从真实CT图像生成合成FLAIR MRI图像,用于人体膝关节图像滑液的精确分割

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Isam Abu-Qasmieh, I. Masad, Hiam Alquran, Khaled Z. Alawneh
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引用次数: 1

摘要

利用预先定义的CT均值和标准差与相应质子密度PD、T1和T2的映射函数,从真实的CT图像中生成了合成的腹部三维多模态幻影和人体膝关节的MRI FLAIR图像。这些质子密度PD、T1和T2是先前从自旋回波序列中生成的。首先,测试了从不同序列生成合成MR图像的有效性,并将真实CT图像生成的相同PD、T1和T2图用于MRI逆恢复(IR)序列的模拟。在不同的反演次数下,真实红外序列图像与合成红外序列图像的相似度结果显示出很好的一致性。在确认了利用假体从原自旋回波序列获得的PD、T1和t2图生成合成红外图像的可行性之后,利用反演-恢复序列的稳态横向磁化公式,从相应的膝关节CT真实图像生成膝关节图像的仿真。模拟FLAIR IR序列MR图像使用适当的TI来消除来自滑液的信号,其中图像补体用作掩膜,用于分割真实CT图像中的相应组织区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generation of synthetic FLAIR MRI image from real CT image for accurate synovial fluid segmentation in human knee image
Synthetic MRI FLAIR images of an abdominal 3D multimodality phantom and in vivo human knee have been generated from real CT images using predefined mapping functions of CT mean and standard deviation with the corresponding proton density PD, T1 and T2 that were previously generated from spin-echo sequence. First, the validity of generating synthetic MR images from different sequences were tested and the same PD, T1 and T2 maps that were generated from the real CT image have been used in the simulation of MRI inversion-recovery (IR) sequence. The similarity results between the real and synthetic IR sequence images, using different inversion times TI, showed a very good agreement. After confirming the feasibility of generating synthetic IR images from the PD, T1 and T2-maps, that were originally obtained from spin-echo sequence using the phantom, the simulation of a knee image has been generated from the corresponding knee CT real image using the steady-state transverse magnetization formula of the inversion-recovery sequence. The simulated FLAIR IR sequence MR image are generated using proper TI for nulling the signal from the synovial fluid, where the image complement is used as a mask for segmenting the corresponding tissue region in the real CT image.
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来源期刊
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
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