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Article

Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma

Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(1), 34-43; https://doi.org/10.18383/j.tom.2020.00018
Submission received: 10 December 2019 / Revised: 8 January 2020 / Accepted: 5 February 2020 / Published: 1 March 2020

Abstract

Apparent diffusion coefficient has limits to differentiate solid tumor from normal tissue or edema in glioblastoma (GBM). This study investigated a microstructure model (MSM) in GBM using a clinically available diffusion imaging technique. The MSM was modified to integrate with bi-polar diffusion gradient waveforms, and applied to 30 patients with newly diagnosed GBM. Diffusion-weighted (DW) images acquired on a 3 T scanner with b-values from 0 to 2500 s/mm2 were fitted in volumes of interest (VOIs) of solid tumor to obtain the apparent restriction size of intracellular water (ARS), the fractional volume of intracellular water (Vin), and extracellular (Dex) water diffusivity. The parameters in solid tumor were compared with those of other tissue types by Students’ t test. For comparison, DW images were fitted by conventional mono-exponential and bi-exponential models. ARS, Dex, and Vin from the MSM in tumor VOIs were significantly greater than those in WM, GM, and edema (P values of .01–.001). ARS values in solid tumors (from 21.6 to 34.5 um) had absolutely no overlap with those in all other tissue types (from 0.9 to 3.5 um). Vin values showed a descending order from solid tumor (from 0.32 to 0.52) to WM, GM, and edema (from 0.05 to 0.25), consisting with the descending cellularity in these tissue types. The parameters from mono-exponential and bi-exponential models could not significantly differentiate solid tumor from all other tissue types, particularly from edema. Further development and histopathological validation of the MSM will warrant its role in clinical management of GBM.
Keywords: Diffusion-weighted imaging; high-order diffusion MR methods; quantitation; glioblastoma Diffusion-weighted imaging; high-order diffusion MR methods; quantitation; glioblastoma

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MDPI and ACS Style

Li, Y.; Kim, M.; Lawrence, T.S.; Parmar, H.; Cao, Y. Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma. Tomography 2020, 6, 34-43. https://doi.org/10.18383/j.tom.2020.00018

AMA Style

Li Y, Kim M, Lawrence TS, Parmar H, Cao Y. Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma. Tomography. 2020; 6(1):34-43. https://doi.org/10.18383/j.tom.2020.00018

Chicago/Turabian Style

Li, Yuan, Michelle Kim, Theodore S. Lawrence, Hemant Parmar, and Yue Cao. 2020. "Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma" Tomography 6, no. 1: 34-43. https://doi.org/10.18383/j.tom.2020.00018

APA Style

Li, Y., Kim, M., Lawrence, T. S., Parmar, H., & Cao, Y. (2020). Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma. Tomography, 6(1), 34-43. https://doi.org/10.18383/j.tom.2020.00018

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