Dependence of Scan Parameters on Nerve Fiber Crossing Depiction in Diffusion Spectrum Imaging in Clinical Practice

Tang, Minghui; Oshinomi, Kazuya; Ishizaka, Kinya; Tha, Khin Khin; Yamamoto, Toru

doi:10.1097/rct.0000000000000715

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…CS-DSI may be the most optimized scheme among them in the future, which were used to undersample DSI data (i.e., hemispherical scheme) and reconstruct diffusional signal to achieve the scanning-time reduction [86]. Apart from the gradient direction and b max value, other parameters possibly affect the scanning time, such as the TR, FOV, and bandwidth, which could be optimized to improve the DSI scan scheme [87]. Secondly, with regard to clinical applications, future studies should combine more metrics, such as ODF-and PDF-based measures and hybrid diffusion imaging [88], to examine the underlying pathomechanism of the diseases.…”

Section: Limitations and Future Outlooksmentioning

confidence: 99%

Research Progress in Diffusion Spectrum Imaging

Sun,

Huang,

Wang

et al. 2023

Brain Sciences

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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.

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Section: Limitations and Future Outlooksmentioning

confidence: 99%