ArticleIEEE transactions on bio-medical engineering2023
Ultrasound Volume Reconstruction From Freehand Scans Without Tracking.
Article in IEEE transactions on bio-medical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- 3D Freehand Ultrasound Reconstruction of the Carotid Artery.Ultrasound in medicine & biology · 2026Article
- Geometry aware neural radiance fields for freehand ultrasound reconstruction.Biomedical physics & engineering express · 2026Article
- Opportunities for Interventional Radiology in Transbronchial Interventions.Cardiovascular and interventional radiology · 2026Review
- Preoperative MRI-Guided Freehand Ultrasound Volume Reconstruction.Chemical & biomedical imaging · 2026Article
- Sensorless End-to-End Freehand 3-D Ultrasound Reconstruction With Physics-Guided Deep Learning.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Article
- Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Transrectal ultrasound is commonly used for guiding prostate cancer biopsy, where 3D ultrasound volume reconstruction is often desired. Current methods for 3D reconstruction from freehand ultrasound scans require external tracking devices to provide spatial information of an ultrasound transducer. This paper presents a novel deep learning approach for sensorless ultrasound volume reconstruction, which efficiently exploits content correspondence between ultrasound frames to reconstruct 3D volumes without external tracking. The underlying deep learning model, deep contextual-contrastive network (DC
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Registered trials
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