Generalized min-max bound-based MRI pulse sequence design framework for wide-range T(1) relaxometry: A case study on the tissue specific imaging sequence

This paper proposes a new design strategy for optimizing MRI pulse sequences for T(1) relaxometry. The design strategy optimizes the pulse sequence parameters to minimize the maximum variance of unbiased T(1) estimates over a range of T(1) values using the Cramér-Rao bound. In contrast to prior sequences optimized for a single nominal T(1) value, the optimized sequence using our bound-based strategy achieves improved precision and accuracy for a broad range of T(1) estimates within a clinically feasible scan time. The optimization combines the downhill simplex method with a simulated annealing process. To show the effectiveness of the proposed strategy, we optimize the tissue specific imaging (TSI) sequence. Preliminary Monte Carlo simulations demonstrate that the optimized TSI sequence yields improved precision and accuracy over the popular driven-equilibrium single-pulse observation of T(1) (DESPOT1) approach for normal brain tissues (estimated T(1) 700–2000 ms at 3.0T). The relative mean estimation error (MSE) for T(1) estimation is less than 1.7% using the optimized TSI sequence, as opposed to less than 7.0% using DESPOT1 for normal brain tissues. The optimized TSI sequence achieves good stability by keeping the MSE under 7.0% over larger T(1) values corresponding to different lesion tissues and the cerebrospinal fluid (up to 5000 ms). The T(1) estimation accuracy using the new pulse sequence also shows improvement, which is more pronounced in low SNR scenarios.