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Abstract #1090

AUTOmated pulse SEQuence generation (AUTOSEQ) and neural network decoding for fast quantitative MR parameter measurement using continuous and simultaneous RF transmit and receive

Bo Zhu1,2,3, Jeremiah Liu4, Neha Koonjoo1,2,3, Bruce R. Rosen1,2, and Matthew S Rosen1,2,3

1Radiology, MGH Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Radiology, Harvard Medical School, Boston, MA, United States, 3Physics, Harvard University, Cambridge, MA, United States, 4Biostatistics, Harvard University, CAMBRIDGE, MA, United States

Limited human intuition of the Bloch equations’ nonlinear dynamics, particularly over long periods of non-steady-state time evolution or in regimes such as off-resonance excitation, is an obstacle to fully exploiting the vast parameter space of potential MR pulse sequences. Our previous work introduced a computational graph approach to modeling the Bloch equations. In this work, we show the AUTOSEQ framework extended with a multilayer fully-connected neural network to perform fast quantitative MR parameter measurement. By employing continuous off-resonant excitation with simultaneous continuous receive, we demonstrate in simulated experiments the ability to quantify T1 and T2 parameters in a single TR.

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