Low-dose CBCT image reconstruction method based on three-dimensional adversarial generation network
An image reconstruction and adversarial technology, which is applied in the field of medical image processing, can solve the problems of inability to meet the needs of clinical diagnosis and poor quality of reconstructed CT images, so as to improve the efficiency of clinical diagnosis, reduce the amount of X-ray radiation, and shorten the acquisition time.
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[0035] The method of the present invention is divided into a training stage and a testing stage. The steps include: first, using the sinusoidal image of the complete projection data and the sinusoidal image of the incomplete projection data to train the adversarial generation network model, and obtain a three-dimensional adversarial network model that can generate high-quality sinusoidal images. Generate a network model model; secondly, use the trained model to predict the missing part of the sinusoidal image of the incomplete projection data, and obtain the generated sinusoidal image of the complete projection; finally, use the FDK method to reconstruct the CT from the sinusoidal image of the generated complete projection data image. The invention can predict missing projection data and further reconstruct high-quality CT images conforming to clinical diagnosis.
[0036] The present invention will be further described below in conjunction with the accompanying drawings. The ...
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