  Cone Beam CT (CBCT) plays a crucial role in Adaptive Radiation Therapy (ART)
by accurately providing radiation treatment when organ anatomy changes occur.
However, CBCT images suffer from scatter noise and artifacts, making relying
solely on CBCT for precise dose calculation and accurate tissue localization
challenging. Therefore, there is a need to improve CBCT image quality and
Hounsfield Unit (HU) accuracy while preserving anatomical structures. To
enhance the role and application value of CBCT in ART, we propose an
energy-guided diffusion model (EGDiff) and conduct experiments on a chest tumor
dataset to generate synthetic CT (sCT) from CBCT. The experimental results
demonstrate impressive performance with an average absolute error of
26.87$\pm$6.14 HU, a structural similarity index measurement of 0.850$\pm$0.03,
a peak signal-to-noise ratio of the sCT of 19.83$\pm$1.39 dB, and a normalized
cross-correlation of the sCT of 0.874$\pm$0.04. These results indicate that our
method outperforms state-of-the-art unsupervised synthesis methods in accuracy
and visual quality, producing superior sCT images.
