3D/2D model-to-image registration by imitation learning for cardiac procedures

• Published in: International journal for computer assisted radiology and surgery Vol. 13; no. 8; pp. 1141 - 1149
• Main Authors: Toth, Daniel, Miao, Shun, Kurzendorfer, Tanja, Rinaldi, Christopher A., Liao, Rui, Mansi, Tommaso, Rhode, Kawal, Mountney, Peter
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2018; Springer Nature B.V
• Subjects: Algorithms; Cardiac Resynchronization Therapy - methods; Computer Imaging; Computer Science; Datasets; Feasibility studies; Field of view; Ground truth; Health Informatics; Heart - diagnostic imaging; Humans; Image enhancement; Image Processing, Computer-Assisted - methods; Image registration; Imaging; Imaging, Three-Dimensional; Machine Learning; Medicine; Medicine & Public Health; Model accuracy; Models, Anatomic; Multimodal Imaging - methods; Original; Original Article; Pattern Recognition and Graphics; Radiology; Registration; Reproducibility of Results; Robustness; Surgery; Three dimensional models; Two dimensional models; Vision; X-rays; Deep learning; Cardiac registration; Imitation learning; Cardiac resynchronization therapy; Image fusion
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-018-1774-y

Purpose In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application. Methods This paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images. Results Accuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was 2.92 ± 2.22 mm on 1000 test cases, superior to that of manual ( 6.48 ± 5.6 mm ) and gradient-based ( 6.79 ± 4.75 mm ) registration. High robustness is shown in 19 clinical CRT cases. Conclusion Besides the proposed methods feasibility in a clinical environment, evaluation has shown good accuracy and high robustness indicating that it could be applied in image-guided interventions.