  In cone-beam X-ray transmission imaging, perspective deformation causes
difficulty in direct, accurate geometric assessments of anatomical structures.
In this work, the perspective deformation correction problem is formulated and
addressed in a framework using two complementary (180{\deg}) views. The
complementary view setting provides a practical way to identify perspectively
deformed structures by assessing the deviation between the two views. It also
provides bounding information and reduces uncertainty for learning perspective
deformation. Two representative networks Pix2pixGAN and TransU-Net for
correcting perspective deformation are investigated. Experiments on numerical
bead phantom data demonstrate the advantage of complementary views over
orthogonal views or a single view. They show that Pix2pixGAN as a fully
convolutional network achieves better performance in polar space than Cartesian
space, while TransU-Net as a transformer-based hybrid network achieves
comparable performance in Cartesian space to polar space. Further study
demonstrates that the trained model has certain tolerance to geometric
inaccuracy within calibration accuracy. The efficacy of the proposed framework
on synthetic projection images from patients' chest and head data as well as
real cadaver CBCT projection data and its robustness in the presence of bulky
metal implants and surgical screws indicate the promising aspects of future
real applications.
