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Abstract #3495

A Real-time Solver for Convex Optimization of Diffusion Encoding Gradient Waveforms

Michael Loecher1, Matthew Middione1, Kévin Moulin1, and Daniel Ennis1

1Radiology, Stanford University, Stanford, CA, United States

Recent work has demonstrated the usefulness of designing time optimal gradient waveforms for diffusion encoding, especially when incorporating gradient moment nulling to mitigate bulk motion artifacts. Prolonged optimization times, however, can limit the usability of these methods. This work presents a solver fast enough to run in real-time, and investigates the performance over a range of parameters and tests for convergence. The solver is able to generate diffusion encoding gradient waveforms with gradient moment nulling in 5-50 ms – fast enough to permit real-time optimization on the scanner.

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