Optimization methods for magnetic resonance imaging gradient waveform design. (27th April 2020)

• Record Type: Journal Article
• Authors: Middione, Matthew J.; Loecher, Michael; Moulin, Kévin; Ennis, Daniel B.
• Other Names: Spencer Richard G. guestEditor.
• Is Part Of: NMR in biomedicine. Volume 33:Number 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-27
• Subjects: acquisition methods -- applications -- diffusion methods -- diffusion MR sequences -- flow imaging methods -- flow quantitation -- methods and engineering -- methods and engineering -- methods and engineering -- other applications; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4308
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : The development and implementation of novel MRI pulse sequences remains challenging and laborious. Gradient waveforms are typically designed using a combination of analytical and ad hoc methods to construct each gradient waveform axis independently. This strategy makes coding the pulse sequence complicated, in addition to being time inefficient. As a consequence, nearly all commercial MRI pulse sequences fail to maximize use of the available gradient hardware or efficiently mitigate physiological effects. This results in expensive MRI systems that underperform relative to their inherent hardware capabilities. To address this problem, a software solution is proposed that incorporates numerical optimization methods into MRI pulse sequence programming. Examples are shown for rotational variant vs. invariant waveform designs, acceleration nulled velocity encoding gradients, and mitigation of peripheral nerve stimulation for diffusion encoding. The application of optimization methods to MRI pulse sequence design incorporates gradient hardware limits and the prescribed MRI protocol parameters (e.g. field‐of‐view, resolution, gradient moments, and/or b ‐value) to simultaneously construct time‐optimal gradient waveforms. In many cases, the resulting constrained gradient waveform design problem is convex and can be solved on‐the‐fly on the MRI scanner. The result is a set of multi‐axis time‐optimal gradient waveforms that satisfy the design constraints, thereby increasing SNR‐efficiency. These optimization methods can also be used to mitigate imaging artifacts (e.g. eddy currents) or account for peripheral nerve stimulation. The result of the optimization method is to enable easier pulse sequence gradient waveform design and permit on‐the‐fly implementation for a range of MRI pulse sequences. Abstract : Most MRI pulse sequences fail to make optimal use of the available gradient hardware due to the use of ad hoc design choices. These ad hoc strategies complicate gradient waveform design, are not typically time‐optimal, and are not amenable to including complex design constraints. This paper outlines a generalized, open‐source optimization approach that incorporates hardware, protocol, physiological, and correction constraints for on‐the‐fly time‐optimal gradient waveform design.