ArticleNature biomedical engineering2019
Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
Article in Nature biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
109 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Stakeholder experience with artificial intelligence in healthcare: a bibliometric study of satisfaction, trust, acceptance, and patient engagement.Frontiers in digital health · 2026Pooled it
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Dual-view scout scans with deep learning for ultra-low dose attenuation correction in PET.Medical physics · 2026Article
- Latent accelerated diffusion-based deformation estimation for real-time volumetric imaging.Physics in medicine and biology · 2026Article
- Feasibility of predicting free-breathing body contours from biplanar CT scout images for surface-guided DIBH radiotherapy.Physics and imaging in radiation oncology · 2026Article
- 'See' through the surface: surface-derived three-dimensional AI-driven real-time imaging solution for intra-treatment image guidance.Machine Learning. Health · 2026Article
- Real-time reconstruction of 3D bone models via very-low-dose protocols.NPJ digital medicine · 2026Article
- Deep Few-View High-Resolution Photon-Counting CT at Halved Dose for Extremity Imaging.IEEE transactions on medical imaging · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- A Physics-Informed Neural Network forResearch square · 2026Article
- Artificial Intelligence in Label-Free Optical Imaging Applications.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Real-time 4D MRI reconstruction using DVR-NeMF, a framework for dynamic volumetric reconstruction.Cell reports methods · 2025Article
- Seeing the invisible: In vivo dosimetry in radiotherapy with radiacoustic IMAGING.Medical physics · 2025Review
- Article
- Anatomically informed deep learning framework for generating fast, low-dose synthetic CBCT for prostate radiotherapy.Scientific reports · 2025Article
- Real-time volumetric CBCT reconstruction using surface and X-ray imaging for image-guided radiotherapy.Medical image analysis · 2025Article
- Risk-minimizing tube current and tube voltage modulation for CT: A simulation study.Medical physics · 2025Article
- Real-Time Dose-Guided Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Review
- CBCT Reconstruction Using Single X-Ray Projection With Cycle-Domain Geometry-Integrated Denoising Diffusion Probabilistic Models.IEEE transactions on medical imaging · 2025Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a deep-learning model trained to map projection radiographs of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view. We demonstrate the feasibility of the approach with upper-abdomen, lung, and head-and-neck computed tomography scans from three patients. Volumetric reconstruction via deep learning could be useful in image-guided interventional procedures such as radiation therapy and needle biopsy, and might help simplify the hardware of tomographic imaging systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.