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 Zhu^1,2,3, Jeremiah Liu⁴, Neha Koonjoo^1,2,3, Bruce R. Rosen^1,2, and Matthew S Rosen^1,2,3

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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