Landmark-Guided Rigid Registration for Temporomandibular Joint MRI-CBCT Images with Large Field-of-View Difference.

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.