Deep learning-based acquisition protocol optimization and parameter estimation for diffusion exchange spectroscopy

We propose a deep-learning-based method to optimize acquisition protocol and estimate parameter for diffusion exchange spectroscopy. A unified framework has been carefully designed to achieve both goals simultaneously. Using this framework, the acquisition protocol can be directly optimized with an objective function that minimizes the parameter estimation errors, regardless of its degree of freedom. The experimental results from Monte Carlo simulations show that the proposed method outperforms the existing methods in both accuracy and precision of parameters’ estimation. Besides, it speeds up the calculation by 480 times.