Model Selection in Magnetic Resonance Imaging Measurements of Vascular Permeability: Gadomer in a 9L Model of Rat Cerebral Tumor

James R Ewing, Stephen L Brown, Mei Lu, Swayamprava Panda, Guangliang Ding, Robert A Knight, Yue Cao, Quan Jiang, Tavarekere N Nagaraja, Jamie L Churchman, Joseph D Fenstermacher
2005 Journal of Cerebral Blood Flow and Metabolism  
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
more » ... t agent, and MRI measures of T 1 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 (7s.d.; n ¼ 15): v D ¼ 0.07 70.03 for K i ¼ 0.010570.005 min À1 and v e ¼ 0.1070.04. Model 2 K i estimates were approximately 30% of Model 3, but highly correlated (r ¼ 0.80, Po0.0003). 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.
doi:10.1038/sj.jcbfm.9600189 pmid:16079791 fatcat:4psi6voipzeazheh5htyvfwwge