Abstract #0812

Flexible and Efficient Cramér-Rao Bound Optimization for Magnetic Resonance Fingerprinting using Automatic Differentiation of Bloch Simulations

Philip K. Lee^1,2, Lauren E. Watkins^2,3, Timothy I. Anderson¹, Guido Buonincontri⁴, and Brian A. Hargreaves^1,2,3

¹Electrical Engineering, Stanford University, Stanford, CA, United States, ²Radiology, Stanford University, Stanford, CA, United States, ³Bioengineering, Stanford University, Stanford, CA, United States, ⁴IRCCS Fondazione Stella Maris and Fondazione Imago7, Pisa, Italy

The Cramér-Rao Lower Bound (CRLB) is a metric for optimizing quantitative sequences that requires an analytical expression for the signal. The CRLB for Magnetic Resonance Fingerprinting (MRF) has a complex formulation that makes it difficult to account for system imperfections or relevant signal contributions such as diffusion. We apply automatic differentiation to Bloch simulations and choose flip angles and repetition times that optimize the CRLB of the MRF sequence without deriving an explicit analytical expression for the signal. This method is computationally efficient and can easily be extended to include B0 and B1 inhomogeneities. Results are validated with in-vivo measurements.

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