Abstract
Cone-beam C-arm CT systems allow to scan patients in weight-bearing positions to assess knee cartilage health under more realistic conditions. Involuntary patient motion during the acquisition results in motion artifacts in the reconstructions. The current motion estimation method is based on fiducial markers. They can be tracked with a high spatial accuracy in the projection images, but only deliver sparse information. Further, placement of the markers on the patient’s leg is time consuming and tedious. Instead of relying on a few well defined points, we seek to establish correspondences on dense surface data to estimate 3D displacements.
In this feasibility study, motion corrupted X-ray projections and surface data are simulated. We investigate motion estimation by registration of the surface information. The proposed approach is compared to a motion free, an uncompensated, and a state-of-the-art marker-based reconstruction using the SSIM.
The proposed approach yields motion estimation accuracy and image quality close to the current state-of-the-art, reducing the motion artifacts in the reconstructions remarkably. Using the proposed method, Structural Similarity improved from 0.887 to 0.975 compared to uncorrected images. The results are promising and encourage future work aiming at facilitating its practical applicability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aichert, A., Wang, J., Schaffert, R., Dörfler, A., Hornegger, J., Maier, A.: Epipolar consistency in fluoroscopy for image-based tracking. In: Proceedings of BMVC, pp. 82.1–82.10 (2015)
Bauer, S., et al.: Real-time range imaging in health care: a survey. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds.) Time-of-Flight and Depth Imaging. LNCS, vol. 8200, pp. 228–254. Springer, Heidelberg (2013)
Bellekens, B., Spruyt, V., Weyn, M.: A survey of rigid 3D pointcloud registration algorithms. In: The Fourth International Conference on Ambient Computing, Applications, Services and Technologies, AMBIENT 2014, pp. 8–13 (2014)
Berger, M., Müller, K., Aichert, A., Unberath, M., Thies, J., Choi, J.H., Fahrig, R., Maier, A.: Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med. Phys. 43(3), 1235–1248 (2016)
Bier, B., Aichert, A., Felsner, L., Unberath, M., Levenston, M., Gold, G., Fahrig, R., Maier, A.: Epipolar consistency conditions for motion correction in weight-bearing imaging. In: Maier-Hein, K.H., et al. (eds.) Bildverarbeitung für die Medizin 2017. Springer, Heidelberg (2017). doi:10.1007/978-3-662-54345-0_47
Choi, J.H., Fahrig, R., Keil, A., Besier, T.F., Pal, S., McWalter, E.J., Beaupré, G.S., Maier, A.: Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. Part I. Numerical model-based optimization. Med. Phys. 41(6), 061902 (2014)
Choi, J.H., Maier, A., Keil, A., Pal, S., McWalter, E.J., Beaupré, G.S., Gold, G.E., Fahrig, R.: Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment. Med. Phys. 41(6), 061902 (2014)
Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., Buatti, J., Aylward, S., Miller, J.V., Pieper, S., Kikinis, R.: 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging 30(9), 1323–1341 (2012)
Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. J. Opt. Soc. Am. A 1(6), 612 (1984)
Fischer, M., Fuerst, B., Lee, S.C., Fotouhi, J., Habert, S., Weidert, S., Euler, E., Osgood, G., Navab, N.: Preclinical usability study of multiple augmented reality concepts for K-wire placement. Int. J. Comput. Assist. Radiol. Surg. 11(6), 1007–1014 (2016)
Fotouhi, J., Fuerst, B., Wein, W., Navab, N.: Can real-time RGBD enhance intraoperative Cone-Beam CT? Int. J. Comput. Assist. Radiol. Surg. (2017). https://link.springer.com/journal/11548/onlineFirst/page/3
Geimer, T., Unberath, M., Taubmann, O., Bert, C., Maier, A.: Combination of markerless surrogates for motion estimation in radiation therapy. In: Computer Assisted Radiology and Surgery (CARS) 2016, pp. 59–60 (2016)
Maier, A., Choi, J.H., Keil, A., Niebler, C., Sarmiento, M., Fieselmann, A., Gold, G., Delp, S., Fahrig, R.: Analysis of vertical and horizontal circular C-arm trajectories. In: SPIE Medical Imaging, vol. 7961, pp. 796123-1–796123-8 (2011)
Maier, A., Hofmann, H.G., Berger, M., Fischer, P., Schwemmer, C., Wu, H., Müller, K., Hornegger, J., Choi, J.H., Riess, C., Keil, A., Fahrig, R.: CONRAD - a software framework for cone-beam imaging in radiology. Med. Phys. 40(11), 111914 (2013)
McNamara, J.E., Pretorius, P.H., Johnson, K., Mukherjee, J.M., Dey, J., Gennert, M.A., King, M.A.: A flexible multicamera visual-tracking system for detecting and correcting motion-induced artifacts in cardiac SPECT slices. Med. Phys. 36(5), 1913–1923 (2009)
Müller, K., Berger, M., Choi, J., Maier, A., Fahrig, R.: Automatic motion estimation and compensation framework for weight-bearing C-arm CT scans using fiducial markers. In: IFMBE Proceedings, pp. 58–61 (2015)
Müller, K., Berger, M., Choi, J.H., Datta, S., Gehrisch, S., Moore, T., Marks, M.P., Maier, A.K., Fahrig, R.: Fully automatic head motion correction for interventional C-arm systems using fiducial markers. In: Proceedings of the 13th Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, pp. 534–537 (2015)
Ouadah, S., Stayman, J.W., Gang, G.J., Ehtiati, T., Siewerdsen, J.H.: Self-calibration of cone-beam CT geometry using 3D2D image registration. Phys. Med. Biol. 61(7), 2613–2632 (2016)
Powers, C.M., Ward, S.R., Fredericson, M.: Knee extension in persons with lateral subluxation of the patella: a preliminary study. J. Orthop. Sports Phys. Ther. 33(11), 677–685 (2013)
Sisniega, A., Stayman, J.W., Cao, Q., Yorkston, J., Siewerdsen, J.H., Zbijewski, W.: Image-based motion compensation for high-resolution extremities cone-beam CT. In: SPIE Medical Imaging, vol. 9783, p. 97830K (2016)
Sisniega, A., Stayman, J., Yorkston, J., Siewerdsen, J., Zbijewski, W.: Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion. Phys. Med. Biol. 62(9), 3712 (2017)
Unberath, M., Choi, J.H., Berger, M., Maier, A., Fahrig, R.: Image-based compensation for involuntary motion in weight-bearing C-arm cone-beam CT scanning of knees. In: SPIE Medical Imaging, vol. 9413, p. 94130D (2015)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wasenmüller, O., Stricker, D.: Comparison of Kinect v1 and v2 depth images in terms of accuracy and precision. In: Chen, C.S., Lu, J., Ma, K.K. (eds.) ACCV 2016. LNCS, vol. 10117, pp. 34–45. Springer, Cham (2016). doi:10.1007/978-3-319-54427-4_3
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bier, B. et al. (2017). Motion Compensation Using Range Imaging in C-Arm Cone-Beam CT. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-60964-5_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60963-8
Online ISBN: 978-3-319-60964-5
eBook Packages: Computer ScienceComputer Science (R0)