Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique

Thomas R. Barrick,Jeremy Madigan,Carson Ingo,Catherine A. Spilling,Timothy L. Jones,Philip Rich,Matt G. Hall,Jeremy D. Isaacs,Franklyn A. Howe,Richard L. Magin

doi:10.1016/j.neuroimage.2020.116606

Abstract

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1–4 min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps.QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s−1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value.We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena.

Highlights

Over the last decade there have been extensive advances in acquisition and analysis of diffusion magnetic resonance imaging data but these have not been routinely translated into clinical practice
We show that quasidiffusion tensor imaging (QDTI) provides maps analogous to Diffusional Kurtosis Imaging (DKI) and that QDTI is capable of estimating D and α within image voxels where DKI fails
Axial α values exhibited diffusion dynamics closer to Gaussianity than radial α values (Table 1). α anisotropy was smaller in magnitude than D1,2 anisotropy and was significantly smaller in brain tissue

Summary

Introduction

Over the last decade there have been extensive advances in acquisition and analysis of diffusion magnetic resonance imaging (dMRI) data but these have not been routinely translated into clinical practice. One reason for this is the frequently lengthy acquisition times required to provide adequate image quality. This can be prohibitive, for example, in acute stroke where there is a need for rapid data acquisition as patients are not always able to remain motionless throughout lengthy MRI protocols. We introduce a novel dMRI technique, Quasi-Diffusion MRI (QDI), which utilises a rapid scan acquisition that can be acquired on clinical MR systems and provides high tissue contrast images. The QDI technique provides a coherent framework for data acquisition and processing to give all conventional dMRI contrasts plus images analogous to Diffusional Kurtosis Imaging (DKI) (Jensen et al, 2005; Jensen and Helpern, 2010)

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