Comparison of different tensor encoding combinations in microstructural parameter estimation

Afzali Deligani, Maryam, Tax, Chantal MW ORCID: https://orcid.org/0000-0002-7480-8817, Chatziantoniou, Cyrano and Jones, Derek K ORCID: https://orcid.org/0000-0003-4409-8049 2019. Comparison of different tensor encoding combinations in microstructural parameter estimation. Presented at: IEEE International Symposium on Biomedical Imaging, Venice, Italy, 8-11 Apr 2019. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, pp. 1471-1474. 10.1109/ISBI.2019.8759100

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Abstract

Diffusion-weighted magnetic resonance imaging is a noninvasive tool to investigate the brain white matter microstructure. It provides the information to estimate the compartmental diffusion parameters. Several studies in the literature have shown that there is degeneracy in the estimated parameters using traditional linear diffusion encoding (Stejskal-Tanner pulsed gradient spin echo). Multiple strategies have been proposed to solve degeneracy, however, it is not clear if those methods would completely solve the problem. One of the approaches is b-tensor encoding. The combination of linear-spherical tensor encoding (LTE+STE) and linear-planar (LTE+PTE) have been utilized to make the estimations stable in the previous works. In this paper, we compare the results of fitting a two-compartment model using different combinations of b-tensor encoding. The four different combinations linear-spherical (LTE+STE), linear-planar (LTE+PTE), planar-spherical (PTE+STE) and linear-planar-spherical (LTE+PTE+STE) have been compared. The results show that the combination of tensor encodings leads to lower bias and higher precision in the parameter estimates than single tensor encoding.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Psychology
Publisher:	IEEE
ISBN:	9781538636428
Last Modified:	25 Oct 2023 06:16
URI:	https://orca.cardiff.ac.uk/id/eprint/126615

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