Abstract
Purpose:
Minimally invasive beating-heart surgery is currently performed using endoscopes and without navigation. Registration of intraoperative ultrasound to a preoperative cardiac CT scan is a valuable step toward image-guided navigation.
Methods:
The registration was achieved by first extracting a representative point set from each ultrasound image in the sequence using a deformable registration. A template shape representing the cardiac chambers was deformed through a hierarchy of affine transformations to match each ultrasound image using a generalized expectation maximization algorithm. These extracted point sets were matched to the CT by exhaustively searching over a large number of precomputed slices of 3D geometry. The result is a similarity transformation mapping the intraoperative ultrasound to preoperative CT.
Results:
Complete data sets were acquired for four patients. Transesophageal echocardiography ultrasound sequences were deformably registered to a model of oriented points with a mean error of 2.3 mm. Ultrasound and CT scans were registered to a mean of 3 mm, which is comparable to the error of 2.8 mm expected by merging ultrasound registration with uncertainty of cardiac CT.
Conclusion:
The proposed algorithm registered 3D CT with dynamic 2D intraoperative imaging. The algorithm aligned the images in both space and time, needing neither dynamic CT imaging nor intraoperative electrocardiograms. The accuracy was sufficient for navigation in thoracoscopically guided beating-heart surgery.
Similar content being viewed by others
References
Bisleri G, Rosati F, Bontempi L, Curnis A, Muneretto C (2013) Hybrid approach for the treatment of long-standing persistent atrial fibrillation: electrophysiological findings and clinical results. Eur J Cardiothorac Surg 44(5):919–923
Muneretto C, Bisleri G, Bontempi L, Curnis A (2012) Durable staged hybrid ablation with thoracoscopic and percutaneous approach for treatment of long-standing atrial fibrillation: a 30-month assessment with continuous monitoring. J Thorac Cardiovasc Surg 144(6):1460–1465
Huang X, Moore J, Guiraudon G, Jones DL, Bainbridge D, Ren J, Peters TM (2009) Dynamic 2D ultrasound and 3D CT image registration of the beating heart. IEEE Trans Med Imaging 28(8):1179–1189
Luo Z, Cai J, Peters TM, Gu L (2013) Intra-operative 2-D ultrasound and dynamic 3-D aortic model registration for magnetic navigation of transcatheter aortic valve implantation. IEEE Trans Med Imaging 32(11):2152–2165
Tavard F, Simon A, Leclercq C, Donal E, Hernndez AI, Garreau M (2014) Multimodal registration and data fusion for cardiac resynchronization therapy optimization. IEEE Trans Med Imaging 33(6):1363–1372
Li FP, Rajchl M, White JA, Goela A, Peters TM (2015) Ultrasound guidance for beating heart mitral valve repair augmented by synthetic dynamic CT. IEEE Trans Med Imaging 34(10):2025–2035
Khalil A, Faisal A, Lai KW, Ng SC, Liew YM (2017) 2D to 3D fusion of echocardiography and cardiac CT for TAVR and TAVI image guidance. Med Biol Eng Comput 55(8):1317–1326
Sandoval Z, Castro M, Alirezaie J, Bessire F, Lafon C, Dillenseger JL (2018) Transesophageal 2D ultrasound to 3D computed tomography registration for the guidance of a cardiac arrhythmia therapy. Phys Med Biol 63(15):155007
Tavakoli V, Amini AA (2013) A survey of shaped-based registration and segmentation techniques for cardiac images. Comput Vis Image Underst 117(9):966–989
Ravikumar N, Gooya A, Frangi AF, Taylor ZA (2017) Generalised coherent point drift for group-wise registration of multi-dimensional point sets. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duchesne S (eds) Medical image computing and computer-assisted intervention, vol 10433. LNCS. Springer, Cham, pp 309–316
Min Z, Wang J, Meng MQH (2018) Robust generalized point cloud registration using hybrid mixture model. In: IEEE Int Conf Robot Autom, pp 4812–4818
Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256
Ferrante E, Paragios N (2017) Slice-to-volume medical image registration: a survey. Med Image Anal 39:101–123
Ferrante E, Paragios N (2013) Non-rigid 2D–3D medical image registration using Markov random fields. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical image computing and computer-assisted intervention, vol 8151. LNCS. Springer, Heidelberg, pp 163–170
Ferrante E, Fecamp V, Paragios N (2015) Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation. Int J Comput Assist Radiol Surg 10(6):791–800
Ferrante E, Fecamp V, Paragios N (2015) Implicit planar and in-plane deformable mapping in medical images through high order graphs. In: Proc IEEE Int Symp Biomed Imaging, pp 721–724
Ferrante E, Paragios N (2018) Graph-based slice-to-volume deformable registration. Int J Comput Vis 126(1):36–58
Zikic D, Glocker B, Kutter O, Groher M, Komodakis N, Kamen A, Paragios N, Navab N (2010) Linear intensity-based image registration by Markov random fields and discrete optimization. Med Image Anal 14(4):550–562
Porchetto R, Stramana F, Paragios N, Ferrante E (2017) Rigid slice-to-volume medical image registration through Markov random fields. Med Comput Vis Bayesian Graph Models Biomed Imaging 2016:172–185
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B Methodol 39(1):1–38
Maiseli B, Gu Y, Gao H (2017) Recent developments and trends in point set registration methods. J Vis Commun Image Represent 46(C):95–106
Meng XL, Rubin DB (1993) Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika 80(2):267–278
Sra S (2012) A short note on parameter approximation for von Mises–Fisher distributions: and a fast implementation of \(I_s(x)\). Comput Stat 27(1):177–190
Horaud R, Forbes F, Yguel M, Dewaele G, Zhang J (2011) Rigid and articulated point registration with expectation conditional maximization. IEEE Trans Pattern Anal Mach Intell 33(3):587–602
Buechel RR, Husmann L, Herzog BA, Pazhenkottil AP, Nkoulou R, Ghadri JR, Treyer V, von Schulthess P, Kaufmann PA (2011) Low-dose computed tomography coronary angiography with prospective electrocardiogram triggering: feasibility in a large population. J Am Coll Cardiol 57(3):332–336
Boersma LV, Castella M, van Boven W, Berruezo A, Yilmaz A, Nadal M, Sandoval E, Calvo N, Brugada J, Kelder J, Wijffels M, Mont L (2012) Atrial fibrillation catheter ablation versus surgical ablation treatment (fast): a 2-center randomized clinical trial. Circulation 125(1):23–30
Khoynezhad A, Ellenbogen KA, Al-Atassi T, Wang PJ, Kasirajan V, Wang X, Edgerton JR (2017) Hybrid atrial fibrillation ablation: current status and a look ahead. Circ Arrhythm Electrophysiol 10(10):e005263
Jacob G, Noble JA, Behrenbruch C, Kelion AD, Banning AP (2002) A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography. IEEE Trans Med Imaging 21(3):226–238
Billings S, Taylor R (2015) Generalized iterative most likely oriented-point (G-IMLOP) registration. Int J Comput Assist Radiol Surg 10:1213–1226
Serafin J, Grisetti G (2015) NICP: dense normal based point cloud registration. Rep U S, pp 742–749
Baka N, Metz CT, Schultz CJ, van Geuns R, Niessen WJ, van Walsum T (2014) Oriented Gaussian mixture models for nonrigid 2D/3D coronary artery registration. IEEE Trans Med Imaging 33(5):1023–1034
Neal RM, Hinton GE (1998) A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan MI (ed) Learning in graphical models. Springer, Dordrecht, pp 355–368
Granger S, Pennec X (2002) Multi-scale EM-ICP: a fast and robust approach for surface registration. Comput Vis ECCV, pp 418–432
Bernard F, Salamanca L, Thunberg J, Tack A, Jentsch D, Lamecker H, Zachow S, Hertel F, Goncalves J, Gemmar P (2017) Shape-aware surface reconstruction from sparse 3D point-clouds. Med Image Anal 38:77–89
Zhou Z, Zheng J, Dai Y, Zhou Z, Chen S (2014) Robust non-rigid point set registration using Student’s-t mixture model. PLoS One 9(3):1–11
Ravikumar N, Gooya A, Çimen S, Frangi AF, Taylor ZA (2018) Group-wise similarity registration of point sets using Student’s t-mixture model for statistical shape models. Med Image Anal 44:156–176
Lowe DG (1999) Object recognition from local scale-invariant features. In: Proc IEEE Int Conf Comput Vis, vol 2, pp 1150–1157
Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346–359
Toews M, Wells WM III (2013) Efficient and robust model-to-image alignment using 3D scale-invariant features. Med Image Anal 17(3):271–282
Toews M, Wells WM III (2018) Phantomless auto-calibration and online calibration assessment for a tracked freehand 2-D ultrasound probe. IEEE Trans Med Imaging 37(1):262–272
Machado I, Toews M, Luo J, Unadkat P, Essayed W, George E, Teodoro P, Carvalho H, Martins J, Golland P, Pieper S, Frisken S, Golby A, Wells W III (2018) Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching. Int J Comput Assist Radiol Surg 13(10):1525–1538
Bersvendsen J, Toews M, Danudibroto A, Wells III WM, Urheim S, Estépar RSJ, Samset E (2016) Robust spatio-temporal registration of 4D cardiac ultrasound sequences. Proc SPIE Int Soc Opt Eng
Lasowski R, Benhimane S, Vogel J, Jakobs TF, Zech CJ, Trumm C, Clason C, Navab N (2008) Adaptive visualization for needle guidance in RF liver ablation: taking organ deformation into account. Proc SPIE Int Soc Opt Eng 6918:69180A
Acknowledgements
This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under Grant RGPIN-2018-04430.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 1 (mp4 4486 KB)
Rights and permissions
About this article
Cite this article
Peoples, J.J., Bisleri, G. & Ellis, R.E. Deformable multimodal registration for navigation in beating-heart cardiac surgery. Int J CARS 14, 955–966 (2019). https://doi.org/10.1007/s11548-019-01932-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-019-01932-2