CBCT reconstruction using single X-ray projection with cycle-domain geometry-integrated denoising diffusion probabilistic models Journal Article


Authors: Pan, S.; Peng, J.; Gao, Y.; Lo, S. Y.; Luan, T.; Li, J.; Wang, T.; Chang, C. W.; Tian, Z.; Yang, X.
Article Title: CBCT reconstruction using single X-ray projection with cycle-domain geometry-integrated denoising diffusion probabilistic models
Abstract: In the sphere of Cone Beam Computed Tomography (CBCT), acquiring X-ray projections from sufficient angles is indispensable for traditional image reconstruction methods to accurately reconstruct 3D anatomical intricacies. However, this acquisition procedure for the linear accelerator-mounted CBCT systems in radiotherapy takes approximately one minute, impeding its use for ultra-fast intra-fractional motion monitoring during treatment delivery. To address this challenge, we introduce the Patient-specific Cycle-domain Geometric-integrated Denoising Diffusion Probabilistic Model (CG-DDPM). This model aims to leverage patient-specific priors from patient's CT/4DCT images, which are acquired for treatment planning purposes, to reconstruct 3D CBCT from a single-view 2D CBCT projection of any arbitrary angle during treatment, namely single-view reconstructed CBCT (svCBCT). The CG-DDPM framework encompasses a dual DDPM structure: the Projection-DDPM for synthesizing comprehensive full-view projections and the CBCT-DDPM for creating CBCT images. A key innovation is our Cycle-Domain Geometry-Integrated (CDGI) method, incorporating a Cone Beam X-ray Geometric Transformation Module (GTM) to ensure precise, synergistic operation between the dual DDPMs, thereby enhancing reconstruction accuracy and reducing artifacts. Evaluated in a study involving 37 lung cancer patients, the method demonstrated its ability to reconstruct CBCT not only from simulated X-ray projections but also from real-world data. The CG-DDPM significantly outperforms existing V-shape convolutional neural networks (V-nets), Generative Adversarial Networks (GANs), and DDPM methods in terms of reconstruction fidelity and artifact minimization. This was confirmed through extensive voxel-level, structural, visual, and clinical assessments. The capability of CG-DDPM to generate high-quality reconstructed CBCT from a single-view projection at any arbitrary angle using a single model opens the door for ultra-fast, in-treatment volumetric imaging. This is especially beneficial for radiotherapy at motion-associated cancer sites and image-guided interventional procedures.
Keywords: accuracy; training; imaging; image reconstruction; computed tomography; noise reduction; cbct; biomedical imaging; ct image; convolutional neural-network; image synthesis; x-ray imaging; denoising diffusion probabilistic model; three-dimensional displays; single-view projection reconstruction; dual-domain
Journal Title: IEEE Transactions on Medical Imaging
Volume: 44
Issue: 7
ISSN: 0278-0062
Publisher: IEEE  
Date Published: 2025-07-01
Start Page: 2933
End Page: 2947
Language: English
ACCESSION: WOS:001523480800027
DOI: 10.1109/tmi.2025.3556402
PROVIDER: wos
PMCID: PMC12310287
PUBMED: 40168234
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF -- Source: Wos
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  1. Tonghe Wang
    56 Wang