Learning Perspective Deformation in X-Ray Transmission Imaging

Abstract

In cone-beam X-ray transmission imaging, due to the divergence of X-rays, imaged structures with different depths have different magnification factors on an X-ray detector, which results in perspective deformation. Perspective deformation causes difficulty in direct, accurate geometric assessments of anatomical structures. In this work, in order to reduce perspective deformation in X-ray images acquired from regular cone-beam computed tomography (CBCT) systems, we investigate on learning perspective deformation, i.e., converting perspective (cone-beam) projections into orthogonal (parallel-beam) projections. Directly converting a single perspective projection image into an orthogonal projection image is extremely challenging due to the lack of depth information. Therefore, we propose to utilize one additional perspective projection, a complementary (180 • ) or orthogonal (90 • ) view, to provide a certain degree of depth information. Furthermore, learning perspective deformation in different spatial domains, i.e. in Cartesian, polar, and log-polar coordinates, is investigated. Our proposed method is evaluated on numerical spherical bead phantoms as well as patients' chest and head X-ray data. The experiments on numerical bead phantom data demonstrate that learning perspective deformation in polar coordinates has significant advantages over learning in Cartesian coordinates, as root-mean-square error (RMSE) decreases from 5.31 to 1.40, while learning in log-polar coordinates has no further considerable improvement (RMSE = 1.85). In addition, using a complementary view (RMSE = 1.40) is better than an orthogonal view (RMSE = 3.87). The experiments on patients' chest and head data demonstrate that learning perspective deformation using dual complementary views is also applicable in anatomical X-ray data, allowing accurate cardiothoracic ratio measurements in chest X-ray images and cephalometric analysis in synthetic cephalograms from cone-beam X-ray projections. Our proof-of-concept experiments indicate that learning perspective deformation has the potential to empower conventional CBCT systems with more applications, e.g. chest X-ray imaging, which typically require specialized X-ray devices.