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Abstract #1134

Optimized Gradient Properties through Timing in K-Space (OPTIKS)

Matthew McCready1, Xiaozhi Cao2, Congyu Liao2, Kawin Setsompop1,2, John Pauly1, and Adam Kerr1,3
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Data Acquisition, Acquisition Methods

Motivation: Fast imaging sequences exploit higher gradient amplitudes and slew rates and efficient non-Cartesian k-space trajectories. With these improvements comes greater concern for PNS, acoustic noise, and system damage.

Goal(s): To design gradient waveforms which efficiently follow an arbitrary k-space trajectory while controlling time varying properties such as PNS, and frequency content.

Approach: We construct and solve an optimization problem for k-space speed across trajectory arclength. This determines the trajectory's time parameterization, and therefore the gradient waveform properties.

Results: We demonstrate 10% faster PNS-limited readout than conventional methods, up to 9dB decrease in acoustic noise, and up to 19x reduction in gradient coil vibrations.

Impact: We develop a novel customizable gradient waveform design method (OPTIKS) capable of optimizing time-dependent properties while adhering to any specified trajectory. We apply OPTIKS to design fast-PNS limited readouts, make imaging quieter, and reduce large prolonged gradient coil vibrations.

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Keywords