Abstract #4087
Comparison of Logan Plot Analysis and Nested Model Selection Technique for MR Estimation of Distribution Volume in Human Brain Tumor at 3Tesla
Hassan Bagher-Ebadian 1,2 , James R Ewing 2,3 , Siamak P. Nejad-Davarani 4,5 , Hamed Moradi 6 , Reza Faghihi 6 , Rajan Jain 7 , Tom Mikkelsen 8 , Lisa Scarpace 8 , and Hamid Soltanian-Zadeh 1,9
1
Radiology, Henry Ford Hospital, Detroit, MI,
United States,
2
Physics,
Oakland University, Rochester, MI, United States,
3
Neurology,
Henry Ford Hospital, MI, United States,
4
Neurology,
Henry Ford Hospital, Detroit, MI, United States,
5
Biomedical
Engineering, University of Michigan, Ann Arbor, MI,
United States,
6
Mechanical
Engineering, Shiraz University, Fars, Iran,
7
Radiology,
NYU Langone Medical Center, NY, United States,
8
Neurosurgery,
Henry Ford Hospital, Detroit, MI, United States,
9
CIPCE,
ECE Dept., University of Tehran, Tehran, Iran
In this study, Logan plot analysis was applied to
dynamic-contrast-enhanced MRI data of 15 patients with
Glioblastoma-Multiforme to estimate the tumor
distribution volume (VD). BDS (W.A.Brock, W.Dechert and
J.Scheinkman) statistic was used to identify the
equilibrium condition of the Logan curve.
Nested-Model-Selection (NMS) technique was also applied
to the same dataset. Results confirm that the VD values
estimated by the two techniques are quite in agreement
(0.946,p<0.001) while there is considerable variation
between subjects in both methods (VD:5% to 46% in
Logan-plot with mean and STD of VD=0.23%0.13% and 7% to
53% in NMS with mean and STD of VD=0.27%0.14%).
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