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利用整合了物质分数和结构响应的重建模型进行保形电子清洗。

Fold-preserving electronic cleansing using a reconstruction model integrating material fractions and structural responses.

机构信息

School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea.

出版信息

IEEE Trans Biomed Eng. 2013 Jun;60(6):1546-55. doi: 10.1109/TBME.2013.2238937. Epub 2013 Jan 16.

Abstract

In this paper, we propose an electronic cleansing method using a novel reconstruction model for removing tagged materials (TMs) in computed tomography (CT) images. To address the partial volume (PV) and pseudoenhancement (PEH) effects concurrently, material fractions and structural responses are integrated into a single reconstruction model. In our approach, colonic components including air, TM, an interface layer between air and TM, and an interface layer between soft-tissue (ST) and TM (IL ST/TM ) are first segmented. For each voxel in IL ST/TM, the material fractions of ST and TM are derived using a two-material transition model, and the structural response to identify the folds submerged in the TM is calculated by the rut-enhancement function based on the eigenvalue signatures of the Hessian matrix. Then, the CT density value of each voxel in IL ST/TM is reconstructed based on both the material fractions and structural responses. The material fractions remove the aliasing artifacts caused by a PV effect in IL ST/TM effectively while the structural responses avoid the erroneous cleansing of the submerged folds caused by the PEH effect. Experimental results using ten clinical datasets demonstrated that the proposed method showed higher cleansing quality and better preservation of submerged folds than the previous method, which was validated by the higher mean density values and fold preservation rates for manually segmented fold regions.

摘要

在本文中,我们提出了一种使用新颖的重建模型进行电子清洗的方法,用于去除计算机断层扫描(CT)图像中的标记材料(TM)。为了同时解决部分容积(PV)和伪增强(PEH)效应的问题,我们将材料分数和结构响应集成到了一个单一的重建模型中。在我们的方法中,首先对包括空气、TM、空气和 TM 之间的界面层以及软组织(ST)和 TM 之间的界面层(IL ST/TM)在内的结肠成分进行分割。对于 IL ST/TM 中的每个体素,使用双材料转换模型来获得 ST 和 TM 的材料分数,而用于识别淹没在 TM 中的褶皱的结构响应则是通过基于 Hessian 矩阵特征值的 Rut-Enhancement 函数计算得出的。然后,根据材料分数和结构响应来重建 IL ST/TM 中每个体素的 CT 密度值。材料分数可以有效地去除 IL ST/TM 中由 PV 效应引起的混叠伪影,而结构响应可以避免由 PEH 效应引起的错误清洗淹没褶皱。使用十个临床数据集进行的实验结果表明,与先前的方法相比,所提出的方法显示出更高的清洗质量和更好的褶皱保留能力,这可以通过手动分割褶皱区域的更高平均密度值和褶皱保留率得到验证。

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