A Review of 3d/2d Registration Methods for Image-Guided Interventions
Open accessAbstract
Registration of pre- and intra-interventional data is one of the key technologies for image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy, and interventional radiology. In this paper, we survey those 3D/2D data registration methods that utilize 3D computer tomography or magnetic resonance images as the pre-interventional data and 2D X-ray projection images as the intra-interventional data. The 3D/2D registration methods are reviewed with respect to image modality, image dimensionality, registration basis, geometric transformation, user interaction, optimization procedure, subject, and object of registration.
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The capture range magnitude depends on the similarity function and the optimizer, and can be stated quantitatively as the mean target registration error between 3D keypoints at initialization.
“Its magnitude depends, among other things, on the similarity function as well as the optimizer, and it can be stated quantitatively as the mean target registration error (mTRE) between 3D keypoints at initialization1.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Markelj et al., 2012
A central component to many if not most image-guided navigation solutions is image-based 2D/3D registration.
“A central component to many if not most image-guided navigation solutions is image-based 2D/3D registration, which estimates the spatial relationship between a 3D model of the scene (potentially including anatomy and instrumentation) and 2D interventional images thereof (Markelj et al., 2012; Liao et al., 2013).”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Markelj et al., 2012
Analytical solutions now exist to precisely recover 2D/3D spatial relations under certain conditions.
“While, indeed, analytical solutions now exist to precisely recover 2D/3D spatial relations under certain conditions (Markelj et al., 2012; Uneri et al., 2013; Gao et al., 2020b; Grupp et al., 2020b), several hard challenges prevail.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Markelj et al., 2012
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“The magnitude by which the initial parameter guesses may be incorrect for the downstream algorithm to still produce a successful registration is referred to as the capture range [15, 28].”
BackgroundInfluentialReal-time 2D/3D registration via CNN regression
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“…registration has been extensively investigated in various technical aspects including image modalities, methods for achieving dimensional correspondence, similarity metrics, and registration basis (e.g., extrinsic, feature-based, intensity-based and gradient-based methods) (Markelj et al 2012).”
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- Authors
- Markelj, P., Tomaževič, D., Likar, B., & Pernuš, F.
- Affiliations
- University of Ljubljana (SI)
- Corresponding author
- Markelj, P.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 16
- Issue
- 3
- Pages
- 642-661
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W1972344168
- Cited by
- 746
- References
- 2
- Citations by year
- 2026: 19 · 2025: 31 · 2024: 46 · 2023: 47 · 2022: 42 · 2021: 52 · 2020: 48 · 2019: 46 · 2018: 64 · 2017: 65 · 2016: 55 · 2015: 61 · 2014: 58 · 2013: 55 · 2012: 35
- FWCI
- 41.19
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- Repository of the University of Ljubljana (University of Ljubljana) (publishedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Keywords
- 2D-3D registration, image-guided interventions, X-ray projection images, CT images, MRI images, image-guided radiotherapy, radiosurgery, minimally invasive surgery, endoscopy, interventional radiology, geometric transformations
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Imaging, Three-Dimensional, Surgery, Computer-Assisted
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