Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
An Introduction to Neural Networks
An Introduction to Neural Networks
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Fast and accurate measurement of the longitudinal relaxation time, T1 has become increasingly important in quantitative estimates of such tissue physiological parameters as perfusion, capillary permeability, and tissue interstitial space using dynamic contrast-enhanced MRI (DCE-MRI). The Look-Locker (LL) sequence provides accurate T1 estimates, with the advantages of reduced acquisition time, and a wide range of sampling times post-inversion. In this study, an Adaptive Neural Network (ANN) was trained and employed as an unbiased estimator of T1. The ANN estimator was trained by simulating the LL signal at different levels of SNR. The results of its application to the simulated data were compared with T1 maps estimated by conventional methods (Simplex method with non-negative least-squares fitting). Experimental results of the ANN method for 19 animals were also compared to the the conventional method, and with values of T1 reported in literature. The ANN and conventional methods produce estimates that are highly correlated in normal (r=0.957, p 1 map in tissue, and thus to estimate from LL data in DCE studies the temporal change in tissue R1 that occurs after administration of contrast agent, a measure that plays an important role in quantification of physiological parameters using MRI.