AST-n: A Fast Sampling Approach for Low-Dose CT Reconstruction using Diffusion Models
This work addresses the computational bottleneck for clinical adoption of diffusion models in medical imaging, though it is incremental as it builds on existing diffusion methods with acceleration techniques.
The paper tackles the problem of slow inference time in diffusion-based models for low-dose CT reconstruction by introducing AST-n, an accelerated sampling framework that reduces sampling steps from ~16 seconds to under 1 second per slice while maintaining image quality with PSNR above 38 dB and SSIM above 0.95.
Low-dose CT (LDCT) protocols reduce radiation exposure but increase image noise, compromising diagnostic confidence. Diffusion-based generative models have shown promise for LDCT denoising by learning image priors and performing iterative refinement. In this work, we introduce AST-n, an accelerated inference framework that initiates reverse diffusion from intermediate noise levels, and integrate high-order ODE solvers within conditioned models to further reduce sampling steps. We evaluate two acceleration paradigms--AST-n sampling and standard scheduling with high-order solvers -- on the Low Dose CT Grand Challenge dataset, covering head, abdominal, and chest scans at 10-25 % of standard dose. Conditioned models using only 25 steps (AST-25) achieve peak signal-to-noise ratio (PSNR) above 38 dB and structural similarity index (SSIM) above 0.95, closely matching standard baselines while cutting inference time from ~16 seg to under 1 seg per slice. Unconditional sampling suffers substantial quality loss, underscoring the necessity of conditioning. We also assess DDIM inversion, which yields marginal PSNR gains at the cost of doubling inference time, limiting its clinical practicality. Our results demonstrate that AST-n with high-order samplers enables rapid LDCT reconstruction without significant loss of image fidelity, advancing the feasibility of diffusion-based methods in clinical workflows.