An automatic approach for estimating bolus arrival time in dynamic contrast MRI using piecewise continuous regression models.

We present two regression models for the automatic estimation of bolus arrival times (BATs) in dynamic contrast MRI datasets. Results of Monte Carlo simulation experiments show that the means and standard deviations of the estimated BATs are within the sampling interval even in the presence of significant noise.

[1]  K. Zierler.  Equations for Measuring Blood Flow by External Monitoring of Radioisotopes , 1965, Circulation research.

[2]  William H. Press,et al.  Numerical Recipes: FORTRAN , 1988 .

[3]  Ting-Yim Lee,et al.  An Adiabatic Approximation to the Tissue Homogeneity Model for Water Exchange in the Brain: I. Theoretical Derivation , 1998, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[4]  Philip E. Gill,et al.  Practical optimization , 1981 .

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

[6]  David L. Thomas,et al.  Measuring Cerebral Blood Flow Using Magnetic Resonance Imaging Techniques , 1999, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

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

[8]  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.

[9]  T S Koh,et al.  The inclusion of capillary distribution in the adiabatic tissue homogeneity model of blood flow. , 2001, Physics in medicine and biology.