Diffusion‐relaxation correlation spectroscopic imaging: A multidimensional approach for probing microstructure

Abstract

Purpose To propose and evaluate a novel multidimensional approach for imaging sub-voxel tissue compartments called Diffusion-Relaxation Correlation Spectroscopic Imaging (DR-CSI). Theory and Methods Multi-exponential modeling of MR diffusion or relaxation data is commonly used to infer the many different microscopic tissue compartments that contribute signal to macroscopic MR imaging voxels. However, multi-exponential estimation is known to be difficult and ill-posed. Observing that this ill-posedness is theoretically reduced in higher dimensions, DR-CSI uses a novel multidimensional imaging experiment that jointly encodes diffusion and relaxation information, and then uses a novel constrained reconstruction technique to generate a multidimensional diffusion-relaxation correlation spectrum for every voxel. The peaks of the multidimensional spectrum are expected to correspond to the distinct tissue microenvironments that are present within each macroscopic imaging voxel. Results Using numerical simulations, experiment data from a custom-built phantom, and experiment data from a mouse model of traumatic spinal cord injury, DR-CSI is demonstrated to provide substantially better multi-compartment resolving power compared to conventional diffusion- and relaxation-based methods. Conclusion The DR-CSI approach provides powerful new capabilities for resolving the different components of multi-compartment tissue models, and can be leveraged to significantly expand the insights provided by MRI in studies of tissue microstructure.

A diffusion model‐free framework with echo time dependence for free‐water elimination and brain tissue microstructure characterization

“…Interestingly, only a number of TE repetitions equal to the number of compartments that are assumed to be in the tissue is necessary. This results of the substitution of the ILTs by BSS, in comparison to other techniques . We found a good agreement between the estimates of the FLAIR multi‐echo SE for 17 TEs and those of BSS for 2 TEs.…”

“…Furthermore, our solution is diffusion protocol‐agnostic (only two TEs and one nondiffusion weighted volume are necessary), allowing for flexibility in the design of the acquisition protocol, which might include any number of diffusion directions and b ‐values. This gives it an advantage over diffusion–relaxation correlation techniques based on regularized ILTs …”

Magnetic resonance microdynamic imaging reveals distinct tissue microenvironments

“…To date, these multidimensional relaxation–diffusion correlation (REDCO) spectroscopy experiments have been primarily relegated to applications involving NMR studies in homogeneous samples (Silva et al, 2002; Galvosas and Callaghan, 2010; Bernin and Topgaard, 2013; Song et al, 2016). However, apart from a few studies (Zhang and Blümich (2014); Tax et al (2017); Kim et al (2017)), these methods have not been widely used in MRI applications owing to the vast amount of scan time and acquired MR data required to reconstruct a single multidimensional spectrum.…”

“…With that method, a maximal acceleration factor of 3.5 was achieved, reducing the number of acquisitions from 1,800 to 450, which is still infeasible for clinical MRI applications. A recent spatially resolved REDCO study reported one distinct D - T 2 peak in the WM region and a different distinct peak in the gray matter (GM) region of a spinal cord specimen (Kim et al, 2017). These results, specifically the monocompartmental tissue structure in WM, contradict many previous studies (e.g., Ronen et al (2006); Peled et al (1999); MacKay et al (2006)) and may be due to the very small number of acquisitions, 28, used to reconstruct the D - T 2 spectra, or to the use of spatial regularization.…”

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

“…Other studies have proposed methods that do not explicitly impose the number of compartments, but rather derive them from the data . Although prone to overfitting, such methods have been shown to provide good results in problems in which the number of components is difficult to establish a priori, such as crossing fibers or the joint diffusion– T 2 relaxometry quantification …”