Statistical learning in computed tomography image estimation
Statistical learning in computed tomography image estimation
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DOI：
10.1002/mp.13204
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发表时间：
2018-12-01
期刊：
影响因子：
3.8
通讯作者：
Yu, Jun
中科院分区：
文献类型：
--
作者：
Bayisa, Fekadu L.;Liu, Xijia;Yu, Jun
目的从磁共振（MR）图像中估计计算机断层扫描（CT）图像的方法越来越受到人们的关注。估计的CT图像可以用于诊断和放射治疗工作流程中的衰减校正、患者定位和剂量规划。本研究旨在引入一种新型统计学习方法来改善MR图像的CT估计，并将我们的方法的性能与现有的基于模型的CT图像估计方法进行比较。方法本文提出的统计学习方法包括两个阶段。在训练阶段，从CT图像的组织类型的先验知识与高斯混合模型（GMM）一起使用，以探索从MR图像的CT图像估计。由于先验知识在预测阶段不可用，基于RUSBoost算法的分类器被训练用于从MR图像中估计组织类型。对于一个新的病人，训练的分类器和GARCH被用来预测CT图像从MR图像。分别采用体素级十重交叉验证和患者级留一法交叉验证对分类器和Gestival进行验证。结果与现有的基于模型的方法相比，该方法具有更好的CT估计质量，特别是在骨组织上。我们的方法提高了5%和23%的全脑和骨组织的CT图像估计，分别。结论我们的方法的评价表明，这是一个有前途的方法来产生CT图像替代实施完全基于MR的放射治疗和PET/MRI应用。
Purpose There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images can be utilized for attenuation correction, patient positioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introduce a novel statistical learning approach for improving CT estimation from MR images and to compare the performance of our method with the existing model-based CT image estimation methods. Methods The statistical learning approach proposed here consists of two stages. At the training stage, prior knowledge about tissue types from CT images was used together with a Gaussian mixture model (GMM) to explore CT image estimations from MR images. Since the prior knowledge is not available at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimate the tissue types from MR images. For a new patient, the trained classifier and GMMs were used to predict CT image from MR images. The classifier and GMMs were validated by using voxel-level tenfold cross-validation and patient-level leave-one-out cross-validation, respectively. Results The proposed approach has outperformance in CT estimation quality in comparison with the existing model-based methods, especially on bone tissues. Our method improved CT image estimation by 5% and 23% on the whole brain and bone tissues, respectively. Conclusions Evaluation of our method shows that it is a promising method to generate CT image substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications.