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

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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.
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Affiliations: 1: Department of Computer Science, Xi’an Jiaotong University City College, Xi’an 710018, China 2: Technology and Product Management Department, Agricultural Bank of China Shaanxi Branch, Xi’an 710065, China 3: Xi’an Central Hospital, Xi’an 710003, China 4: Network and Information Center of Shaanxi Xueqian Normal University, Xi’an 710100, China 5: DHC Software Co., Ltd., Xi’an 710000, China

Appeared or available online: January 29, 2021

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