Large-Deformation Image Registration of CT-TEE for Surgical Navigation of Congenital Heart Disease

The surgical treatment of congenital heart disease requires navigational assistance with transesophageal echocardiography (TEE); however, TEE images are often difficult to interpret and provide very limited anatomical information. Registering preoperative CT images to intraoperative TEE images provides surgeons with richer and more useful anatomical information. Yet, CT and TEE images differ substantially in terms of scale and geometry. In the present research, we propose a novel method for the registration of CT and TEE images for navigation during surgical repair of large defects in patients with congenital heart disease. Valve data was used for the coarse registration to determine the basic location. This was followed by the use of an enhanced probability model map to overcome gray-level differences between the two imaging modalities. Finally, the rapid optimization of mutual information was achieved by migrating parameters. This method was tested on a dataset of 240 images from 12 infant, children (≤ 3 years old), and adult patients with congenital heart disease. Compared to the “bronze standard” registration, the proposed method was more accurate with an average Dice coefficient of 0.91 and an average root mean square of target registration error of 1.2655 mm.

[1]  Rama Chellappa,et al.  A new approach to image feature detection with applications , 1996, Pattern Recognit..

[2]  Smita Pradhan,et al.  Enhanced Mutual Information-based Multimodal Brain MR Image Registration Using Phase Congruency , 2018 .

[3]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[4]  Ibrahim Atli,et al.  The effect of contrast enhancement techniques on mutual information based image registration , 2017, 2017 25th Signal Processing and Communications Applications Conference (SIU).

[5]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[6]  Liang-Gee Chen,et al.  A spatial first 3-D XYT feature point extraction algorithm for efficient human action recognition , 2014, 2014 IEEE International Conference on Consumer Electronics - Taiwan.

[7]  John Moore,et al.  Predicting target vessel location on robot-assisted coronary artery bypass graft using CT to ultrasound registration. , 2012, Medical physics.

[8]  Feng Li,et al.  Towards real-time 3D US-CT registration on the beating heart for guidance of minimally invasive cardiac interventions , 2012, Medical Imaging.

[9]  M. Phelps,et al.  Automated iterative three-dimensional registration of positron emission tomography images. , 1993, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[10]  Jay B. West,et al.  Predicting error in rigid-body point-based registration , 1998, IEEE Transactions on Medical Imaging.

[11]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[12]  D. R. Fish,et al.  A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs. , 1994, Medical physics.

[13]  Claus Brenner,et al.  3D feature point extraction from LiDAR data using a neural network , 2016 .

[14]  David J. Hawkes,et al.  Registration of freehand 3D ultrasound and magnetic resonance liver images , 2004, Medical Image Anal..

[15]  Paul L. Rosin,et al.  Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging , 2015, Pattern Recognit..

[16]  C. Brenner,et al.  3 D FEATURE POINT EXTRACTION FROM LIDAR DATA USING A NEURAL NETWORK , 2016 .

[17]  Max H. M. Costa,et al.  Automatic registration of satellite images , 1997, Proceedings X Brazilian Symposium on Computer Graphics and Image Processing.

[18]  Douglas L. Jones,et al.  Inside the beating heart: an in vivo feasibility study on fusing pre- and intra-operative imaging for minimally invasive therapy , 2009, International Journal of Computer Assisted Radiology and Surgery.

[19]  Zhen Wang,et al.  A Multiscale and Hierarchical Feature Extraction Method for Terrestrial Laser Scanning Point Cloud Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[20]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[21]  Maoguo Gong,et al.  A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Viktoria Fodor,et al.  Characterization of SURF and BRISK Interest Point Distribution for Distributed Feature Extraction in Visual Sensor Networks , 2015, IEEE Transactions on Multimedia.

[23]  Daniel Rueckert,et al.  Non-rigid registration using higher-order mutual information , 2000, Medical Imaging.

[24]  Yang Cheng,et al.  Object Recognition by Using Multi-level Feature Point Extraction , 2017, ArXiv.

[25]  Jerry L. Prince,et al.  Multimodal Registration via Mutual Information Incorporating Geometric and Spatial Context , 2015, IEEE Transactions on Image Processing.

[26]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[27]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[28]  Douglas L. Jones,et al.  Surface-Based CT–TEE Registration of the Aortic Root , 2013, IEEE Transactions on Biomedical Engineering.

[29]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.