Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.
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...
| Publicado en: | Molecular Imaging & Biology Vol. 20; no. 1; pp. 150 - 160 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127460432&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127460432 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15361632 KJU jtl: Molecular Imaging & Biology issn: 15361632 maglogo: N pubinfo: dt: Feb2018 vid: 20 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127460432 127460432 144169096 NLM28536804 127460432 10.1007/s11307-017-1090-x NLM28536804 127460432 ppf: 150 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF. aug: au: Duan, Chong Kallehauge, Jesper F. Pérez-Torres, Carlos J. Bretthorst, G. Larry Beeman, Scott C. Tanderup, Kari Ackerman, Joseph J. H. Garbow, Joel R. affil: Department of Chemistry, Washington University, Saint Louis, MO, USA sug: subj: Magnetic Resonance Imaging Algorithms Contrast Media Models, Biological Kinetics Computer Simulation Time Factors Uncertainty Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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