基于多模态MRI图像的耳软骨模型构建

doi:10.3969/j.issn.0258-8021.2019.06.009

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Abstract Constructing high-fidelity cartilage scaffolds has always been the research core of auricle reconstruction. Traditional imaging methods have a lower discrimination between auricular cartilage and surrounding tissues. In order to explore a new MRI scanning scheme for auricular cartilage imaging, and construct high-precision 3D bioprinting models of the auricular cartilage, this study proposed the combination of ultrashort time to echo (UTE) and 3D_T2 sequences. We collected unilateral external auricle images of 40 healthy volunteers. First, two experienced raters segmented auricular cartilages layer by layer based on UTE images, and remove other tissues such as earlobe according to the 3D_T2 images. Each rater repeated 3 times. Next, the inter- and intra-rater correlations between volume (Cg. V), surface area (Cg. S), and thickness (Cg. Th) of segmentation results were calculated to evaluate whether different raters could define the same area of interest. Results indicated that intra-rater precision errors(PE_CV)were Cg.V≤3.05%,Cg.S≤1.80%,Cg.Th≤3.43%, and the inter-rater PE_CV were Cg.V=2.39%,Cg.S=3.75%,Cg.Th=3.37%. The intra-class correlation coefficients (ICC) of Cg.V,Cg.S and Cg.Th were higher than 0.95, 0.97 and 0.77, the inter-class correlation coefficients were 0.97, 0.89 and 0.69, respectively. The Dice similarity coefficient (DSC) reached to above 80%. This study demonstrated that the combination of UTE and 3D_T2 was a scanning solution for auricle reconstruction research for its ability to characterize morphological differences in auricular cartilage and provide high precision auricular cartilage models for 3D bioprinting.

Key words： auricular cartilage segmentation ultrashort time to echo(UTE) 3D-T2 3D bioprinting model construction

Received: 23 April 2019

PACS:

R318

Corresponding Authors: E-mail: z_cheng@ibms.pumc.edu.cn

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	Liu Xiaofang
	Zhao Cheng

Cite this article:

Liu Xiaofang,Zhao Cheng. Construction of Auricular Cartilage ModelsBased on Multimodal MRI Images[J]. Chinese Journal of Biomedical Engineering, 2019, 38(6): 711-718.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.06.009 OR http://cjbme.csbme.org/EN/Y2019/V38/I6/711

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