title: An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies.
abstract: Extravascular extracellular space (ve) is a key parameter to characterize the tissue of cerebral tumors. This study introduces an artificial neural network (ANN) as a fast, direct, and accurate estimator of vefrom a time trace of the longitudinal relaxation rate, ΔR1(R1= 1/T1), in DCE-MRI studies. Using the extended Tofts equation, a set of ΔR1profiles was simulated in the presence of eight different signal to noise ratios. A set of gain- and noise-insensitive features was generated from the simulated ΔR1profiles and used as the ANN training set. A K-fold cross-validation method was employed for training, testing, and optimization of the ANN. The performance of the optimal ANN (12:6:1, 12 features as input vector, six neurons in hidden layer, and one output) in estimating veat a resolution of 10% (error of ±5%) was 82%. The ANN was applied on DCE-MRI data of 26 glioblastoma patients to estimate vein tumor regions. Its results were compared with the maximum likelihood estimation (MLE) of ve. The two techniques showed a strong agreement (r = 0.82, p < 0.0001). Results implied that the perfected ANN was less sensitive to noise and outperformed the MLE method in estimation of ve.
authors.affiliation: Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran.
authors.forename: Azimeh N V
authors.initials: AN
authors.lastname: Dehkordi
authors.affiliation: Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran.
authors.forename: Alireza
authors.initials: A
authors.lastname: Kamali-Asl
authors.affiliation: Department of Physics, Oakland University, Rochester, Michigan, USA.|Department of Neurology, Henry Ford Hospital, Detroit, Michigan, USA.
authors.forename: James R
authors.initials: JR
authors.lastname: Ewing
authors.affiliation: Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA.
authors.forename: Ning
authors.initials: N
authors.lastname: Wen
authors.affiliation: Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA.
authors.forename: Indrin J
authors.initials: IJ
authors.lastname: Chetty
authors.affiliation: Department of Physics, Oakland University, Rochester, Michigan, USA.|Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA.
authors.forename: Hassan
authors.initials: H
authors.lastname: Bagher-Ebadian
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000378:metabolism / Q000473:pathology; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D019786:Gadolinium DTPA / Q000493:pharmacokinetics*; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000378:metabolism / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D009389:Neovascularization, Pathologic / Q000000981:diagnostic imaging* / Q000378:metabolism* / Q000473:pathology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA
keywords: DCE-MRI; artificial neural network; extravascular extracellular space; maximum likelihood estimation; pharmacokinetics