Feasibility of IVIM parameters from diffusion-weighted imaging at 11.7T MRI for detecting ischemic changes in common carotid artery occlusion rats

Author:

Fujiwara ShunrouORCID,Mori Yuki,de la Mora Daniela Martinez,Akamatsu Yosuke,Yoshida Kenji,Shibata Yuji,Masuda Tomoyuki,Ogasawara Kuniaki,Yoshioka Yoshichika

Abstract

AbstractThis study aimed to investigate whether intravoxel incoherent motion (IVIM) parameters can identify ischemic changes in the rat cerebral cortex using a preclinical ultra-high-field 11.7 Tesla magnetic resonance imaging (11.7TMRI) scanner. In nine female Wistar rats (eight weeks old), diffusion-weighted imaging (DWI) for IVIM analysis was successfully performed before (Pre) and after unilateral (UCCAO) and bilateral (BCCAO) common carotid artery occlusion. From the acquired DWI signals averaged in six regions of interest (ROI) placed on the cortex, volume fraction of perfusion compartment (F), pseudo diffusion coefficient (D*), F × D* and apparent diffusion coefficient (ADC) were determined as IVIM parameters in the following three DWI signal models: the bi-exponential, kurtosis, and tri-exponential model. For a subgroup analysis, four rats that survived two weeks after BCCAO were assigned to the long survival (LS) group, whereas the non-LS group consisted of the remaining five animals. Each IVIM parameter change among three phases (Pre, UCCAO and BCCAO) was statistically examined in each ROI. Then, the change in each rat group was also examined for subgroup analysis. All three models were able to identify cerebral ischemic change and damage as IVIM parameter change among three phases. Furthermore, the kurtosis model could identify the parameter changes in more regions than the other two models. In the subgroup analysis with the kurtosis model, ADC in non-LS group significantly decreased between UCCAO and BCCAO but not in LS group. IVIM parameters at 11.7TMRI may help us to detect the subtle ischemic change; in particular, with the kurtosis model.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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