  Cone-beam computed tomography (CBCT) is widely used in image-guided
radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is
highly desired for improved imaging efficiency, dose reduction, and better
mechanical clearance. LA-CBCT reconstruction, however, suffers from severe
under-sampling artifacts, making it a highly ill-posed inverse problem.
Diffusion models can generate data/images by reversing a data-noising process
through learned data distributions; and can be incorporated as a
denoiser/regularizer in LA-CBCT reconstruction. In this study, we developed a
diffusion model-based framework, prior frequency-guided diffusion model
(PFGDM), for robust and structure-preserving LA-CBCT reconstruction. PFGDM uses
a conditioned diffusion model as a regularizer for LA-CBCT reconstruction, and
the condition is based on high-frequency information extracted from
patient-specific prior CT scans which provides a strong anatomical prior for
LA-CBCT reconstruction. Specifically, we developed two variants of PFGDM
(PFGDM-A and PFGDM-B) with different conditioning schemes. PFGDM-A applies the
high-frequency CT information condition until a pre-optimized iteration step,
and drops it afterwards to enable both similar and differing CT/CBCT anatomies
to be reconstructed. PFGDM-B, on the other hand, continuously applies the prior
CT information condition in every reconstruction step, while with a decaying
mechanism, to gradually phase out the reconstruction guidance from the prior CT
scans. The two variants of PFGDM were tested and compared with current
available LA-CBCT reconstruction solutions, via metrics including PSNR and
SSIM. PFGDM outperformed all traditional and diffusion model-based methods.
PFGDM reconstructs high-quality LA-CBCTs under very-limited gantry angles,
allowing faster and more flexible CBCT scans with dose reductions.
