ArticleScientific reports2023
Deep 3D reconstruction of synchrotron X-ray computed tomography for intact lungs.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- In vivo imaging of central nervous system fluid spaces using synchrotron radiation-based micro computed tomography.Nature communications · 2026Article
- Article
- Imaging technologies in experimental pulmonary fibrosis research: essential tool for enhanced translational relevance.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- Patient-specific cerebral 3D vessel model reconstruction using deep learning.Medical & biological engineering & computing · 2024Article
- Automatic 3D cell segmentation of fruit parenchyma tissue from X-ray micro CT images using deep learning.Plant methods · 2024Article
- Spherical alveolar shapes in live mouse lungs.Scientific reports · 2023Article
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synchrotron X-rays can be used to obtain highly detailed images of parts of the lung. However, micro-motion artifacts induced by such as cardiac motion impede quantitative visualization of the alveoli in the lungs. This paper proposes a method that applies a neural network for synchrotron X-ray Computed Tomography (CT) data to reconstruct the high-quality 3D structure of alveoli in intact mouse lungs at expiration, without needing ground-truth data. Our approach reconstructs the spatial sequence of CT images by using a deep-image prior with interpolated input latent variables, and in this way significantly enhances the images of alveolar structure compared with the prior art. The approach successfully visualizes 3D alveolar units of intact mouse lungs at expiration and enables us to measure the diameter of the alveoli. We believe that our approach helps to accurately visualize other living organs hampered by micro-motion.
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Registered trials
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