CT-fluoroscopy (CTF) is an efficient imaging method for guiding percutaneous lung interventions such as biopsy.
During CTF-guided biopsy procedure, four to ten axial sectional images are captured in a very short time period to
provide nearly real-time feedback to physicians, so that they can adjust the needle as it is advanced toward the target
lesion. Although popularly used in clinics, this traditional CTF-guided intervention procedure may require frequent scans
and cause unnecessary radiation exposure to clinicians and patients. In addition, CTF only generates limited slices of
images and provides limited anatomical information. It also has limited response to respiratory movements and has
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 a CTF image is captured during the intervention, our algorithm can pick the best
respiratory phase-matched 3D CT image and performs a fast deformable registration to warp the 3D CT toward the CTF.
The new 3D CT image can be used to guide the intervention by superimposing the EM-guided needle location on it.
Compared to the traditional repetitive CTF guidance, the registered CT integrates both 3D volumetric patient data and
nearly real-time local anatomy for more effective and efficient guidance. In this new system, CTF is used as a nearly
real-time sensor to overcome the discrepancies between static pre-procedural CT and the patient's anatomy, so as to
provide global guidance that may be supplemented with electromagnetic (EM) tracking and to reduce the number of CTF
scans needed. In the experiments, the comparative results showed that our fast CT-CTF algorithm can achieve better