CoChat
Sign Up
Cited by 53Medical Image Segmentation TechniquesAdvanced Radiotherapy TechniquesRadiation Dose and Imaging

3D-2D Registration for Surgical Guidance: Effect of Projection View Angles on Registration Accuracy

Open access
Uneri, A, Otake, Y, Wang, A S, Kleinszig, G, Vogt, S, Khanna, A J, Siewerdsen, J H·2014·Physics in Medicine and Biology
DOI 10.1088/0031-9155/59/2/271PMC PMC4927006
Open this paper in CoChat — free account

Abstract

An algorithm for intensity-based 3D-2D registration of CT and x-ray projections is evaluated, specifically using single- or dual-projection views to provide 3D localization. The registration framework employs the gradient information similarity metric and covariance matrix adaptation evolution strategy to solve for the patient pose in six degrees of freedom. Registration performance was evaluated in an anthropomorphic phantom and cadaver, using C-arm projection views acquired at angular separation, Δθ, ranging from ∼0°-180° at variable C-arm magnification. Registration accuracy was assessed in terms of 2D projection distance error and 3D target registration error (TRE) and compared to that of an electromagnetic (EM) tracker. The results indicate that angular separation as small as Δθ ∼10°-20° achieved TRE <2 mm with 95% confidence, comparable or superior to that of the EM tracker. The method allows direct registration of preoperative CT and planning data to intraoperative fluoroscopy, providing 3D localization free from conventional limitations associated with external fiducial markers, stereotactic frames, trackers and manual registration.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1088/0031-9155/59/2/271

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 Uneri et al., 2013

    The Discussion reports precise 3D localization under specified projection-angle conditions (TRE <2 mm at about 10° separation). But Methods §2.1 describes an iterative optimization algorithm, not an analytical solution for recovering the 2D/3D spatial relationship. The analytically exact calculation mentioned in Methods §2.1.2 concerns a projection line integral, not the registration solution. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Registration ambiguity is most pronounced in single-view registration.

    “The aforementioned complexity also leads to registration ambiguity, which is most pronounced in single-view registration (Otake et al., 2013; Uneri 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 Uneri et al., 2013

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 41 · Cited by 53

Research works printed but not linked: 34

  1. Birkfellner W Figl M Kettenbach J Hummel J Homolka P Schernthaner R Nau T Bergmann H 2007 Rigid 2D/3D slice-to-volume registration and its application on fluoroscopic CT images Med Phys 34 246 17278510
  2. Comeau RM Sadikot AF Fenster A Peters TM 2000 Intraoperative ultrasound for guidance and tissue shift correction in image-guided neurosurgery Med Phys 27 787 10798702
  3. Fei B Duerk JL Boll DT Lewin JS Wilson DL 2003 Slice-to-volume registration and its potential application to interventional MRI-guided radio-frequency thermal ablation of prostate cancer IEEE Trans Med Imaging 22 515 25 12774897
  4. Fitzpatrick JM West JB Maurer CR 1998 Predicting error in rigid-body point-based registration IEEE Trans Med Imaging 17 694 702 9874293
  5. Foley JM 1980 Binocular distance perception Psychol Rev 87 411 34 7413886
  6. Frühwald L Kettenbach J Figl M Hummel J Bergmann H Birkfellner W 2009 A comparative study on manual and automatic slice-to-volume registration of CT images Eur Radiol 19 2647 53 19504108 PMC2855949
  7. Galigekere RR Wiesent K Holdsworth DW 2003 Cone-beam reprojection using projection-matrices IEEE Trans Med Imaging 22 1202 14 14552575
  8. Hansen N Niederberger ASP Guzzella L Koumoutsakos P 2009 A method for handling uncertainty in evolutionary optimization with an application to feedback control of combustion IEEE Trans Evol Comput 13 180 97
  9. Hanson GR Suggs JF Freiberg AA Durbhakula S Li G 2006 Investigation of in vivo 6DOF total knee arthoplasty kinematics using a dual orthogonal fluoroscopic system J Orthop Res 24 974 81 16596640
  10. Herman A Dekel A Botser IB Steinberg EL 2009 Computer-assisted surgery for dynamic hip screw, using surgix, a novel intraoperative guiding system Int J Med Robot 5 45 50 19115246
  11. Hummel J Figl M Bax M Bergmann H Birkfellner W 2008 2D/3D registration of endoscopic ultrasound to CT volume data Phys Med Biol 53 4303 16 18653922
  12. Jain A Fichtinger G 2006 C-arm tracking and reconstruction without an external tracker Med Image Comput Comput Assist Interv 4190 494 502 17354927
  13. Jomier J Bullitt E Van Horn M Pathak C Aylward SR 2006 3D/2D model-to-image registration applied to TIPS surgery Med Image Comput Comput Assist Interv 4191 662 9 17354829 PMC2430607
  14. Lemieux L 1994 A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs Med Phys 21 1749 7891637
  15. Leventon M Wells WM III Grimson WEL 1997 Multiple view 2D-3D mutual information registration Image Underst Workshop 1 625 9
  16. Markelj P Tomaževič D Likar B Pernuš F 2012 A review of 3D/2D registration methods for image-guided interventions Med Image Anal 16 642 61 20452269
  17. Maurer CR Fitzpatrick JM Wang MY Galloway RL Maciunas RJ Allen GS 1997 Registration of head volume images using implantable fiducial markers IEEE Trans Med Imaging 16 447 62 9263002
  18. Micu R Jakobs TF Urschler M Navab N 2006 A new registration/visualization paradigm for CT-fluoroscopy guided RF liver ablation Med Image Comput Comput Assist Interv 4190 882 90 17354974
  19. Niethammer M Tannenbaum A Angenent S 2006 Dynamic active contours for visual tracking IEEE Trans Autom Control 51 562 79 PMC3666594 23729836
  20. Otake Y Schafer S Stayman JW Zbijewski W Kleinszig G Graumann R Khanna AJ Siewerdsen JH 2012 Automatic localization of vertebral levels in x-ray fluoroscopy using 3D–2D registration: a tool to reduce wrong-site surgery Phys Med Biol 57 5485 508 22864366 PMC3429949
  21. Penney GP Weese J Little JA Desmedt P Hill DL Hawkes DJ 1998 A comparison of similarity measures for use in 2-D-3-D medical image registration IEEE Trans Med Imaging 17 586 95 9845314
  22. Pluim JP Maintz JB Viergever MA 2000 Image registration by maximization of combined mutual information and gradient information IEEE Trans Med Imaging 19 809 14 11055805
  23. Russakoff DB Rohlfing T Adler JR Maurer CR 2005 Intensity-based 2D–3D spine image registration incorporating a single fiducial marker Acad Radiol 12 37 50 15691724
  24. Sadowsky O Lee J Sutter EG Wall SJ Prince JL Taylor RH 2011 Hybrid cone-beam tomographic reconstruction: incorporation of prior anatomical models to compensate for missing data IEEE Trans Med Imaging 30 69 83 20667807 PMC3415332
  25. Siddon RL 1985 Fast calculation of the exact radiological path for a three-dimensional CT array Med Phys 12 252 5 4000088
  26. Siewerdsen JH Moseley DJ Burch S Bisland SK Bogaards A Wilson BC Jaffray DA 2005 Volume CT with a flat-panel detector on a mobile, isocentric C-arm: pre-clinical investigation in guidance of minimally invasive surgery Med Phys 32 241 54 15719975
  27. Söderman M Babic D Homan R Andersson T 2005 3D roadmap in neuroangiography: technique and clinical interest Neuroradiology 47 735 40 16001241
  28. Turgeon G-A Lehmann G Guiraudon G Drangova M Holdsworth D Peters T 2005 2D–3D registration of coronary angiograms for cardiac procedure planning and guidance Med Phys 32 3737 16475773
  29. Vahala E Ylihautala M Tuominen J Schiffbauer H Katisko J Yrjänä S Vaara T Ehnholm G Koivukangas J 2001 Registration in interventional procedures with optical navigator J Magn Reson Imaging 13 93 8 11169809
  30. Van de Kraats EB Penney GP Tomazevic D van Walsum T Niessen WJ 2005 Standardized evaluation methodology for 2-D–3-D registration IEEE Trans Med Imaging 24 1177 89 16156355
  31. Weese J Penney GP Desmedt P Buzug TM Hill DLG Hawkes DJ 1997 Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery IEEE Trans Inf Technol Biomed 1 284 93 11020832
  32. Wood BJ 2005 Navigation with electromagnetic tracking for interventional radiology procedures: a feasibility study J Vasc Interv Radiol 16 493 505 15802449 PMC2375916
  33. Youkilis AS Quint DJ McGillicuddy JE Papadopoulos SM 2001 Stereotactic navigation for placement of pedicle screws in the thoracic spine Neurosurgery 48 771 8 11322437
  34. Zollei L Grimson E Norbash A Wells W 2001 2D–3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators CVPR’2001: Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition 2 II-696 II-703

Authors
Uneri, A., Otake, Y., Wang, A. S., Kleinszig, G., Vogt, S., Khanna, A. J., & Siewerdsen, J. H.
Affiliations
  • Johns Hopkins University (US)
  • Siemens Healthineers (Germany) (DE)
ORCID
Venue
Physics in Medicine and Biology
Publisher
IOP Publishing
Published
2013-12-19
Volume
59
Issue
2
Pages
271-287
ISSN
  • 0031-9155 (ISSN-L)
  • 1361-6560
DOI
10.1088/0031-9155/59/2/271
PMC
PMC4927006
OpenAlex
W1984497179
Cited by
53
References
41
Citations by year
2026: 2 · 2025: 2 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 8 · 2020: 4 · 2019: 5 · 2018: 1 · 2017: 5 · 2016: 1 · 2015: 6 · 2014: 3
FWCI
3.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Physics in Medicine and Biology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute of Biomedical Imaging and Bioengineering (R01EB017226)
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, surgical guidance, intensity-based registration, C-arm fluoroscopy, target registration error, anthropomorphic phantom, corpses, intraoperative localization
MeSH terms
Tomography, X-Ray Computed, Imaging, Three-Dimensional, Surgery, Computer-Assisted

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review