GenDiff is proposed, a generalizable diffusion-based framework for LDCT reconstruction that jointly models continuous radiation dose and anatomical information within a unified reconstruction network and achieves superior reconstruction quality while maintaining strong robustness across different dose levels, anatomical regions, and acquisition domains, making it a promising solution for practical low-dose CT imaging.
Abstract
Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality. Existing deep learning-based low-dose CT (LDCT) reconstruction methods are typically optimized for fixed dose levels or specific anatomical regions, limiting their robustness and generalization in realistic clinical settings. We propose GenDiff, a generalizable diffusion-based framework for LDCT reconstruction that jointly models continuous radiation dose and anatomical information within a unified reconstruction network. The proposed framework integrates a Dose-Anatomy Encoder to learn acquisition-aware embeddings, a dose- and anatomy-conditioned cold diffusion backbone for iterative refinement, a physics-consistency update to enforce fidelity to the CT forward model, and a Structural Prior Refinement Module (SPRM) that preserves anatomical structures while suppressing dose-dependent artifacts. Extensive experiments on multi-anatomy clinical datasets, including unseen ultra-low-dose conditions as well as out-of-distribution phantom and animal datasets, demonstrate that GenDiff consistently outperforms state-of-the-art convolutional neural network and diffusion-based reconstruction methods. The proposed approach achieves superior reconstruction quality while maintaining strong robustness across different dose levels, anatomical regions, and acquisition domains, making it a promising solution for practical low-dose CT imaging.
Experimental results across several CT inverse problem settings show that Flow Matching-based approaches consistently outperform diffusion-based methods in terms of PSNR, SSIM, and perceptual quality, while requiring fewer sampling steps.
Multi-phase contrast-enhanced CT (CECT) is widely employed to capture the dynamic enhancement patterns and temporal evolution of organs and lesions. However, acquiring multiple phases increases radiation exposure and is inevitably accompanied by inter-phase misalignment and inconsistencies due to patient motion and the temporal variations in contrast uptake. Further dose reduction exacerbates noise and streak artifacts, severely degrading image quality and diagnostic reliability. In this work, we propose a novel reconstruction framework for multi-phase low-dose CECT that is guided by a routinely acquired non-contrast CT scan under weakly paired conditions. Specifically, the reconstruction model was formulated that explicitly separates common anatomical structures from phase-specific contrast variations and noise by deep dictionary representations. Then we employ a proximal gradient optimization method, analytically deriving its iterative procedure and unfolding it into an end-to-end trainable architecture, which preserves the theoretical interpretability of the model and facilitates efficient inference. To enhance structural alignment, we integrate local optimal transport to establish anatomically meaningful correspondences across phases, thereby enforcing structural fidelity and radiodensity consistency. Extensive experiments on real clinical multi-phase datasets demonstrate that our method effectively suppresses noise and streak artifacts while recovering fine contrast-enhanced details. Both quantitative evaluation and expert clinical assessment confirm its superior performance compared with existing approaches. Moreover, downstream evaluation using the TotalSegmentator liver-lesion model shows substantial gains in hepatic tumor detectability under reduced-dose settings, enabling reliable lesion identification while significantly lowering radiation exposure. The codes and models are available at https://github.com/lixing0810/LOT-NCIRecon
Xing Li, Miao-Miao Wang, Bao-Ping Zhang et al.· IEEE Transactions on Image P...· 0 citations
Objective. Low-dose computed tomography (LDCT) reduces radiation dose but, introduces heterogeneous noise due to different photon attenuation based on anatomical tissue. Most deep learning techniques assume uniform noise in LDCT and perform equal noise removal across different regions, leading to sub-optimal performance across different tissues. This work aims to design a physics-based framework that explicitly models region-dependent noise characteristics to improve LDCT noise removal. Approach. We propose an anatomically adaptive noise reduction framework. The proposed anatomically adaptive feature-wise linear modulation (FiLM) model consists of a U-Net architecture that integrates two complementary units: the global FiLM unit in the encoder, which modifies features globally based on global image statistics to remove overall noise, and the local FiLM unit in the decoder, which modifies features locally based on the type of anatomical tissue. This dual design enables the modeling of overall image noise characteristics in addition to the removal of local noise associated with each anatomical tissue. Main results. The model performance was evaluated using AAPM–Mayo Clinic LDCT dataset, and the trained model tested on the TCIA dataset. The proposed model outperformed all competing methods. Local noise analysis showed that noise removal was consistent across different anatomical regions, achieving 37.14% in the lung, 48.75% in soft tissue, and 35.18% in bone. Visual results also confirmed significant improvements in noise removal, preservation of structural details, and reduction of non-residual distortions. Furthermore, the model demonstrated its ability to generalize under domain shift. Significance. This work presents a framework for anatomically adapted noise removal by linking feature modification with the physical properties of noise in LDCT through a dual-modulation process for both general and tissue-related noise. The model achieves a balance between noise removal and preservation of anatomical detail, making it a robust approach to LDCT noise removal.
Safa Alfattama, Ankita Vaish· Physics in Medicine and Biol...· 0 citations
D 3 R-Net establishes a robust and interpretable dual-domain reconstruction framework for ULDCT imaging and consistently out-performs competing methods in terms of quantitative metrics and visual image quality across all evaluated scenarios.
Jia-Bing Xiang, Yu-Hang Yang, Yan-Xin Wang et al.· Physics in Medicine and Biol...· 0 citations
During standard radiotherapy planning, repeated CT acquisitions are often required for patient registration, verification, and adaptive planning, resulting in increased cumulative X-ray dose. To mitigate this, low-dose cone-beam CT (CBCT) is routinely acquired during treatment delivery. However, CBCT image quality remains insufficient for accurate dose calculation and adaptive radiotherapy planning due to increased scatter, noise, beam hardening, and reconstruction related artifacts. This study develops a supervised deep learning based CBCT to CT synthesis framework using a conditional denoising diffusion probabilistic model (DDPM), where the generation of a CT-based planning for accurate positioning and dose calculation is obtained using generative models with low dose CBCT imaging. Beyond demonstrating CBCT to CT synthesis, the primary objective is to investigate how the representation of CBCT input data, either standard clinical DICOM CBCT images or filtered back-projection (FDK) reconstructions from raw projection data, affects the performance of diffusion based CT synthesis. The overarching aim is to assess whether physics aware CBCT representations better support CT-equivalent image quality while maintaining reduced imaging dose in radiotherapy workflows.
Alzahra Altalib, Chunhui Li, Christopher Hamill Taylor et al.· 1 citation
HiGDiff is proposed, a feed-forward hierarchical Gaussian diffusion framework that decomposes reconstruction both spatially and from structure to detail in three distinct CT benchmark datasets.
Yuezhe Yang, Liang Cheng· 0 citations
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