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利用多种正则化方法在正电子发射断层扫描(PET)中进行肿瘤同时分割、图像恢复和模糊核估计

Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations.

作者信息

Li Laquan, Wang Jian, Lu Wei, Tan Shan

机构信息

Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.

出版信息

Comput Vis Image Underst. 2017 Feb;155:173-194. doi: 10.1016/j.cviu.2016.10.002. Epub 2016 Oct 6.

Abstract

Accurate tumor segmentation from PET images is crucial in many radiation oncology applications. Among others, partial volume effect (PVE) is recognized as one of the most important factors degrading imaging quality and segmentation accuracy in PET. Taking into account that image restoration and tumor segmentation are tightly coupled and can promote each other, we proposed a variational method to solve both problems simultaneously in this study. The proposed method integrated total variation (TV) semi-blind de-convolution and Mumford-Shah segmentation with multiple regularizations. Unlike many existing energy minimization methods using either TV or regularization, the proposed method employed TV regularization over tumor edges to preserve edge information, and regularization inside tumor regions to preserve the smooth change of the metabolic uptake in a PET image. The blur kernel was modeled as anisotropic Gaussian to address the resolution difference in transverse and axial directions commonly seen in a clinic PET scanner. The energy functional was rephrased using the -convergence approximation and was iteratively optimized using the alternating minimization (AM) algorithm. The performance of the proposed method was validated on a physical phantom and two clinic datasets with non-Hodgkin's lymphoma and esophageal cancer, respectively. Experimental results demonstrated that the proposed method had high performance for simultaneous image restoration, tumor segmentation and scanner blur kernel estimation. Particularly, the recovery coefficients (RC) of the restored images of the proposed method in the phantom study were close to 1, indicating an efficient recovery of the original blurred images; for segmentation the proposed method achieved average dice similarity indexes (DSIs) of 0.79 and 0.80 for two clinic datasets, respectively; and the relative errors of the estimated blur kernel widths were less than 19% in the transversal direction and 7% in the axial direction.

摘要

在许多放射肿瘤学应用中,从PET图像中准确分割肿瘤至关重要。其中,部分容积效应(PVE)被认为是降低PET成像质量和分割准确性的最重要因素之一。考虑到图像恢复和肿瘤分割紧密相关且能相互促进,我们在本研究中提出了一种变分方法来同时解决这两个问题。所提出的方法将总变差(TV)半盲去卷积和具有多种正则化的Mumford-Shah分割相结合。与许多现有的使用TV或正则化的能量最小化方法不同,该方法在肿瘤边缘采用TV正则化以保留边缘信息,在肿瘤区域内部采用正则化以保留PET图像中代谢摄取的平滑变化。模糊核被建模为各向异性高斯分布,以解决临床PET扫描仪中常见的横向和轴向分辨率差异。能量泛函使用Γ收敛近似进行重新表述,并使用交替最小化(AM)算法进行迭代优化。所提出方法的性能在一个物理体模和分别患有非霍奇金淋巴瘤和食管癌的两个临床数据集上得到了验证。实验结果表明,该方法在同时进行图像恢复、肿瘤分割和扫描仪模糊核估计方面具有高性能。特别是,在体模研究中,该方法恢复图像的恢复系数(RC)接近1,表明能有效恢复原始模糊图像;对于分割,该方法在两个临床数据集上分别实现了平均骰子相似性指数(DSI)为0.79和0.80;估计的模糊核宽度在横向方向上的相对误差小于19%,在轴向方向上小于7%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cace/5463621/534ad92d1ccc/nihms831838f1.jpg

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本文引用的文献

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A review on segmentation of positron emission tomography images.正电子发射断层成像图像分割研究综述。
Comput Biol Med. 2014 Jul;50:76-96. doi: 10.1016/j.compbiomed.2014.04.014. Epub 2014 Apr 28.
4
A graph-theoretic approach for segmentation of PET images.一种用于PET图像分割的图论方法。
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:8479-82. doi: 10.1109/IEMBS.2011.6092092.
7
A new method for volume segmentation of PET images, based on possibility theory.基于可能性理论的 PET 图像体积分割新方法。
IEEE Trans Med Imaging. 2011 Feb;30(2):409-23. doi: 10.1109/TMI.2010.2083681. Epub 2010 Oct 14.

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