Research on Low Dose CBCT Image Reconstruction Algorithm Based on Deep Learning

Radiation therapy, along with surgery and internal medicine therapy, is one of the three major methods of tumor treatment. To ensure that the actual treatment process is consistent with the radiotherapy plan and make the radiation accurately strikes the tumor, it is necessary to use the Cone Beam Computed Tomography (CBCT) device onboard the medical linear accelerator to quickly obtain CBCT images of the patient's treatment site before implementing radiotherapy. On the one hand, it is used to correct the patient's posture, and on the other hand, it can be used to observe the changes in the patient's tumor and normal tissue structure around the tumor, so as to timely revise the radiotherapy plan. Compared with CT images, CBCT has the characteristics of lower dose and more convenient access. At present, CBCT only has single level ray imaging, and the commonly used CBCT imaging energy is 80 keV, which can avoid patients receiving additional radiation exposure. However, the organ tissue edges of the 80keVCBCT image are relatively blurry and cannot effectively suppress the generation of metal artifacts, making it limited in observing changes in the patient's target area. The use of 140keV imaging can significantly suppress artifacts in the image, improve image quality, and thus improve radiotherapy accuracy, but at the same time, it increases the burden on patients. Therefore, we propose a deep learning method. By collecting low-dose 80keVCBCT images, we can reconstruct and generate images closer to 140keV energy level, reduce the radiation exposure received by patients, and at the same time provide radiotherapy technicians with higher quality CBCT images, so as to improve the observation effect of tumors and their surrounding normal tissues, and achieve the purpose of improving the accuracy of radiotherapy.