An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies.

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 ve from a time trace of the longitudinal relaxation rate, ΔR1 (R1  = 1/T1 ), in DCE-MRI studies. Using the extended Tofts equation, a set of ΔR1 profiles 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 ΔR1 profiles 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 ve at a resolution of 10% (error of ±5%) was 82%. The ANN was applied on DCE-MRI data of 26 glioblastoma patients to estimate ve in 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 .