A Microfabricated Phantom for Quantitative MR Perfusion Measurements: Validation of Singular Value Decomposition Deconvolution Method

A perfusion phantom with unique features and a wide variety of applications in magnetic resonance imaging (MRI) and other imaging modalities is presented. Using microfabrication technique, a network of microchannels, in the scale of actual microvasculature, was created. The geometry of the network was determined based on Murray's “minimum work” law to simulate the hemodynamic in actual capillary networks. The perfusion-related parameters, such as flow, volume ratio, and the transit time, were precisely calculated using a finite-element method based program. These parameters were also estimated through the deconvolution of the residue function from the tissue concentration-time curve in the perfusion model. The widely accepted singular value decomposition (SVD) method in standard sSVD and reformulated rSVD forms were used for the purpose of the deconvolution and regularization. The accuracy of these methods in the presence of delay and dispersion was investigated. Comparing the estimated values to the true values, the contribution of each of these sources of error to the total error in the estimated perfusion parameters was determined.

[1]  Sandro Rossitti,et al.  Vascular Dimensions of the Cerebral Arteries Follow the Principle of Minimum Work , 1993, Stroke.

[2]  Fernando Calamante,et al.  Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics , 2003, NeuroImage.

[3]  B. Rosen,et al.  High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis , 1996, Magnetic resonance in medicine.

[4]  Fernando Calamante,et al.  Contrast agent concentration measurements affecting quantification of bolus‐tracking perfusion MRI , 2007, Magnetic resonance in medicine.

[5]  L. K. Hansen,et al.  Defining a local arterial input function for perfusion MRI using independent component analysis , 2004, Magnetic resonance in medicine.

[6]  D G Gadian,et al.  Quantification of Perfusion Using Bolus Tracking Magnetic Resonance Imaging in Stroke: Assumptions, Limitations, and Potential Implications for Clinical Use , 2002, Stroke.

[7]  S. Kety,et al.  THE DETERMINATION OF CEREBRAL BLOOD FLOW IN MAN BY THE USE OF NITROUS OXIDE IN LOW CONCENTRATIONS , 1945 .

[8]  J. Ruminski,et al.  Parametric imaging in dynamic susceptibility contrast MRI-phantom and in vivo studies , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  C B Grandin,et al.  Whole brain quantitative CBF, CBV, and MTT measurements using MRI bolus tracking: Implementation and application to data acquired from hyperacute stroke patients , 2000, Journal of magnetic resonance imaging : JMRI.

[10]  M. Wintermark,et al.  Comparative overview of brain perfusion imaging techniques. , 2005, Stroke.

[11]  M. Viergever,et al.  Maximum likelihood estimation of cerebral blood flow in dynamic susceptibility contrast MRI , 1999, Magnetic resonance in medicine.

[12]  D. Le Bihan,et al.  Viability Thresholds of Ischemic Penumbra of Hyperacute Stroke Defined by Perfusion-Weighted MRI and Apparent Diffusion Coefficient , 2001, Stroke.

[13]  K. Zierler,et al.  On the theory of the indicator-dilution method for measurement of blood flow and volume. , 1954, Journal of applied physiology.

[14]  B. Rosen,et al.  Modeling Cerebral Blood Flow and Flow Heterogeneity from Magnetic Resonance Residue Data , 1999, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[15]  Robert W Barber,et al.  Biomimetic design of microfluidic manifolds based on a generalised Murray's law. , 2006, Lab on a chip.

[16]  I. Kanno,et al.  Tracer Delay Correction of Cerebral Blood Flow with Dynamic Susceptibility Contrast-Enhanced MRI , 2005, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[17]  Jan-Ray Liao,et al.  Spoiled gradient‐echo as an arterial spin tagging technique for quick evaluation of local perfusion , 2002, Journal of magnetic resonance imaging : JMRI.

[18]  Céline Fouard,et al.  A Novel Three‐Dimensional Computer‐Assisted Method for a Quantitative Study of Microvascular Networks of the Human Cerebral Cortex , 2006, Microcirculation.

[19]  B. Rosen,et al.  Tracer arrival timing‐insensitive technique for estimating flow in MR perfusion‐weighted imaging using singular value decomposition with a block‐circulant deconvolution matrix , 2003, Magnetic resonance in medicine.

[20]  Karl J. Friston,et al.  Bayesian estimation of cerebral perfusion using a physiological model of microvasculature , 2006, NeuroImage.

[21]  Jianfeng Gao,et al.  Cerebral blood flow measurement by dynamic contrast MRI using singular value decomposition with an adaptive threshold , 1999, Magnetic resonance in medicine.

[22]  M. Kuroda,et al.  Composition of MRI phantom equivalent to human tissues. , 2005, Medical physics.

[23]  P. Sandercock,et al.  Comparison of 10 Different Magnetic Resonance Perfusion Imaging Processing Methods in Acute Ischemic Stroke: Effect on Lesion Size, Proportion of Patients With Diffusion/Perfusion Mismatch, Clinical Scores, and Radiologic Outcomes , 2007, Stroke.

[24]  C D Murray,et al.  The Physiological Principle of Minimum Work: I. The Vascular System and the Cost of Blood Volume. , 1926, Proceedings of the National Academy of Sciences of the United States of America.

[25]  D. Gadian,et al.  Delay and dispersion effects in dynamic susceptibility contrast MRI: Simulations using singular value decomposition , 2000, Magnetic resonance in medicine.

[26]  D. Gadian,et al.  Quantification of bolus‐tracking MRI: Improved characterization of the tissue residue function using Tikhonov regularization , 2003, Magnetic resonance in medicine.

[27]  Richard Frayne,et al.  Reexamining the quantification of perfusion MRI data in the presence of bolus dispersion , 2007, Journal of magnetic resonance imaging : JMRI.

[28]  G. Pawlik,et al.  Quantitative capillary topography and blood flow in the cerebral cortex of cats: an in vivo microscopic study , 1981, Brain Research.