Single-Shell NODDI Using Dictionary Learner Estimated Isotropic Volume Fraction

TL;DR

This study introduces a dictionary learning approach to estimate NODDI microstructural parameters from single-shell diffusion MRI data, enabling robust brain tissue analysis without multi-shell scans.

Contribution

It presents a novel method using dictionary learning to accurately estimate NODDI parameters from single-shell data, overcoming previous fitting failures.

Findings

DLpN achieves comparable NDI and ODI estimates to multi-shell NODDI.

Single-shell protocol with b=2000 s/mm2 yields ~5% error in white and grey matter.

Method enables NODDI analysis on existing single-shell datasets.

Abstract

Neurite orientation dispersion and density imaging (NODDI) enables the assessment of intracellular, extracellular and free water signals from multi-shell diffusion MRI data. It is an insightful approach to characterize brain tissue microstructure. Single-shell reconstruction for NODDI parameters has been discouraged in previous studies caused by failure when fitting, especially for the neurite density index (NDI). Here, we investigated the possibility of creating robust NODDI parameter maps with single-shell data, using the isotropic volume fraction (fISO) as prior. Prior estimation was made independent of the NODDI model constraint using a dictionary learning approach. First, we used a stochastic sparse dictionary-based network (DictNet) in predicting fISO which is trained with data obtained from in vivo and simulated diffusion MRI data. In single-shell cases, the mean diffusivity (MD) and raw T2 signal with no diffusion weighting (S0) was incorporated in the dictionary for the fISO estimation. Then, the NODDI framework was used with the known fISO to estimate the NDI and orientation dispersion index (ODI). The fISO estimated by our model was compared with other fISO estimators in the simulation. Further, using both synthetic data simulation and human data collected on a 3T scanner, we compared the performance of our dictionary-based learning prior NODDI (DLpN) with the original NODDI for both single-shell and multi-shell data. Our results suggest that DLpN derived NDI and ODI parameters for single-shell protocols are comparable with original multi-shell NODDI, and protocol with b=2000 s/mm2 performs the best (error ~5% in white and grey matter). This may allow NODDI evaluation of studies on single-shell data by multi-shell scanning of two subjects for DictNet fISO training.