Cone-Beam CT Image Quality Enhancement Using A Latent Diffusion Model Trained with Simulated CBCT Artifacts

Abstract

Cone-beam computed tomography (CBCT) images are problematic in clinical medicine because of their low contrast and high artifact content compared with conventional CT images. Although there are some studies to improve image quality, in regions subject to organ deformation, the anatomical structure may change after such image quality improvement. In this study, we propose an overcorrection-free CBCT image quality enhancement method based on a conditional latent diffusion model using pseudo-CBCT images. Pseudo-CBCT images are created from CT images using a simple method that simulates CBCT artifacts and are spatially consistent with the CT images. By performing self-supervised learning with these spatially consistent paired images, we can improve image quality while maintaining anatomical structures. Furthermore, extending the framework of the conditional diffusion model to latent space improves the efficiency of image processing. Our model was trained on pelvic CT-pseudo-CBCT paired data and was applied to both pseudo-CBCT and real CBCT data. The experimental results using data of 75 cases show that with our proposed method, the structural changes were less than 1/1000th (in terms of the number of pixels) of those of a conventional method involving learning with real images, and the correlation coefficient between the CT value distributions of the generated and reference images was 0.916, approaching the same level as conventional methods. We also confirmed that the proposed framework achieves faster processing and superior improvement performance compared with the framework of a conditional diffusion model, even under constrained training settings.

Figures (10)

• Figure 1: Difference between CT and CBCT images. CBCT images are classified into two main types according to image characteristics: Type 1 is characterized by decreased CT values, while Type 2 is marked by poor contrast. (a) CT image, (b) CBCT image, (c) artifact location on CBCT image. The arrows indicate radial artifacts, and CT values are reduced around the blue dashed line and in the area of the red line.
• Figure 2: Procedure for creating a pseudo-CBCT image. Background colors indicate the assumed factors of artifacts modeled. The processes shown in red boxes have several parameters that modify the image features. The generated images have the imaging features found in the real CBCT images, while their anatomical structures are consistent with those of the original CT images.
• Figure 3: Three examples of real and pseudo-CBCT images. The arrows indicate image characteristics common to both images.
• Figure 4: Structure and data flow of the proposed method. Pretrained VQVAE is used for the encoder $\mathcal{E}$ and the decoder $\mathcal{D}$.
• Figure 5: Enlarged coronal views of volumes generated with different initial noise settings. (a) Different initial noise for each slice, (b) The same initial noise for all slices. Using different initial noise introduces inter-slice discontinuities.