Fast CT-CT fluoroscopy registration with respiratory motion compensation for image-guided lung intervention

Abstract

CT-fluoroscopy (CTF) is an efficient imaging method for guiding percutaneous lung interventions. During CTF-guided biopsy procedure, three to ten axial sectional images are captures in a very short time period to provide nearly real-time feedback to advise adjustment of the needle as it is advanced towards the target lesion. However, thisprocedure may require frequent scans and cause unnecessary radiation exposure to physicians and technicians. Its response to respiratory movements is limited and only provides narrow local anatomical dynamics. To better utilize CTF guidance, we propose a fast CT-CTF registration algorithm with respiratory motion estimation for image-guided lung intervention using electromagnetic (EM) guidance. With the pre-procedural exhale and inhale CT scans, it would be possible to estimate a series of CT images of the same patient at different respiratory phases. Then, once CTF images are captured during the intervention, our algorithm can choose the best respiratory phase-matched 3DCT image and performs a fast deformable registration to warp the 3DCT toward CTF. The new 3D CT image can then be used by the interventional system . Compared to the traditional repetitive CTF guidance,the registered CT integrates both 3D volumetric patient data and local nearly real-time anatomy for more effective and efficient guidance. Therefore, CTF is used as a nearly real-time sensor to overcomethe discrepancies between static pre-procedural CT and the patient’sanatomy, so as to provide global guidance that may be supplementedwith electromagnetic (EM) tracking and reduce the number of CTF scans. The comparative results using simulated and real data showed thatour fast CT-CTF algorithm can achieve better registration accuracythan using traditional 3D algorithms for CT-CTF registration.