Research Progress in Diffusion Spectrum Imaging

Abstract

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.

White matter microstructure alterations in type 2 diabetes mellitus and its correlation with cerebral small vessel disease and cognitive performance

“…(4) some fiber bundles (CH and AF) were adjacent to gray matter and cannot be entirely quantified by AFQ, we cannot know their microstructure changes;(5) due to the cerebral white matter voxel contains multiple fiber populations and complex fiber geometries, DTI metrics should be cautiously interpreted, as they are indirect measures of real biological structures. We would encourage future studies to use other MRI approach that are more robust to the presence of crossing fibers such as diffusion spectrum imaging (DSI) 60 , q-space imaging (QSI) 61 , high-angular-resolution diffusion imaging (HARDI) 62 , and diffusion kurtosis imaging (DKI) 63 , etc.…”

Mapping Stiffness Landscape of Heterogeneous and Anisotropic Fibrous Tissue

“…Similar to MRE, our proposed model can also be affected by this limitation as it uses DTI data for the construction of fiber paths. Recently, advanced diffusion imaging techniques, such as high-angular-resolution diffusion imaging (HARDI) and diffusion spectrum imaging (DSI), have been developed to address this limitation [113][114][115] . These techniques allow for a more detailed characterization of complex fiber orientations and provide a more accurate representation of fiber crossings.…”