A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI

II. In vivo results

Matthias Schabel, Edward V R DiBella, Randy L. Jensen, Karen L. Salzman

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

Accurate quantification of pharmacokinetic model parameters in tracer kinetic imaging experiments requires correspondingly accurate determination of the arterial input function (AIF). Despite significant effort expended on methods of directly measuring patient-specific AIFs in modalities as diverse as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), dynamic positron emission tomography (PET), and perfusion computed tomography (CT), fundamental and technical difficulties have made consistent and reliable achievement of that goal elusive. Here, we validate a new algorithm for AIF determination, the Monte Carlo blind estimation (MCBE) method (which is described in detail and characterized by extensive simulations in a companion paper), by comparing AIFs measured in DCE-MRI studies of eight brain tumor patients with results of blind estimation. Blind AIFs calculated with the MCBE method using a pool of concentration-time curves from a region of normal brain tissue were found to be quite similar to the measured AIFs, with statistically significant decreases in fit residuals observed in six of eight patients. Biases between the blind and measured pharmacokinetic parameters were the dominant source of error. Averaged over all eight patients, the mean biases were +7% in Ktrans, 0% in kep,-11% in vp and +10% in ve. Corresponding uncertainties (median absolute deviation from the best fit line) were 0.0043 min-1 in Ktrans, 0.0491 min-1 in k ep, 0.29% in vp and 0.45% in ve. The use of a published population-averaged AIF resulted in larger mean biases in three of the four parameters (-23% in Ktrans,-22% in kep,-63% in vp), with the bias in ve unchanged, and led to larger uncertainties in all four parameters (0.0083 min-1 in K trans, 0.1038 min-1 in kep, 0.31% in v p and 0.95% in ve). When blind AIFs were calculated from a region of tumor tissue, statistically significant decreases in fit residuals were observed in all eight patients despite larger deviations of these blind AIFs from the measured AIFs. The observed

Original languageEnglish (US)
Pages (from-to)4807-4823
Number of pages17
JournalPhysics in Medicine and Biology
Volume55
Issue number16
DOIs
StatePublished - Aug 21 2010
Externally publishedYes

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Monte Carlo Method
Uncertainty
Pharmacokinetics
Magnetic Resonance Imaging
Brain Neoplasms
Research Design
Perfusion
Brain
Population
Neoplasms

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI : II. In vivo results. / Schabel, Matthias; DiBella, Edward V R; Jensen, Randy L.; Salzman, Karen L.

In: Physics in Medicine and Biology, Vol. 55, No. 16, 21.08.2010, p. 4807-4823.

Research output: Contribution to journalArticle

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abstract = "Accurate quantification of pharmacokinetic model parameters in tracer kinetic imaging experiments requires correspondingly accurate determination of the arterial input function (AIF). Despite significant effort expended on methods of directly measuring patient-specific AIFs in modalities as diverse as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), dynamic positron emission tomography (PET), and perfusion computed tomography (CT), fundamental and technical difficulties have made consistent and reliable achievement of that goal elusive. Here, we validate a new algorithm for AIF determination, the Monte Carlo blind estimation (MCBE) method (which is described in detail and characterized by extensive simulations in a companion paper), by comparing AIFs measured in DCE-MRI studies of eight brain tumor patients with results of blind estimation. Blind AIFs calculated with the MCBE method using a pool of concentration-time curves from a region of normal brain tissue were found to be quite similar to the measured AIFs, with statistically significant decreases in fit residuals observed in six of eight patients. Biases between the blind and measured pharmacokinetic parameters were the dominant source of error. Averaged over all eight patients, the mean biases were +7{\%} in Ktrans, 0{\%} in kep,-11{\%} in vp and +10{\%} in ve. Corresponding uncertainties (median absolute deviation from the best fit line) were 0.0043 min-1 in Ktrans, 0.0491 min-1 in k ep, 0.29{\%} in vp and 0.45{\%} in ve. The use of a published population-averaged AIF resulted in larger mean biases in three of the four parameters (-23{\%} in Ktrans,-22{\%} in kep,-63{\%} in vp), with the bias in ve unchanged, and led to larger uncertainties in all four parameters (0.0083 min-1 in K trans, 0.1038 min-1 in kep, 0.31{\%} in v p and 0.95{\%} in ve). When blind AIFs were calculated from a region of tumor tissue, statistically significant decreases in fit residuals were observed in all eight patients despite larger deviations of these blind AIFs from the measured AIFs. The observed",
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