Abstract #3873
Classical and Knowledge-Based Pharmacokinetic Model Selection Techniques in Analysis of Dynamic Contrast Enhanced MRI Studies: Performance and Bias Comparison
Hassan Bagher-Ebadian 1,2 , Mohammadreza Mohammadian-Behbahani 3,4 , Azimeh Noorizadeh Vahed Dehkordi 3,5 , James R Ewing 2,6 , Alireza Kamali-Asl 3 , Siamak P Nejad-Davarani 7 , Hamed Moradi 8 , Stephen Brown 2,9 , Brent Griffith 10 , Ali S Arbab 11 , Tom Mikkelsen 12 , Lisa Scarpace 12 , and Hamid Soltanian-Zadeh 1,13
1
Radiology and Research Administration, Henry
Ford Hospital, Detroit, Michigan, United States,
2
Physics,
Oakland University, Rochester, Michigan, United States,
3
Nuclear
Engineering, Shahid Beheshti University, Tehran, Iran,
4
Nuclear
Engineering, Amir-Kabir University of Technology,
Tehran, Iran,
5
Nuclear
Engineering, Najaf Abad Branch, Islamic Azad University,
Isfahan, Iran,
6
Neurology,
Henry Ford Hospital, Detroit, Michigan, United States,
7
Neurology,
Henry Ford Hospital, Michigan, Iran,
8
Nuclear
Engineering, Shiraz University, Shiraz, Fars, Iran,
9
Radiation
Oncology, Henry Ford Hospital, Detroit, Michigan, United
States,
10
Radiology, Henry Ford Hospital,
Detroit, Michigan, United States,
11
GRU
Cancer Center, Georgia Regents University, Atlanta,
Georgia, United States,
12
Neurosurgery, Henry
Ford Hospital, Detroit, Michigan, United States,
13
CIPCE,
School of Electrical and Computer Engineering,
University of Tehran, Tehran, Iran
Given Dynamic-Contrast-Enhanced MRI data, accurate
estimation of Pharmacokinetic (PK) parameters strongly
relies on appropriate selection of the best PK model to
fit the data. This study investigates the impact of
different classical and adaptive MS technique such as
F-Statistic (F-test), Akaike-Information-Criterion
(AIC), Bayesian-Information-Criterion (BIC),
Log-Likelihood-Ratio (LLR) and Artificial Neural Network
(ANN) on estimation of vascular permeability parameters.
Results imply that ANN generates significantly
less-biased estimates of PK parameters compared to other
techniques while both the LLR and BIC methods outperform
the other classical MS techniques, the ANN, LLR and BIC
are the best candidates for PK analysis of DCE-MRI data.
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