Chapter title |
Fast Marker Based C-Arm Pose Estimation
|
---|---|
Chapter number | 78 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008
|
Published in |
Lecture notes in computer science, September 2008
|
DOI | 10.1007/978-3-540-85990-1_78 |
Pubmed ID | |
Book ISBNs |
978-3-54-085989-5, 978-3-54-085990-1
|
Authors |
Bernhard Kainz, Markus Grabner, Matthias Rüther, Kainz, B, Grabner, M, Rüther, M, Kainz, Bernhard, Grabner, Markus, Rüther, Matthias |
Abstract |
To estimate the pose of a C-Arm during interventions therapy we have developed a small sized X-Ray Target including a special set of beads with known locations in 3D space. Since the patient needs to remain in the X-Ray path for all feasible poses of the C-Arm during the intervention, we cannot construct a single marker which is entirely visible in all images. Therefore finding 2D-3D point correspondences is a non-trivial task. The marker pattern has to be chosen in a way such that its projection onto the image plane is unique in a minimal-sized window for all relevant poses of the C-Arm. We use a two dimensional adaption of a linear feedback shift register (LFSR) to generate a two-dimensional pattern with unique sub-patterns in a certain window range. Thereby uniqueness is not achieved by placing unique 2D sub patterns side by side but by the code property itself. The code is designed in a way that any sub window of a minimal size guarantees uniqueness and that even occlusions from medical instruments can be handled. Experiments showed that we were able to estimate the C-Arm's pose from a single image within one second with a precision below one millimeter and one degree. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 6% |
France | 1 | 6% |
Austria | 1 | 6% |
Unknown | 15 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 4 | 22% |
Student > Ph. D. Student | 4 | 22% |
Lecturer | 3 | 17% |
Other | 2 | 11% |
Student > Doctoral Student | 2 | 11% |
Other | 2 | 11% |
Unknown | 1 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 10 | 56% |
Engineering | 4 | 22% |
Medicine and Dentistry | 2 | 11% |
Physics and Astronomy | 1 | 6% |
Unknown | 1 | 6% |