Abstract #1482

Rapid and Direct Quantification of Longitudinal Relaxation Time (T1) in Look-Locker Sequences Using an Adaptive Neural Network

Hassan Bagher-Ebadian^1,2, Ramesh Paudyal^1,2, Tom Mikkelsen³, Quan Jiang¹, James Russel Ewing^1,2

¹Neurology, Henry Ford Hospital, Detroit, MI, USA; ²Physics, Oakland University, Rochester, MI, USA; ³Neurosergery, Henry Ford Hospital, Detroit, MI, USA

In this study an adaptive neural network is employed for direct estimation of longitudinal relaxation time (T1) in Look-Locker signals. It is hypothesized that, given a signal generated by a LL model, an ANN could be trained to directly estimate T1. The analytical equation of the Look-Locker signal was considered as the gold standard of training and a set of LL signals for a wide range of T1s were generated. For each T1 value LL signal inputs were generated by varying the other independent parameters in the synthetic model of signal (T2*, M0 etc).