Magnetic resonance estimation of longitudinal relaxation time (T1) in spoiled gradient echo using an adaptive neural network

Recently, the acquisition of high-resolution T₁ maps in a clinically feasible time frame has been demonstrated with Driven Equilibrium Single Pulse Observation of T₁ (DESPOT1). DESPOT1 derives the longitudinal relaxation time, T₁, from two or more spoiled gradient recalled echo (SPGR) images acquired with a constant T_R and different flip angles. In general, T₁ can be estimated from two or more SPGR images. Estimation of MR parameters (T₁, M₀, etc.) from these sequences is challenging and susceptible to the level of noise in signal acquisition. Methods such as Simplex Optimization, Weighted Non-Linear Least Squares (WNLS), Linear Least Square (LLS or Gupta´s LLS), and Intensity based Linear Least Square (ILLS) method have been employed to estimate T₁. In both linear and non-linear methods, the estimated T₁ values are highly dependent on defining the weighting factors; errors in these weighting factors can result in a biased estimate of T₁. In this study, an adaptive neural network (ANN) is introduced, trained and evaluated. The ANN was trained using an analytical model of the SPGR signal in the presence of different levels of signal to noise ratio (2 to 30). Receiver Operator Characteristic (ROC) analysis and the K-fold cross-validation (KFCV) method were employed to train, test, and optimize the network. The result (Az=0.81) shows that, compared to the other techniques, ANNs can provide a faster and unbiased estimate of T₁ from SPGR signals.