4D-CBCT imaging method based on motion compensation learning

The invention discloses a 4D-CBCT imaging method based on motion compensation, and belongs to the field of computed tomography. The method comprises the following steps: firstly, acquiring high-quality 4D-CBCT data of a patient, and dividing the high-quality 4D-CBCT data into samples and label data; then, requiring a motion compensation learning convolutional neural network of 4D-CBCT data to be constructed, wherein the network is used for establishing mapping between different phase images; secondly, training the network by taking the sample and the label data as inputs to obtain an optimal network parameter weight; and finally, reconstructing 4D-CBCT projection data under clinical scanning with the assistance of the network, and acquiring a high-quality reconstructed image. According tothe method, reconstruction blurring caused by respiratory movement and noise and artifacts caused by data acquisition angle loss can be reduced to a great extent, the scanning period can be shortened.The radiation damage to a