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

Extravascular extracellular space (v_e ) 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 v_e 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 v_e at a resolution of 10% (error of ±5%) was 82%. The ANN was applied on DCE-MRI data of 26 glioblastoma patients to estimate v_e in tumor regions. Its results were compared with the maximum likelihood estimation (MLE) of v_e . 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 v_e .