Abstract
Fused MRI-CBCT images provide desirable complementary information of the articular disc and condyle surface for optimum diagnosis, has been shown to be accurate and reliable in Temporomandibular Disorders (TMD) assessment. But field-of-view difference between multi-modality images brings challenges to conventional registration algorithms. In this paper, we proposed a landmark-guided learning method for Temporomandibular Joint (TMJ) MRI-CBCT images registration. First, end-to-end landmark localization network was used to detect correspondence landmark pairs in the different modality images to generate the landmark guidance information. Then taking image patches centered landmarks as input, an unsupervised learning network regresses the rigid transformation matrix using mutual information as a measure of similarity between image patches. Finally combined landmarks coordinates with the rigid transformation matrix, the whole image registration can be realized. Experiment results demonstrate that our approach achieves better overall performance on registration of images from different patients and modalities with 100x speed-up in execution time.
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Notes
- 1.
Medical Image Registration Library – SimpleElastix: https://simpleelastix.github.io/.
- 2.
Advanced Normalization Tools – ANTs: https://stnava.github.io/ANTs/.
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Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities (2021JBM003) and the National Natural Science Foundation of China with Project (81671034). Computations used the Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill facility.
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Li, J., Wang, Y., Wang, S., Zhang, K., Li, G. (2021). Landmark-Guided Rigid Registration for Temporomandibular Joint MRI-CBCT Images with Large Field-of-View Difference. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_54
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