Probabilistic sparse matching for robust 3D/3D fusion in minimally invasive surgery

IEEE Transactions on Medical Imaging

Volumn 34, Issue 1, 2015, Pages 49-60

  Neumann, Dominik ^a,b   Grbic, Sasa ^a,c   John, Matthias ^a   Navab, Nassir ^c   Hornegger, Joachim ^b   Ionasec, Razvan ^a  

^a SIEMENS AG   (Germany)

^b UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Anatomical overlay; computed tomography (CT); model based cardiac image registration; Procedure guidance

Indexed keywords

ARTIFICIAL INTELLIGENCE; IMAGE FUSION; IMAGE REGISTRATION; IMAGING TECHNIQUES; LEARNING SYSTEMS; MEDICAL IMAGING; OPTIMIZATION; ROBOTIC ARMS; SURGERY; TOMOGRAPHY;

ANATOMICAL OVERLAY; IMAGE-TO-IMAGE REGISTRATION; INTRA-OPERATIVE IMAGING; MINIMALLY INVASIVE SURGERY; MODEL-BASED OPC; NUMERICAL OPTIMIZATION TECHNIQUES; PATIENT SPECIFIC MODEL; QUANTITATIVE EXPERIMENTS;

COMPUTERIZED TOMOGRAPHY;

ABDOMINAL RADIOGRAPHY; ARTICLE; BIOLOGICAL MODEL; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC IMAGING; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; INTRAOPERATIVE PERIOD; MACHINE LEARNING; MINIMALLY INVASIVE CARDIAC SURGERY; MULTIMODAL IMAGING; PREOPERATIVE EVALUATION; QUANTITATIVE ANALYSIS; SURGICAL ANATOMY; THORAX RADIOGRAPHY; THREE DIMENSIONAL IMAGING; CARDIAC IMAGING; COMPUTER ASSISTED SURGERY; MINIMALLY INVASIVE SURGERY; PROCEDURES; RADIOGRAPHY; STATISTICAL MODEL; TRUNK;

CARDIAC IMAGING TECHNIQUES; HUMANS; IMAGING, THREE-DIMENSIONAL; MINIMALLY INVASIVE SURGICAL PROCEDURES; MODELS, STATISTICAL; SURGERY, COMPUTER-ASSISTED; TOMOGRAPHY, X-RAY COMPUTED; TORSO;

EID: 84920159378     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2014.2343936     Document Type: Article

References (34)

1. T. Walther, M. Chu, and F. Mohr, "Transcatheter aortic valve implantation: Time to expand?," Curr. Opin. Cardiol., vol. 23, no. 2, pp. 111-116, 2008.
2. M. B. Leon et al., "Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery," N. Eng. J. Med., vol. 363, pp. 1597-1607, 2010.
3. S. K. Kodali et al., "Two-year outcomes after transcatheter or surgical aortic-valve replacement," N. Eng. J. Med., vol. 366, no. 18, p. 1686, 2012.
4. P. Markelj, D. Toma?evi?, B. Likar, and F. Pernu?, "A review of 3D/2D registration methods for image-guided interventions," Med. Image Anal., vol. 16, no. 3, pp. 642-661, 2012.
5. C. A. Linte et al., "Towards subject-specific models of the dynamic heart for image-guided mitral valve surgery," in Medical Image Computing and Computer-Assisted Intervention, N. Ayache, S. Ourselin, and A. Maeder, Eds., Berlin, Germany, 2007, vol. 4792, Lecture Notes Comput. Sci., pp. 94-101.
6. P. Lang et al., "US-fluoroscopy registration for transcatheter aortic valve implantation," IEEE Trans. Biomed. Eng., vol. 59, no. 5, pp. 1444-1453, May 2012.
7. J. T.Maddux et al., "Randomized study of the safety and clinical utility of rotational angiography versus standard angiography in the diagnosis of coronary artery disease," in Catheterizat. Cardiovascular Intervent., 2004, vol. 62, pp. 167-174, 2.
8. Y. Zheng,M. John, R. Liao, J. Boese, U. Kirschstein, B. Georgescu, K. S. Zhou, J. Kempfert, T. Walther, G. Brockmann, and D. Comaniciu, "Automatic aorta segmentation and valve landmark detection in C-arm CT: Application to aortic valve implantation," in Medical Image Computing and Computer-Assisted Intervention, Berlin, Germany, 2010, vol. 6361, Lecture Notes Comput. Sci., pp. 476-483.
9. J. Maintz and M. A. Viergever, "A survey of medical image registration," Med. Image Anal., vol. 2, no. 1, pp. 1-36, 1998.
10. T. Makela, P. Clarysse, O. Sipila, N. Pauna, Q. C. Pham, T. Katila, and I. E. Magnin, "A review of cardiac image registration methods," IEEE Trans. Med. Imag., vol. 21, no. 9, pp. 1011-1021, Sep. 2002.
11. K. Murphy et al., "Evaluation of registration methods on thoracic CT: The EMPIRE10 challenge," IEEE Trans. Med. Imag., vol. 30, no. 11, pp. 1901-1920, Nov. 2011.
12. M. Jenkinson and S. Smith, "A global optimisation method for robust affine registration of brain images," Med. Image Anal., vol. 5, no. 2, pp. 143-156, 2001.
13. W.Wells, P. Viola, S. Nakajima, and R. Kikinis, "Multi-modal volume registration by maximization of mutual information," Med. Image Anal., vol. 1, no. 1, pp. 35-51, 1996.
14. J. P. Pluim, J. A. Maintz, and M. A. Viergever, "Mutual-informationbased registration of medical images: A survey," IEEE Trans. Med. Imag., vol. 22, no. 8, pp. 986-1004, Aug. 2003.
15. Y. Lu, Y. Sun, R. Liao, and S. Ong, "Registration of pre-operative CT and non-contrast-enhanced C-arm CT: An application to trans-catheter aortic valve implantation (TAVI)," in Proc. Asian Conf. Comput. Vis., K. Lee, Y. Matsushita, J. Rehg, and Z. Hu, Eds., Berlin, Germany, 2013, vol. 7725, Lecture Notes Comput. Sci., pp. 268-280.
16. M. Lorenzo-Valdés, G. Sanchez-Ortiz, and D. Rueckert, "Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration," in Medical Image Computing and Computer-Assisted Intervention, Berlin, Germany, 2002, vol. 2488, Lecture Notes in Computer Science, pp. 642-650.
17. L. Zhang, C. Chefd'hotel, V. Ordy, J. Zheng, X. Deng, and B. Odry, "A knowledge-driven quasi-global registration of thoracic-abdominal CT and CBCT for image-guided interventions," in Proc. SPIE Med. Imag. 2013, Image-Guided Procedures, Robotic Intervent., Model., Mar. 2013, vol. 8671.
18. D. Neumann, S. Grbic, M. John, N. Navab, J. Hornegger, and R. Ionasec, "Robust model-based 3D/3D fusion using sparse matching for minimally invasive surgery," in Medical Image Computing and Computer-Assisted Intervention, Berlin, Germany, 2013, vol. 8149, Lecture Notes Comput. Sci., pp. 171-178.
19. Y. Zheng, F. Vega-Higuera, S. K. Zhou, and D. Comaniciu, "Fast and automatic heart isolation in 3D CT volumes: Optimal shape initialization," in Machine Learning in Medical Imaging. Berlin, Germany: Springer, 2010, vol. 6357, Lecture Notes Comput. Sci., pp. 84-91.
20. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering, and D. Comaniciu, "Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and Steerable features," IEEE Trans. Med. Imag., vol. 27, no. 11, pp. 1668-1681, Nov. 2008.
21. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuring, and D. Comaniciu, "Fast automatic heart chamber segmentation from 3D CT data using marginal space learning and steerable features," in Proc. Int. Conf. Comput. Vis., Oct. 2007, pp. 1-8.
22. T. Heimann and H.-P. Meinzer, "Statistical shape models for 3D medical image segmentation: A review," Med. Image Anal., vol. 13, no. 4, p. 543, 2009.
23. Z. Tu, "Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering," in Proc. 10th IEEE Int. Conf. Comput. Vis., 2005, vol. 2, pp. 1589-1596.
24. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. Conf. Comput. Vis. Pattern Recognit., Dec. 2001, vol. 1, pp. I511-I518.
25. Z. Tu, X. S. Zhou, L. Bogoni, A. Barbu, and D. Comaniciu, "Probabilistic 3D polyp detection in CT images: The role of sample alignment," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2006, vol. 2, pp. 1544-1551.
26. Z. Tu, "Auto-context and its application to high-level vision tasks," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1-8.
27. D. C. Liu and J. Nocedal, "On the limited memory BFGS method for large scale optimization," Math. Program., vol. 45, no. 1-3, pp. 503-528, 1989.
28. N. J. Mitra, N. Gelfand, H. Pottmann, and L. Guibas, "Registration of point cloud data from a geometric optimization perspective," in Proc. Symp. Geometry Process., 2004, pp. 22-31.
29. S. Grbic et al., "Complete valvular heart apparatus model from 4D cardiac CT," Med. Image Anal., vol. 16, no. 5, pp. 1003-1014, 2012.
30. B. K. Horn, "Closed-form solution of absolute orientation using unit quaternions," J. Opt. Soc. Am. A, vol. 4, no. 4, pp. 629-642, 1987.
31. J. Tsao, "Interpolation artifacts in multimodality image registration based on maximization of mutual information," IEEE Trans. Med. Imag., vol. 22, no. 7, pp. 854-864, Jul. 2003.
32. H. Johnson, M. McCormick, and L. Ibanez, The ITK Software Guide, 3rd ed. Clifton Park, NY: Kitware, 2013.
33. D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank, "Nonrigid multimodality image registration," in Proc. SPIEMed. Imag.: Image Process., Jul. 2001, vol. 4322, pp. 1609-1620.
34. B. Chapman, G. Jost, and R. v. d. Pas, Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation) MIT Press, Cambridge, MA, 2007.