Literature DB >> 28050972

Registration of MRI to intraoperative radiographs for target localization in spinal interventions.

T De Silva1, A Uneri, M D Ketcha, S Reaungamornrat, J Goerres, M W Jacobson, S Vogt, G Kleinszig, A J Khanna, J-P Wolinsky, J H Siewerdsen.   

Abstract

Decision support to assist in target vertebra localization could provide a useful aid to safe and effective spine surgery. Previous solutions have shown 3D-2D registration of preoperative CT to intraoperative radiographs to reliably annotate vertebral labels for assistance during level localization. We present an algorithm (referred to as MR-LevelCheck) to perform 3D-2D registration based on a preoperative MRI to accommodate the increasingly common clinical scenario in which MRI is used instead of CT for preoperative planning. Straightforward adaptation of gradient/intensity-based methods appropriate to CT-to-radiograph registration is confounded by large mismatch and noncorrespondence in image intensity between MRI and radiographs. The proposed method overcomes such challenges with a simple vertebrae segmentation step using vertebra centroids as seed points (automatically defined within existing workflow). Forwards projections are computed using segmented MRI and registered to radiographs via gradient orientation (GO) similarity and the CMA-ES (covariance-matrix-adaptation evolutionary-strategy) optimizer. The method was tested in an IRB-approved study involving 10 patients undergoing cervical, thoracic, or lumbar spine surgery following preoperative MRI. The method successfully registered each preoperative MRI to intraoperative radiographs and maintained desirable properties of robustness against image content mismatch and large capture range. Robust registration performance was achieved with projection distance error (PDE) (median  ±  IQR)  =  4.3  ±  2.6 mm (median  ±  IQR) and 0% failure rate. Segmentation accuracy for the continuous max-flow method yielded dice coefficient  =  88.1  ±  5.2, accuracy  =  90.6  ±  5.7, RMSE  =  1.8  ±  0.6 mm, and contour affinity ratio (CAR)  =  0.82  ±  0.08. Registration performance was found to be robust for segmentation methods exhibiting RMSE  <3 mm and CAR  >0.50. The MR-LevelCheck method provides a potentially valuable extension to a previously developed decision support tool for spine surgery target localization by extending its utility to preoperative MRI while maintaining characteristics of accuracy and robustness.

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Year:  2017        PMID: 28050972      PMCID: PMC5321067          DOI: 10.1088/1361-6560/62/2/684

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  30 in total

1.  3-D/2-D registration of CT and MR to X-ray images.

Authors:  Dejan Tomazevic; Bostjan Likar; Tomaz Slivnik; Franjo Pernus
Journal:  IEEE Trans Med Imaging       Date:  2003-11       Impact factor: 10.048

2.  Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies.

Authors:  Angel Torrado-Carvajal; Joaquin L Herraiz; Eduardo Alcain; Antonio S Montemayor; Lina Garcia-Cañamaque; Juan A Hernandez-Tamames; Yves Rozenholc; Norberto Malpica
Journal:  J Nucl Med       Date:  2015-10-22       Impact factor: 10.057

3.  Automated 3-dimensional computed tomographic and fluoroscopic image registration.

Authors:  A Hamadeh; S Lavallee; P Cinquin
Journal:  Comput Aided Surg       Date:  1998

4.  Registration of 2D x-ray images to 3D MRI by generating pseudo-CT data.

Authors:  M J van der Bom; J P W Pluim; M J Gounis; E B van de Kraats; S M Sprinkhuizen; J Timmer; R Homan; L W Bartels
Journal:  Phys Med Biol       Date:  2011-01-21       Impact factor: 3.609

5.  Robust MR spine detection using hierarchical learning and local articulated model.

Authors:  Yiqiang Zhan; Dewan Maneesh; Martin Harder; Xiang Sean Zhou
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

6.  MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences.

Authors:  Vincent Keereman; Yves Fierens; Tom Broux; Yves De Deene; Max Lonneux; Stefaan Vandenberghe
Journal:  J Nucl Med       Date:  2010-05       Impact factor: 10.057

7.  Automatic Masking for Robust 3D-2D Image Registration in Image-Guided Spine Surgery.

Authors:  M D Ketcha; T De Silva; A Uneri; G Kleinszig; S Vogt; J-P Wolinsky; J H Siewerdsen
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2016-03-18

8.  Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery.

Authors:  Y Otake; S Schafer; J W Stayman; W Zbijewski; G Kleinszig; R Graumann; A J Khanna; J H Siewerdsen
Journal:  Phys Med Biol       Date:  2012-08-03       Impact factor: 3.609

9.  Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine.

Authors:  Sofia K Michopoulou; Lena Costaridou; Elias Panagiotopoulos; Robert Speller; George Panayiotakis; Andrew Todd-Pokropek
Journal:  IEEE Trans Biomed Eng       Date:  2009-04-14       Impact factor: 4.538

Review 10.  Epidural steroids for spinal pain and radiculopathy: a narrative, evidence-based review.

Authors:  Indy Wilkinson; Steven P Cohen
Journal:  Curr Opin Anaesthesiol       Date:  2013-10       Impact factor: 2.706

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  3 in total

1.  Clinical Translation of the LevelCheck Decision Support Algorithm for Target Localization in Spine Surgery.

Authors:  Amir Manbachi; Tharindu De Silva; Ali Uneri; Matthew Jacobson; Joseph Goerres; Michael Ketcha; Runze Han; Nafi Aygun; David Thompson; Xiaobu Ye; Sebastian Vogt; Gerhard Kleinszig; Camilo Molina; Rajiv Iyer; Tomas Garzon-Muvdi; Michael R Raber; Mari Groves; Jean-Paul Wolinsky; Jeffrey H Siewerdsen
Journal:  Ann Biomed Eng       Date:  2018-07-26       Impact factor: 4.219

2.  SpineCloud: image analytics for predictive modeling of spine surgery outcomes.

Authors:  Tharindu De Silva; S Swaroop Vedula; Alexander Perdomo-Pantoja; Rohan Vijayan; Sophia A Doerr; Ali Uneri; Runze Han; Michael D Ketcha; Richard L Skolasky; Timothy Witham; Nicholas Theodore; Jeffrey H Siewerdsen
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-18

3.  A Fast 3-Dimensional Magnetic Resonance Imaging Reconstruction for Surgical Planning of Uterine Myomectomy.

Authors:  Sa Ra Lee; Young Jae Kim; Kwang Gi Kim
Journal:  J Korean Med Sci       Date:  2018-01-08       Impact factor: 2.153

  3 in total

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