title: Markerless real-time 3-D target region tracking by motion backprojection from projection images.
abstract: Accurate and fast localization of a predefined target region inside the patient is an important component of many image-guided therapy procedures. This problem is commonly solved by registration of intraoperative 2-D projection images to 3-D preoperative images. If the patient is not fixed during the intervention, the 2-D image acquisition is repeated several times during the procedure, and the registration problem can be cast instead as a 3-D tracking problem. To solve the 3-D problem, we propose in this paper to apply 2-D region tracking to first recover the components of the transformation that are in-plane to the projections. The 2-D motion estimates of all projections are backprojected into 3-D space, where they are then combined into a consistent estimate of the 3-D motion. We compare this method to intensity-based 2-D to 3-D registration and a combination of 2-D motion backprojection followed by a 2-D to 3-D registration stage. Using clinical data with a fiducial marker-based gold-standard transformation, we show that our method is capable of accurately tracking vertebral targets in 3-D from 2-D motion measured in X-ray projection images. Using a standard tracking algorithm (hyperplane tracking), tracking is achieved at video frame rates but fails relatively often (32% of all frames tracked with target registration error (TRE) better than 1.2 mm, 82% of all frames tracked with TRE better than 2.4 mm). With intensity-based 2-D to 2-D image registration using normalized mutual information (NMI) and pattern intensity (PI), accuracy and robustness are substantially improved. NMI tracked 82% of all frames in our data with TRE better than 1.2 mm and 96% of all frames with TRE better than 2.4 mm. This comes at the cost of a reduced frame rate, 1.7 s average processing time per frame and projection device. Results using PI were slightly more accurate, but required on average 5.4 s time per frame. These results are still substantially faster than 2-D to 3-D registration. We conclude that motion backprojection from 2-D motion tracking is an accurate and efficient method for tracking 3-D target motion, but tracking 2-D motion accurately and robustly remains a challenge.
authors.affiliation: Neuroscience Program at SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025-3493, USA. torsten@synapse.sri.com
authors.forename: Torsten
authors.initials: T
authors.lastname: Rohlfing
authors.forename: Joachim
authors.initials: J
authors.lastname: Denzler
authors.forename: Christoph
authors.initials: C
authors.lastname: Grässl
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Russakoff
authors.forename: Calvin R
authors.initials: CR
authors.lastname: Maurer
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003199:Computer Systems; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009068:Movement; D038361:Neuronavigation / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D016634:Radiosurgery / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique