Optimising microstructural characterisation of white-matter phantoms: impact of gradient waveform modulation on Non-uniform Oscillating Gradient Spin-Echo sequences

Abstract

Changes in the nervous system due to neurological diseases take place at very small spatial scales, on the order of the micro and nanometers. Developing non-invasive imaging methods for obtaining this microscopic information as quantitative biomarkers is therefore crucial for improved medical diagnosis. In this context, diffusion weighted magnetic resonance imaging has shown significant advances in revealing tissue microstructural features by probing molecular diffusion processes. Implementing modulated gradient spin-echo sequences allows monitoring time-dependent diffusion processes to reveal such detailed information. In particular, one of those sequences termed Non-uniform Oscillating Gradient Spin-Echo (NOGSE), has shown to selectively characterise microstructure sizes by generating an image contrast based on a signal decay-shift rather than on the conventionally used signal decay rate. In this work, we prove that such decay-shift is more pronounced with instantaneous switches of the sign of the magnetic field gradient strength. As fast gradient ramps need to be avoided in clinical settings, due to potential patient discomfort and artefacts in imaging, we evaluate the method’s efficacy for estimating microstructure sizes using both idealised, sharp gradient modulations and more realistic, smooth modulations. In this more realistic scenario we find that the signal decay-shift might be lost as the diffusion time increases, likely hindering the accurate estimation of microstructural characteristics. We demonstrate, by a combination of numerical simulations, information theory analysis and proof-of-principle experiments with white-matter phantoms, that optimal sequence design to estimate microstructure size distributions can be achieved using either sharp or smooth gradient spin-echo modulations. This approach simplifies the translation of the NOGSE method for its use in clinical settings.