Work

Evaluation of fluence reduction versus sparsity for diffusion posterior sampling reconstruction in low-dose CT

Year: 2026

Type: conference-paper

Abstract: Low-dose computed tomography (CT) remains a popular research topic with the advent of an increasing number of algorithmic solutions to control noise. One such approach that enforces data consistency through a model-based data likelihood term but that also includes a deep learning generative prior is Diffusion Posterior Sampling (DPS). This technique is formulated within a probabilistic framework and is capable of generating high-quality reconstructions under noisy and/or under-sampled conditions. However, one major unanswered question is, given the opportunity to design a low-dose protocol, how should low dose be achieved - through sparse sampling or reduced fluence per projection. In this work, we conducted a simulation study and systematically investigated the impact of acquisition parameters - the number of views (nview) and incident photons per view (I0) - on DPS-based CT reconstruction. We performed a 2D sweep over different combinations of the number of views (nview) and incident photons per view (I0) and compared reconstructions with an equivalent total incident photons (TIP). Reconstruction quality was evaluated in terms of PSNR (Peak Signal-to-Noise Ratio), bias, and posterior sample variability. We found that the number of views had a strong influence on image quality and that most performance curves showed a transition where too few views had a large negative impact on performance. We observed that there is an advantage to be gained by jointly optimizing both the fluence per view and the number of views, with a trend of an increasing number of views required for a higher total incident fluence. These findings provide a strategy for optimizing CT acquisition protocols that adapt both fluence per view and sparsity to optimally maintain image quality at reduced radiation doses.

Source: SPIE Medical Imaging: Physics of Medical Imaging

DOI: 10.1117/12.3087836

Authors: Zimo Liu, Xin Wang, Xiao Jiang, Altea Lorenzon, Grace J. Gang, J. Webster Stayman

Institutions: Johns Hopkins University, University of Pennsylvania

Language: English

Topic: Radiation Dose and Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute