Jul 2026· Journal of imaging informatics in medicine· 0 citations· 23 references
Medicine
TL;DR
This study compared the performance of various deep learning-based denoising models applied to 2D cone-beam CT projections and highlighted the importance of physical modeling, data consistency, and the training strategy of deep learning-based algorithms for robust, generalizable LDCT denoising.
The experimental results demonstrate that MHSG-Model has a good performance on preserving detail information and removing artifacts, which show the potential to be applied in clinical sparse view spectral CT reconstruction.
Qi-Wei Li, Zai-Feng Shi, Fan-Ning Kong et al.· Journal of X-Ray Science and...· 0 citations
Background: Ultra-low-dose (ULD) chest CT can reduce radiation exposure but may compromise image quality. We evaluated image-domain generative adversarial network (GAN)-based denoising at low-dose (LD) and ULD levels, focusing on ULD-AiR versus LD-ADMIRE S3. Methods: In this single-center retrospective paired study, 26...
Low-Dose Computed Tomography (LDCT) reduces radiation exposure but introduces severe quantum noise and streak artifacts that degrade image quality. To address these challenges, we propose the Physics-Guided Sequential State Space Transformer (PS3T), a projection-domain denoising framework that combines sequential state...
Luella Marcos, Paul S. Babyn, Javad Alirezaie· Signals· 0 citations
The experimental results show that the proposed AI-enabled hybrid restoration framework can better preserve structure edges, recover fine anatomical details and suppress noise compared with the traditional optimization methods and deep learning alone, which shows the effectiveness for low-dose medical image denoising.
M. Kristappa, Krishnanaik Vankdoth· International journal of com...· 0 citations
Objective. 4D cone-beam computed tomography (4DCBCT) is a technique used to address respiratory motion in radiotherapy but is limited by significant view-aliasing artifacts. Recently, deep learning methods have been proposed to reduce view-aliasing. This study investigates the robustness and performance of these method...
Samuele Papa, E. Gavves, J. Sonke· Physics in Medicine and Biol...· 0 citations
This study proposes the Ordered Subset Expectation Maximization–Asymptotic Non-local Means (OSEM-ANLM) algorithm for sparse-view Computed Tomography (CT) reconstruction under limited-data conditions and evaluates its performance across different acquisition settings. The algorithms were evaluated using a digital phan...
Sayna Jamaati, S. A. Hosseini, P. Sharifian et al.· Scientific Reports· 0 citations
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