME; UNCERTAINTY
- Publication
Molecular Imaging & Biology, 2018, Vol 20, Issue 1, p150
- ISSN
1536-1632
- Publication type
journal article
- DOI
10.1007/s11307-017-1090-x