The invention provides a real-time 4D-
CBCT imaging method based on a Unet-Transform model, and the method comprises the steps: constructing a comprehensive
cascade deep learning network model integrating a Unet network and a Transform network, training the
network model, inputting a plurality of CBCT projections containing different respiratory movement amplitudes, which are collected at the same rack angle at the online treatment moment of a patient, into the trained
network model, the model outputs three PCA feature tags of a DVF between a predicted phase of
breathing motion of a patient at the current moment and a reference phase corresponding to CBCT projection in real time, the PCA feature tags are converted into the DVF through
principal component analysis, a reference
phase image of an earlier-stage 4D-CT of the patient is deformed under the guidance of the DVF, a corresponding 4D-
CBCT image of the
breathing phase at the current moment is obtained, and the
CBCT image of the
breathing motion of the patient at the current moment is obtained. And finally, a 4D-
CBCT image sequence capable of covering a complete
respiratory cycle is obtained. According to the method, dynamic volume imaging is carried out on the patient during SBRT fractional treatment beam exiting by utilizing a Unt-Transform network model framework, a tool for accurately monitoring movement of tumors in the
lung during beam exiting treatment of the patient is provided for clinicians, and technical guarantee is provided for accurate and safe implementation of the
lung cancer SBRT.