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Range Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions

Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
Erlangen Graduate School in Advanced Optical Technologies (SAOT), 91052 Erlangen, Germany
Radiological Sciences Laboratory, Stanford University, Stanford, CA 94305, USA
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Bier, B.; Unberath, M.; Geimer, T.; Maier, J.; Gold, G.; Levenston, M.; Fahrig, R.; Maier, A. Motion Compensation Using Range Imaging in C-Arm Cone-Beam CT. In the Annual Conference on Medical Image Understanding and Analysis. Springer: Cham, Switzerland, 2017; pp. 561–570.
J. Imaging 2018, 4(1), 13;
Received: 7 November 2017 / Revised: 3 January 2018 / Accepted: 3 January 2018 / Published: 6 January 2018
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
PDF [5057 KB, uploaded 8 January 2018]


C-arm cone-beam computed tomography (CBCT) has been used recently to acquire images of the human knee joint under weight-bearing conditions to assess knee joint health under load. However, involuntary patient motion during image acquisition leads to severe motion artifacts in the subsequent reconstructions. The state-of-the-art uses fiducial markers placed on the patient’s knee to compensate for the induced motion artifacts. The placement of markers is time consuming, tedious, and requires user experience, to guarantee reliable motion estimates. To overcome these drawbacks, we recently investigated whether range imaging would allow to track, estimate, and compensate for patient motion using a range camera. We argue that the dense surface information observed by the camera could reveal more information than only a few surface points of the marker-based method. However, the integration of range-imaging with CBCT involves flexibility, such as where to position the camera and what algorithm to align the data with. In this work, three dimensional rigid body motion is estimated for synthetic data acquired with two different range camera trajectories: a static position on the ground and a dynamic position on the C-arm. Motion estimation is evaluated using two different types of point cloud registration algorithms: a pair wise Iterative Closest Point algorithm as well as a probabilistic group wise method. We compare the reconstruction results and the estimated motion signals with the ground truth and the current reference standard, a marker-based approach. To this end, we qualitatively and quantitatively assess image quality. The latter is evaluated using the Structural Similarity (SSIM). We achieved results comparable to the marker-based approach, which highlights the potential of both point set registration methods, for accurately recovering patient motion. The SSIM improved from 0.94 to 0.99 and 0.97 using the static and the dynamic camera trajectory, respectively. Accurate recovery of patient motion resulted in remarkable reduction in motion artifacts in the CBCT reconstructions, which is promising for future work with real data. View Full-Text
Keywords: surface reconstruction; motion correction; misalignment correction; CBCT reconstruction; range camera simulation; point cloud registration surface reconstruction; motion correction; misalignment correction; CBCT reconstruction; range camera simulation; point cloud registration

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Bier, B.; Ravikumar, N.; Unberath, M.; Levenston, M.; Gold, G.; Fahrig, R.; Maier, A. Range Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions. J. Imaging 2018, 4, 13.

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