Optimization of Quantitative Sequences Using Automatic Differentiation of Bloch Simulations
We apply automatic differentiation (widely used in deep learning) and a simple Bloch simulation to efficiently optimize any sequence that can be simulated. Automatic differentiation allows us to optimize sequences without an analytical expression for the magnetization.
The code is available here.
Automatic differentiation allows us to optimize Magnetic Resonance Fingerprinting sequences an order of magnitude faster than conventional methods with 10x fewer lines of code.
Lee PK, Watkins LE, Anderson TI, Buonincontri G, Hargreaves BA. Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations. Magn Reson Med. 2019 May 26. doi: 10.1002/mrm.27832.