Quantitative estimation of thrombus-erythrocytes using MRI. A phantom study with clot analogs and analysis by statistic regression models

医学 成像体模 统计的 回归 回归分析 血栓 内科学 放射科 心脏病学 统计 数学
作者
Kévin Janot,Tiago R. Oliveira,Gaëlle Fromont,Mariam Annan,Isabelle Filipiak,Laurent Barantin,Roseline Guibon,Sharon Duffy,Michael Gilvarry,Jean-Philippe Cottier,Ana Paula Narata
出处
期刊:Journal of NeuroInterventional Surgery [BMJ]
卷期号:12 (2): 181-185 被引量:14
标识
DOI:10.1136/neurintsurg-2019-014950
摘要

Background Thrombus composition has the potential to affect acute ischemic stroke (AIS) treatment. Objective To evaluate in an in vitro test the correlation of clot composition, especially erythrocytes (red blood cells (RBCs)), with the variation of signal intensity ratio (SIR) obtained with MRI sequences used for AIS, and qualification of the susceptibility vessel sign effect using clot analogs. Materials and methods Nine ovine clots were fixed in a gelatin-manganese solution and studied by MRI (T2GE, T2-weighted gradient echo; SWI, susceptibility-weighted imaging; FLAIR, fluid attenuated inversion recovery). RBC concentration was estimated using regression models (SLR, single linear regression; MLR, multiple linear regression; RF, random Forest; and ANN, artificial neural networking), which combined the SIR–histology relationship of three MRI sequences. Results Negative correlation was found between SIR and RBC concentration. T2GE SWI could not statistically distinguish clots with RBC content >54% and <23%. SLR was applied only to FLAIR images since T2GE and SWI demonstrated signal saturation. All four regression models showed a correlation between MRI and histology: SLR=0.981; MLR=0.986; RF=0.994, and ANN=0.971. One unknown clot was studied and agreement between SIR and histological analyses was found in all models. Conclusions We presented a method to quantify RBC concentration in clot analogs, combining SWI, T2GE, and FLAIR. This in vitro study has some limitations, so clot collection after thrombectomy with simultaneous imaging analysis is necessary to validate this model.
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