Diplomarbeit / Masterarbeit / Bachelor thesis (DA/MA/BA): C-arm Motion Estimation without Radiation

[Subtitle]
Thesis by:
Advisor: Professor Nassir Navab
Supervision by: Lejing Wang, Joerg Traub
Due date:

Abstract

C-arm (X-ray source) motion estimation is a crucial step for many (computer aided) clinical applications, like X-ray image stitching, C-arm placement and so on. Most approaches are based on using X-ray images, and thus introduce additional radiation. Our Camera Augmented Mobile C-arm system is capable of acquiring the X-ray and optical image overlay, so the pose or motion of C-arm X-ray source can be computed using the information from optical images. This will definitely save a lot of radiation. Our current method for pose/motion estimation is using a planar marker pattern. The shortcomings are the difficulty of integrating this planar pattern into the clinical procedure and the limited viewed field to the camera. Therefore, using a flexible distribution of markers, in which markers can located in any 3D positions without any constraint, makes integration much easier and can provide an unlimited viewed field. However, using such a flexible distribution of markers will lose the 3D positions of each marker and thus the estimated translation is up to a scaling factor. We developed a novel method to recover this scaling factor. The main goal of this student project is to implement, evaluate and integrate the method.

Our Camera Augmented Mobile C-arm system for real patient study	The attached camera and the double mirror construction	Visualization shows the overlay of the x-ray onto a foot

Our used planar marker pattern for motion estimation	The result of X-ray stitching using the CamCsystem

Task:

Your responsibility will be to implement the method of recovering the scaling factor, and evaluate and integrate this component into our X-ray image stitching and C-arm placement modules within close supervision by an interdisciplinary and international team. We have a mature library of computer vision algorithms implemented in C++ and matlab, including basic Matrix operations, various numeric methods, camera calibration algorithms, advanced pose estimation algorithms, DLT algorithms, 3D reconstruction and so on. You can definitely make use of these resources and minimize your coding work.

Requirements

• C++ in MS Visual Studio. (Matlab is recommendable )
• Interest in medical technology (You will work most of the time at Narvis).
• Basic knowledge of 3D computer vision is recommendable .

What do we offer

• Experience with different fields of research in computer science: Computer Vision, Visualization, Software Engineering.
• Knowledge about clinical/surgical workflow, medical technology (watching a real computer aided medical operation is possible).
• Your thesis and degree. (and also an academic paper, which depends on your performance)
• Working within the real project on the cutting edge technology. The CamC system is introduced into the real patient study, and the outcomes of your research will be used in the near future.
• Coffee machine, Narvis lab working space in the city center.

Contact

If you are interested please contact Lejing Wang.

Resources

Literature

• Navab, N., Mitschke, M., Bani-Hashemi, A.: Merging visible and invisible: Two camera-augmented mobile C-arm (CAMC) applications. In: Proc. IEEE and ACM Int'l Workshop on Augmented Reality, San Francisco, CA, USA (1999) 134{141
• Wang, L., Traub, J., Heining, S.M., Benhimane, S., Euler, E., Graumann, R., Navab, N.: Long bone x-ray image stitching using camera augmented mobile carm. In Metaxas, D., Axel, L., Fichtinger, G., Szekely, G., eds.: Medical Image Computing and Computer-Assisted Intervention { MICCAI 2008. Volume 5242 of LNCS., Springer (2008) 578{586
• Zhang, X., Fronz, S., Navab, N.: Visual marker detection and decoding in ar systems: A comparative study. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR'02), October 2002. (2002)
• Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, New York, NY, USA (2003)
• Wei Wang; Hung Tat Tsui: A SVD decomposition of essential matrix with eight solutions for the relative positions of two perspective cameras. Pattern Recognition, 2000. Proceedings. 15th International Conference on Volume 1, Issue , 2000 Page(s):362 - 365 vol.1
• S. Benhimane, E. Malis: Homography-based 2D Visual Servoing. IEEE International Conference on Robotics and Automation, Orlando, USA, May 2006.