DiffuCE: Expert-Level CBCT Image Enhancement using a Novel Conditional Denoising Diffusion Model with Latent Alignment

Fang-Yi Su, Tzu-Hung Chang, Jung-Hsien Chiang

Abstract:

Cone-Beam Computed Tomography (CBCT) has garnered significant attention due to lower radiation dosage and faster scanning time, which has been widely used in clinical applications for decades. However, its poor image quality is always challenging to clinical experts. To address this problem, we propose our work DiffuCE, a Diffusion model framework for CBCT Enhancement. The main contributions of our work are three-fold: (1) Increased Generalizability: Our training data exclusively comprises pixel
space data, eliminating the necessity for additional imaging machine settings. This emphasizes the model’s ability to generalize effectively across diverse conditions. (2) Efficient Training: Rather than starting from scratch, our approach fine-tunes from a well-established foundation model. This illustrates the viability of efficient training strategies for medical image restoration tasks, optimizing resource
utilization. (3) Competitive Performance: DiffuCE exhibits outstanding performance, excelling in FID and LPIPS with 0.01 and 36.99 ahead of the second place in the private set. In the public dataset, DiffuCE has a competitive performance compared to other SOTAs. Moreover, in expert assessments, DiffuCE achieves the highest score of 7.06 for overall satisfaction, which is 1.38 ahead of the second place, affirming its performance from a clinical stand-point. Codes are available at https://github.com/lzh107u/DiffuCE