OPTIKS: Optimized Gradient Properties Through Timing in K-Space
Matthew A. McCready, Xiaozhi Cao, Kawin Setsompop, John M. Pauly, Adam B. Kerr
TL;DR
OPTIKS introduces a differentiable, arc-length-based framework for customizing gradient waveforms to follow pre-defined k-space trajectories while optimizing time-domain properties. By integrating time-domain constraints, PNS limits, mechanical resonance avoidance, and acoustic noise reduction into a single loss, OPTIKS enables rapid, safe, and quiet MRI gradient designs without deviating from prescribed trajectories. The approach is demonstrated on spirals, rosettes, CEPI, and MR fingerprinting trajectories, achieving substantial reductions in back-EMF and acoustic output while maintaining image quality, and is released as an open-source package. The work highlights trade-offs between speed and safety/quietness, and provides a flexible tool for trajectory-constrained gradient synthesis with extensible objective terms.
Abstract
A customizable method (OPTIKS) for designing fast trajectory-constrained gradient waveforms with optimized time domain properties was developed. Given a specified multidimensional k-space trajectory, the method optimizes traversal speed (and therefore timing) with position along the trajectory. OPTIKS facilitates optimization of objectives dependent on the time domain gradient waveform and the arc-length domain k-space speed. OPTIKS is applied to design waveforms which limit peripheral nerve stimulation (PNS), minimize mechanical resonance excitation, and reduce acoustic noise. A variety of trajectory examples are presented including spirals, circular echo-planar-imaging, and rosettes. Design performance is evaluated based on duration, standardized PNS models, field measurements, gradient coil back-EMF measurements, and calibrated acoustic measurements. We show reductions in back-EMF of up to 94% and field oscillations up to 91.1%, acoustic noise decreases of up to 9.22 dB, and with efficient use of PNS models speed increases of up to 11.4%. The design method implementation is made available as an open source Python package through GitHub.
