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Abstract #4523

Schedule design for parameter quantification in the transient state using Bayesian optimisation

Giada Fallo1,2, Matteo Cencini2,3, Pedro A. Gómez4, Davide Bacciu1, Antonio Cisternino1, Michela Tosetti2, and Guido Buonincontri2

1Dipartimento di Informatica, Università di Pisa, Pisa, Italy, 2Stella Maris Scientific Institute and IMAGO7 Research Foundation, Pisa, Italy, 3Dipartimento di Fisica, Università di Pisa, Pisa, Italy, 4Computer Science, Technische Universitat Munchen, Munich, Germany

Magnetic resonance fingerprinting (MRF) is a useful tool for simultaneously obtaining multiple tissue-specific parameters in an efficient imaging experiment. This technique uses transient state acquisitions with pseudo-random acquisition parameters. However, specific schedules may be better suited for certain parameter ranges or sampling patterns. This work aims to introduce a framework for pulse sequence optimization, including aliasing and noise in our estimates, individually or jointly optimizing for T1 and T2 relaxation times. We demonstrated the schedules created by our algorithm using MRI acquisitions on a healthy volunteer. The design framework could improve the efficiency and accuracy of T1 and T2 acquisitions.

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