Cited by 6Medical Image Segmentation TechniquesRadiation Dose and ImagingAdvanced Radiotherapy Techniques

Robust patella motion tracking using intensity-based 2D-3D registration on dynamic bi-plane fluoroscopy: towards quantitative assessment in MPFL reconstruction surgery

Otake, Yoshito, Esnault, Matthieu, Grupp, Robert, Kosugi, Shinichi, Sato, Yoshinobu·2016·SPIE Proceedings

Abstract

The determination of in vivo motion of multiple-bones using dynamic fluoroscopic images and computed tomography (CT) is useful for post-operative assessment of orthopaedic surgeries such as medial patellofemoral ligament reconstruction. We propose a robust method to measure the 3D motion of multiple rigid objects with high accuracy using a series of bi-plane fluoroscopic images and a multi-resolution, intensity-based, 2D-3D registration. A Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimizer was used with a gradient correlation similarity metric. Four approaches to register three rigid objects (femur, tibia-fibula and patella) were implemented: 1) an individual bone approach registering one bone at a time, each with optimization of a six degrees of freedom (6DOF) parameter, 2) a sequential approach registering one bone at a time but using the previous bone results as the background in DRR generation, 3) a simultaneous approach registering all the bones together (18DOF) and 4) a combination of the sequential and the simultaneous approaches. These approaches were compared in experiments using simulated images generated from the CT of a healthy volunteer and measured fluoroscopic images. Over the 120 simulated frames of motion, the simultaneous approach showed improved registration accuracy compared to the individual approach: with less than 0.68mm root-mean-square error (RMSE) for translation and less than 1.12° RMSE for rotation. A robustness evaluation was condu…

Integrity signals

Registry

Crossref · 10.1117/12.2214699

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • Traditional intensity-based registration methods are routinely employed to solve compound, multiple object registration problems across broad applications.

    “Despite the small number of learning-based multiple object registration strategies, traditional intensity-based registration methods are now routinely employed to solve compound, multiple object, registration problems across broad applications, such as: kinematic measurements of bone (Chen et al., 2012; Otake et al., 2016; Abe et al., 2019), rib motion and respiratory analysis (Hiasa et al., 2019)”

Cited by

Sorted by citations

Authors
Otake, Y., Esnault, M., Grupp, R., Kosugi, S., & Sato, Y.
Affiliations
  • Nara Institute of Science and Technology (JP)
  • Johns Hopkins University (US)
Venue
SPIE Proceedings
Publisher
SPIE
Published
2016-03-18
Volume
9786
Pages
97860B
ISSN
0277-786X (ISSN-L)
OpenAlex
W2300532603
Cited by
6
References
0
Citations by year
2023: 1 · 2021: 1 · 2019: 2 · 2018: 1 · 2016: 1
FWCI
0.46
Type
conference paper
Language
en
Open access
closed
Locations
Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
  • Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
Keywords
2D-3D registration, intensity-based registration, medial patellofemoral ligament reconstruction, CMA-ES, CT fluoroscopy

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