4D-CBCT imaging method based on motion compensation learning

Abstract

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 subject can be reduced, the quality requirements of clinical analysis and diagnosis can be met, and the tracking efficiency of lung tumors can be improved.

Description

technical field [0001] The present invention relates to the technical field of computed tomography, and more specifically, relates to a 4D-CBCT imaging method based on motion compensation learning. Background technique [0002] With the rapid development of medical technology, many new technologies are applied to the prevention and treatment of tumors, such as radiofrequency ablation, biological cell therapy, gene therapy, etc., but radiotherapy is still one of the three major methods for treating malignant tumors. In recent decades, the mode of clinical implementation of radiotherapy has undergone several major technological innovations. In the late 20th century, with the emergence of technologies such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), radiation therapy plans began to change from two-dimensional to three-dimensional. In the 21st century, intensity-modulated radiation therapy has basically realized a fully automatic computer control mode. Ho...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the problem that the star-stripe artifact and noise cannot be effectively removed in the 4D-CBCT imaging method in the prior art, and provide a 4D-CBCT imaging method based on motion compensation learning, which is called motion compensation learning reconstruction (Motion Compensation Learning Reconstruction, referred to as MCLR), without increasing the cost of the existing CBCT hardware, through the study of key reconstruction techniques, combined with the training of the motion compensation network, to suppress image blurring and stripes caused by breathing motion and angle loss To improve the image quality of 4D-CBCT, so that image-guided radiotherapy can "see more clearly and grasp more accurately", thereby improving the accuracy of tumor radiotherapy and increasing treatment benefits

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