In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI

Abstract

Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, $\omega$, in addition to the diffusion tensor, $D$, and relaxation, $R_1$, $R_2$, correlations. A $D\left(\omega \right)-R_1-R_2$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm³ voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm³ voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their $D\left(\omega \right)-R_1-R_2$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

Keywords: denoising; diffusion tensor distribution; diffusion time dependency; diffusion-relaxation; human brain.