ArticleCell reports. Medicine2022
Self-supervised learning enables 3D digital subtraction angiography reconstruction from ultra-sparse 2D projection views: A multicenter study.
Article in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 26 citations in OpenAlex.
- Generative AI-based low-dose digital subtraction angiography for intra-operative radiation dose reduction: a randomized controlled trial.Nature medicine · 2026Trial
- An Efficient Neuro-framework for Brain Tumor Classification Using a CNN-based Self-supervised Learning Approach with Genetic Optimizations.Current neuropharmacology · 2026Article
- Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives.World journal of radiology · 2025Review
- End-to-end deep learning approach for uterine artery-ovarian artery anastomosis detection from digital subtraction angiography sequences.Medical physics · 2025Article
- Clinical utility of artificial intelligence models in radiology: a systemic scoping review of diagnostic and endovascular applications.CVIR endovascular · 2025Review
- Spatiotemporally constrained 3D reconstruction from biplanar digital subtraction angiography.International journal of computer assisted radiology and surgery · 2025Article
- CDKD-w+: A Keyframe Recognition Method for Coronary Digital Subtraction Angiography Video Sequence Based on w+ Space Encoding.Sensors (Basel, Switzerland) · 2025Article
- NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation.Bioengineering (Basel, Switzerland) · 2024Article
- Autophagy-based therapy for hepatocellular carcinoma: from standard treatments to combination therapy, oncolytic virotherapy, and targeted nanomedicines.Clinical and experimental medicine · 2024Review
- Two Projections Suffice for Cerebral Vascular Reconstruction.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives.Diagnostics (Basel, Switzerland) · 2024Review
- Association of the pretreatment lung immune prognostic index with immune checkpoint inhibitor outcomes in patients with advanced hepatocellular carcinoma.European journal of medical research · 2023Article
- ADSeg: A flap-attention-based deep learning approach for aortic dissection segmentation.Patterns (New York, N.Y.) · 2023Article
- Harnessing artificial intelligence in radiology to augment population health.Frontiers in medical technology · 2023Review
- Recent advances in oncolytic virus therapy for hepatocellular carcinoma.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
17 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
3D digital subtraction angiography (DSA) reconstruction from rotational 2D projection X-ray angiography is an important basis for diagnosis and treatment of intracranial aneurysms (IAs). The gold standard requires approximately 133 different projection views for 3D reconstruction. A method to significantly reduce the radiation dosage while ensuring the reconstruction quality is yet to be developed. We propose a self-supervised learning method to realize 3D-DSA reconstruction using ultra-sparse 2D projections. 202 cases (100 from one hospital for training and testing, 102 from two other hospitals for external validation) suspected to be suffering from IAs were conducted to analyze the reconstructed images. Two radiologists scored the reconstructed images from internal and external datasets using eight projections and identified all 82 lesions with high diagnostic confidence. The radiation dosages are approximately 1/16.7 compared with the gold standard method. Our proposed method can help develop a revolutionary 3D-DSA reconstruction method for use in clinic.
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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.