1. ¹Department of Neurology, Henry Ford Health Systems, University of Michigan, Detroit, Michigan, USA
2. ²Department of Radiology, Henry Ford Health Systems, University of Michigan, Detroit, Michigan, USA
3. ³Department of Radiation Oncology, Henry Ford Health Systems, University of Michigan, Detroit, Michigan, USA
4. ⁴Department of Biostatistics and Research Epidemiology, Henry Ford Health Systems, University of Michigan, Detroit, Michigan, USA
5. ⁵Departments of Radiation Oncology and Radiology, Henry Ford Health Systems, University of Michigan, Detroit, Michigan, USA
6. ⁶Department of Anesthesiology, Henry Ford Health Systems, University of Michigan, Detroit, Michigan, USA

Correspondence: Dr JR Ewing, Neurology NMR Facility, E&R B126, Henry Ford Health Systems, 2799 W Grand Blvd, Detroit, MI 48202, USA. E-mail: jre@neurnis.neuro.hfh.edu

Received 15 February 2005; Revised 18 April 2005; Accepted 6 June 2005; Published online 3 August 2005.

Abstract

Vasculature in and around the cerebral tumor exhibits a wide range of permeabilities, from normal capillaries with essentially no blood–brain barrier (BBB) leakage to a tumor vasculature that freely passes even such large molecules as albumin. In measuring BBB permeability by magnetic resonance imaging (MRI), various contrast agents, sampling intervals, and contrast distribution models can be selected, each with its effect on the measurement's outcome. Using Gadomer, a large paramagnetic contrast agent, and MRI measures of T₁ over a 25-min period, BBB permeability was estimated in 15 Fischer rats with day-16 9L cerebral gliomas. Three vascular models were developed: (1) impermeable (normal BBB); (2) moderate influx (leakage without efflux); and (3) fast leakage with bidirectional exchange. For data analysis, these form nested models. Model 1 estimates only vascular plasma volume, v_D, Model 2 (the Patlak graphical approach) v_D and the influx transfer constant K_i. Model 3 estimates v_D, K_i, and the reverse transfer constant, k_b, through which the extravascular distribution space, v_e, is calculated. For this contrast agent and experimental duration, Model 3 proved the best model, yielding the following central tumor means (s.d.; n=15): v_D=0.070.03 for K_i=0.01050.005 min^-1 and v_e=0.100.04. Model 2 K_i estimates were approximately 30% of Model 3, but highly correlated (r=0.80, P<0.0003). Sizable inhomogeneity in v_D, K_i, and k_b appeared within each tumor. We conclude that employing nested models enables accurate assessment of transfer constants among areas where BBB permeability, contrast agent distribution volumes, and signal-to-noise vary.