Abstract Magnetic resonance fingerprinting (MRF) is a quantitative technique that is able to produce maps of tissue property values in a single and rapid acquisition. MRF has been shown to be sensitive to subtle changes in both normal and diseased tissues in the brain, prostate, breast, and abdomen, yet increased sensitivity is desired for clinical applications. A comprehensive optimization of the MRF acquisition is required to achieve higher sensitivity and faster acquisitions. We propose to apply quantum inspired optimization (QIO) techniques to solve the problem of MRF optimization. QIO methods are effective in handling large and nonconvex problems such as this one, and we propose to apply these algorithms to optimize both sequence parameters such as flip angle, repetition time, and echo time, as well as the sampling trajectories. The objective function to be optimized will be designed to include characteristics such as signal magnitude, pattern matching metrics, and T1 and T2 errors. Sequences will be tested in phantom and in vivo, and compared to current literature on MRF optimization. Such a comprehensive optimization of MRF has not been performed, and by applying these novel computational methods, we will achieve a MRF sequence that is faster and is more sensitive to changes in tissue properties for the purposes of disease detection, characterization, and monitoring.

Project Narrative An optimized framework for magnetic resonance fingerprinting (MRF) is essential in making quantitative MR part of the routine clinical workflow. MRF has already shown high sensitivity in tissue characterization using a rapid multiparametric approach. With a comprehensive optimization using novel computing techniques, MRF will have broad clinical impact in the detection, characterization, and treatment monitoring in a wide variety of diseases.