Swin-transformer-enhanced detector z-axis virtual alignment method for cone-beam CT system

X-ray cone beam computed tomography (CBCT) is a common imaging tool in medical and industrial fields due to its non-invasive and efficient approach to 3D internal imaging. However, obtaining clear cross-section image with CBCT requires precise geometry alignment. Previous alignment methods have mostly relied on specially designed phantoms and/or the iterative optimisation of reconstructed images, both of which are costly and inefficient. This paper presented a Swin-Transformer-enhanced detector z-axis virtual geometry alignment method, which considered the tilt angle (around the central column of the detector) and the horizontal deviation of the centre of projection. The method took advantage of both cross-section and projection domains to virtually restore the geometric misalignments with the help of mathematical models. The maximum prediction error of the tilt angle was 0.0696°, and the single prediction time was about 0.0395 s. To the best of our knowledge, this work was the first study to detect the multiple geometric errors from the slice using deep learning method. It provided a new geometric alignment method for the CBCT system.