Whole-Brain Imaging of Subvoxel T1-Diffusion Correlation Spectra in Human Subjects.

Alexandru V Avram,Joelle E Sarlls,Peter J Basser

doi:10.3389/fnins.2021.671465

Abstract

T1 relaxation and water mobility generate eloquent MRI tissue contrasts with great diagnostic value in many neuroradiological applications. However, conventional methods do not adequately quantify the microscopic heterogeneity of these important biophysical properties within a voxel, and therefore have limited biological specificity. We describe a new correlation spectroscopic (CS) MRI method for measuring how T1 and mean diffusivity (MD) co-vary in microscopic tissue environments. We develop a clinical pulse sequence that combines inversion recovery (IR) with single-shot isotropic diffusion encoding (IDE) to efficiently acquire whole-brain MRIs with a wide range of joint T1-MD weightings. Unlike conventional diffusion encoding, the IDE preparation ensures that all subvoxel water pools are weighted by their MDs regardless of the sizes, shapes, and orientations of their corresponding microscopic diffusion tensors. Accordingly, IR-IDE measurements are well-suited for model-free, quantitative spectroscopic analysis of microscopic water pools. Using numerical simulations, phantom experiments, and data from healthy volunteers we demonstrate how IR-IDE MRIs can be processed to reconstruct maps of two-dimensional joint probability density functions, i.e., correlation spectra, of subvoxel T1-MD values. In vivo T1-MD spectra show distinct cerebrospinal fluid and parenchymal tissue components specific to white matter, cortical gray matter, basal ganglia, and myelinated fiber pathways, suggesting the potential for improved biological specificity. The one-dimensional marginal distributions derived from the T1-MD correlation spectra agree well with results from other relaxation spectroscopic and quantitative MRI studies, validating the T1-MD contrast encoding and the spectral reconstruction. Mapping subvoxel T1-diffusion correlations in patient populations may provide a more nuanced, comprehensive, sensitive, and specific neuroradiological assessment of the non-specific changes seen on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted MRIs (DWIs) in cancer, ischemic stroke, or brain injury.

Highlights

T1-weighted (T1W) MRIs, such as fluid-attenuated inversion recovery (FLAIR) (Bydder and Young, 1985; De Coene et al, 1992; Hajnal et al, 1992) or MP-RAGE (Mugler and Brookeman, 1990) images reflect differences in average T1 relaxation times of tissues
We develop a new method for mapping two-dimensional probability density functions of subvoxel T1-mean diffusivity (MD) values in human subjects, thereby addressing a critical step in the clinical translation of multidimensional CSMRI
We describe how IR-isotropic diffusion encoding (IDE) MRIs with multiple weightings can be processed to derive T1-MD correlation spectra in brain tissues

Summary

Introduction

T1-weighted (T1W) MRIs, such as fluid-attenuated inversion recovery (FLAIR) (Bydder and Young, 1985; De Coene et al, 1992; Hajnal et al, 1992) or MP-RAGE (Mugler and Brookeman, 1990) images reflect differences in average T1 relaxation times of tissues. Diffusion-weighted MRIs (DWIs) reflect differences in average tissue water diffusion properties (Bihan et al, 1986; Moseley et al, 1990), such as the mean apparent diffusion coefficients (mADCs), or the mean diffusivities (MDs) (Basser et al, 1994) Both T1- and diffusion-weighted MRIs provide excellent tissue contrasts and are indispensable in many neurological and neuroradiological applications. The enduring utility of T1W and DWIs in radiological sciences is strong evidence of the high sensitivity of T1 and diffusion to a wide range of pathophysiological processes and motivates efforts to advance the quantitative mapping of these important biophysical tissue properties Despite their widespread use, conventional weighted MRIs do not directly quantify the underlying T1 and diffusion tissue properties. There is a critical need to quantify the microscopic heterogeneity of these parameters in healthy and diseased brain tissues using a model-free, nonparametric approach (Avram et al, 2019)

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Conclusion