Temporal Consistent 2D-3D Registration of Lateral Cephalograms and Cone-Beam Computed Tomography Images

• Published in: Machine Learning in Medical Imaging Vol. 11046; pp. 371 - 379
• Main Authors: Zhang, Yungeng, Pei, Yuru, Qin, Haifang, Guo, Yuke, Ma, Gengyu, Xu, Tianmin, Zha, Hongbin
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783030009182; 3030009181
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-00919-9_43

Craniofacial growths and developments play an important role in treatment planning of orthopedics and orthodontics. Traditional growth studies are mainly on longitudinal growth datasets of 2D lateral cephalometric radiographs (LCR). In this paper, we propose a temporal consistent 2D-3D registration technique enabling 3D growth measurements of craniofacial structures. We initialize the independent 2D-3D registration by the convolutional neural network (CNN)-based regression, which produces the dense displacement field of the cone-beam computed tomography (CBCT) image when given the LCR. The temporal constraints of the growth-stable structures are used to refine the 2D-3D registration. Instead of traditional independent 2D-3D registration, we jointly solve the nonrigid displacement fields of a series of input LCRs captured at different ages. The hierarchical pyramid of the digitally reconstructed radiographs (DRR) is introduced to fasten the convergence. The proposed method has been applied to the growth dataset in clinical orthodontics. The resulted 2D-3D registration is consistent with both the input LCRs concerning the structural contours and the 3D volumetric images regarding the growth-stable structures.