Accelerated pharmacokinetic map determination for dynamic contrast enhanced MRI using frequency-domain based Tofts model

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is widely used in the diagnosis of cancer and is also a promising tool for monitoring tumor response to treatment. The Tofts model has become a standard for the analysis of DCE-MRI. The process of curve fitting employed in the Tofts equation to obtain the pharmacokinetic (PK) parameters is time-consuming for high resolution scans. Current work demonstrates a frequency-domain approach applied to the standard Tofts equation to speed-up the process of curve-fitting in order to obtain the pharmacokinetic parameters. The results obtained show that using the frequency domain approach, the process of curve fitting is computationally more efficient compared to the time-domain approach.

[1]  Roberta Fusco Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI) , 2013 .

[2]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[3]  J. Gore,et al.  Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. , 2005, Magnetic resonance imaging.

[4]  O Henriksen,et al.  Quantitation of blood‐brain barrier defect by magnetic resonance imaging and gadolinium‐DTPA in patients with multiple sclerosis and brain tumors , 1990, Magnetic resonance in medicine.

[5]  A. Garpebring Contributions to quantitative dynamic contrast-enhanced MRI , 2011 .

[6]  Benjamin M Yeh,et al.  Dynamic contrast-enhanced magnetic resonance imaging as a pharmacodynamic measure of response after acute dosing of AG-013736, an oral angiogenesis inhibitor, in patients with advanced solid tumors: results from a phase I study. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[7]  A. Padhani,et al.  Assessing changes in tumour vascular function using dynamic contrast‐enhanced magnetic resonance imaging , 2002, NMR in biomedicine.

[8]  A. Padhani Dynamic contrast‐enhanced MRI in clinical oncology: Current status and future directions , 2002, Journal of magnetic resonance imaging : JMRI.

[9]  Marcelino Bernardo,et al.  The role of dynamic contrast-enhanced MRI in cancer diagnosis and treatment. , 2010, Diagnostic and interventional radiology.

[10]  Steven P Sourbron,et al.  On the scope and interpretation of the Tofts models for DCE‐MRI , 2011, Magnetic resonance in medicine.

[11]  Geoff J M Parker,et al.  Imaging Tumor Vascular Heterogeneity and Angiogenesis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging , 2007, Clinical Cancer Research.

[12]  P S Tofts,et al.  Quantitative Analysis of Dynamic Gd‐DTPA Enhancement in Breast Tumors Using a Permeability Model , 1995, Magnetic resonance in medicine.

[13]  Bart M. ter Haar Romeny,et al.  Pharmacokinetic models in clinical practice: What model to use for DCE-MRI of the breast? , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[14]  L R Schad,et al.  Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. , 1991, Journal of computer assisted tomography.

[15]  Michael Brady,et al.  Analysis of dynamic MR breast images using a model of contrast enhancement , 1997, Medical Image Anal..

[16]  G. Parker,et al.  DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents , 2007, British Journal of Cancer.

[17]  Mario Sansone,et al.  Dynamic contrast-enhanced MRI in breast cancer: A comparison between distributed and compartmental tracer kinetic models , 2012 .