A Framework for Guiding DDPM-Based Reconstruction of Damaged CT Projections Using Traditional Methods.
Denoising Diffusion Probabilistic Models (DDPM) have emerged as a promising generative framework for sample synthesis, yet their limitations in detail preservation hinder practical applications in computed tomography (CT) image reconstruction. To address these technical constraints and enhance recon...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 3; pp. 2569 - 2582 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194225570&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194225570 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2026 vid: 39 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194225570 189894386 194225570 194225570 10.1007/s10278-025-01697-y 194225570 ppf: 2569 ppct: 13 formats: tig: atl: A Framework for Guiding DDPM-Based Reconstruction of Damaged CT Projections Using Traditional Methods. aug: au: Zhang, Ziheng Yang, Yishan Yang, Minghan Guo, Hu Yang, Jiazhao Shen, Xianyue Wang, Jianye affil: https://ror.org/034t30j35 Hefei Institutes of Physical Science, Chinese Academy of Sciences, 230031, Hefei, Anhui, China sug: subj: Tomography, X-Ray Computed Methods Image Processing, Computer Assisted Methods Conceptual Framework Deep Learning Models, Statistical Data Quality Human Funding Source Experimental Studies Pilot Studies Descriptive Statistics China Academic Medical Centers Cancer Care Facilities Qualitative Studies Comparative Studies Machine Learning Algorithms Image Interpretation, Computer Assisted Data Analysis Software Artifacts Sensitivity and Specificity Diagnosis, Computer Assisted Tomography, X-Ray Computed Standards ab: Denoising Diffusion Probabilistic Models (DDPM) have emerged as a promising generative framework for sample synthesis, yet their limitations in detail preservation hinder practical applications in computed tomography (CT) image reconstruction. To address these technical constraints and enhance reconstruction quality from compromised CT projection data, this study proposes the Projection Hybrid Inverse Reconstruction Framework (PHIRF) — a novel paradigm integrating conventional reconstruction methodologies with DDPM architecture. The framework implements a dual-phase approach: Initially, conventional CT reconstruction algorithms (e.g., Filtered back projection(FBP), Algebraic Reconstruction Technique(ART), Maximum-Likelihood Expectation Maximization (ML-EM)) are employed to generate preliminary reconstructions from incomplete projections, establishing low-dimensional feature representations. These features are subsequently parameterized and embedded as conditional constraints in the reverse diffusion process of DDPM, thereby guiding the generative model to synthesize enhanced tomographic images with improved structural fidelity. Comprehensive evaluations were conducted on three representative ill-posed projection scenarios: limited-angle projections, sparse-view acquisitions, and low-dose measurements. Experimental results demonstrate that PHIRF achieves state-of-the-art performance across all compromised data conditions, particularly in preserving fine anatomical details and suppressing reconstruction artifacts. Quantitative metrics and visual assessments confirm the framework's consistent superiority over existing deep learning-based reconstruction approaches, substantiating its adaptability to diverse projection degradation patterns. This hybrid architecture establishes a new paradigm for combining physical prior knowledge with data-driven generative models in medical image reconstruction tasks. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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