Composite MRI Task Construction from CT Images based on Deep Convolution Neural Network

Min Liang; Gu Yi; Xue Rui; Ren Yi; Gao Bo

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Composite MRI Task Construction from CT Images based on Deep Convolution Neural Network

机译：基于深卷积神经网络的CT图像复合MRI任务构造

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In traditional CBCT guided radiotherapy, the conventional process is to scan a planned CT image of the patient before treatment, and use the CT image to prepare a treatment plan for the patient, and calculate the radiation dose with the electronic density information of the CT image to obtain the radiation dose that the patient needs to receive. Because CT images cannot be directly used to calculate the amount of data, in order to solve the problem of CT image attenuation corresponding to MRI image synthesis, the deep convolution network model is used to map the CT image to the MRI image, input the CT image, and synthesize the corresponding MRI image with the convolution network model in this article. The synthetic MRI image can be used for the same mode registration with the patient's positioning MRI image, so as to solve the problem of inaccurate cross-membrane registration. The multi-mode synthesis and transformation of CT/MRI images have been realized. Experiments have proved that the method presented in this article is beneficial to reducing the radiation dose of patients, enabling patients to receive more accurate radiotherapy, so that the tumor part can be irradiated as much as possible and the normal tissues around the tumor can be irradiated less, so as to improve the therapeutic effect of tumor patients. (C) 2021 Society for Imaging Science and Technology.

机译：在传统的CBCT引导放射疗法中，常规方法是在治疗前扫描患者的计划CT图像，并使用CT图像为患者制备治疗计划，并计算CT图像的电子密度信息的辐射剂量为了获得患者需要接收的辐射剂量。因为CT图像不能直接用于计算数据量，所以为了解决与MRI图像合成对应的CT图像衰减的问题，深卷积网络模型用于将CT图像映射到MRI图像，输入CT图像，并在本文中与卷积网络模型合成相应的MRI图像。合成MRI图像可用于与患者定位MRI图像相同的模式配准，以解决跨膜登记不准确的问题。已经实现了CT / MRI图像的多模合成和转换。实验证明了本文中提出的方法有利于减少患者的辐射剂量，使患者能够接受更准确的放疗，从而可以尽可能地照射肿瘤部分，并且可以照射肿瘤周围的正常组织。可以照射肿瘤周围的正常组织少，以改善肿瘤患者的治疗效果。（c）2021年成像科技协会。

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《Journal of Imaging Science and Technology》 |2021年第3期|030404.1-030404.10|共10页
作者
Min Liang; Gu Yi; Xue Rui; Ren Yi; Gao Bo;
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Xian Jiaotong Univ City Coll Dept Comp Sci Xian 710018 Peoples R China;

Agr Bank China Shaanxi Branch Technol & Prod Management Dept Xian 710065 Peoples R China;

Xian Cent Hosp Xian 710003 Peoples R China;

Shaanxi Xueqian Normal Univ Network & Informat Ctr Xian 710100 Peoples R China;

DHC Software Co Ltd Xian 710000 Peoples R China;

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Composite MRI Task Construction from CT Images based on Deep Convolution Neural Network

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