Marker‐free motion correction in weight‐bearing cone‐beam CT of the knee joint
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Purpose To allow for a purely image-based motion estimation and compensation in weight-bearing cone-beam computed tomography of the knee joint. Methods Weight-bearing imaging of the knee joint in a standing position poses additional requirements for the image reconstruction algorithm. In contrast to supine scans, patient motion needs to be estimated and compensated. The authors propose a method that is based on 2D/3D registration of left and right femur and tibia segmented from a prior, motion-free reconstruction acquired in supine position. Each segmented bone is first roughly aligned to the motion-corrupted reconstruction of a scan in standing or squatting position. Subsequently, a rigid 2D/3D registration is performed for each bone to each of K projection images, estimating 6 × 4 × K motion parameters. The motion of individual bones is combined into global motion fields using thin-plate-spline extrapolation. These can be incorporated into a motion-compensated reconstruction in the backprojection step. The authors performed visual and quantitative comparisons between a state-of-the-art marker-based (MB) method and two variants of the proposed method using gradient correlation (GC) and normalized gradient information (NGI) as similarity measure for the 2D/3D registration. Results The authors evaluated their method on four acquisitions under different squatting positions of the same patient. All methods showed substantial improvement in image quality compared to the uncorrect…
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A 2D intraoperative X-ray can be registered to a 3D preoperative CT volume by maximizing its similarity to a rendered X-ray with respect to the camera pose.
“A 2D intraoperative X-ray can be registered to a 3D preoperative CT volume by maximizing its similarity to a rendered X-ray with respect to the camera pose [8, 14, 34].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [8]
Hand-crafted similarity metrics are positively correlated with pose differences when perturbations are small, but are generally non-convex and fail to accurately represent pose offsets when perturbations are large.
“While these metrics are positively correlated with pose differences when the perturbations in {K^v,T^xv,ω^D} are small, they are generally non-convex and fail to accurately represent pose offsets when perturbations are large.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Berger et al., 2016
Conventional intensity-based 2D/3D registration methods mostly use hand-crafted similarity metrics between the current estimate and the 2D observation as loss function, with common choices being Normalized Cross Correlation, gradient information, or Mutual Information.
“Conventional intensity-based methods mostly use hand-crafted similarity metrics between y v and its current best estimate y^v=(P(Kv,Txv)◦DωD)(x)fx1 as loss function. Common choices for the similarity metric are Normalized Cross Correlation (NCC), gradient information (Berger et al., 2016), or Mutual Information (MI) (Maes et al., 1997).”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Berger et al., 2016
Cites
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868 · see citing works
Principles of Computerized Tomographic Imaging
Kak, A. C., & Slaney, M. (2001) · doi:10.1137/1.9780898719277 · Cited by 5,809 · see citing works
A universal image quality index
Wang, Z., & Bovik, A. C. (2002) · IEEE Signal Processing Letters · doi:10.1109/97.995823 · Cited by 5,757 · see citing works
A comparison of similarity measures for use in 2-D-3-D medical image registration
Penney, G. P., Weese, J., Little, J. A., Desmedt, P., Hill, D. L. G., & hawkes, D. J. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.730403 · Cited by 782 · see citing works
A Review of 3d/2d Registration Methods for Image-Guided Interventions
Markelj, P., Tomaževič, D., Likar, B., & Pernuš, F. (2012) · Medical Image Analysis · doi:10.1016/j.media.2010.03.005 · Cited by 746 · see citing works
Fast radial symmetry for detecting points of interest
Loy, G., & Zelinsky, A. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1217601 · Cited by 601 · see citing works
Optimal short scan convolution reconstruction for fan beam CT
Parker, D. L. (1982) · Medical Physics · doi:10.1118/1.595078 · Cited by 524 · see citing works
Pieper, S. D., Halle, M., & Kikinis, R. (2005) · doi:10.1109/isbi.2004.1398617 · Cited by 446 · see citing works
Pieper, S., Halle, M., & Kikinis, R. (2004) · International Symposium on Biomedical Imaging · Cited by 357 · see citing works
A physics-based coordinate transformation for 3-D image matching
Davis, M. H., Khotanzad, A. R., Flamig, D. P., & Harms, S. E. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.585766 · Cited by 267 · see citing works
Ohnesorge, B. M., Flohr, T. G., Schwarz, K., Heiken, J. P., & Bae, K. T. (2000) · Medical Physics · doi:10.1118/1.598855 · Cited by 249 · see citing works
Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT
Livyatan, H., Yaniv, Z. R., & Joskowicz, L. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819288 · Cited by 176 · see citing works
and, E. R., & Klingenbeck, K. (2011) · Medical Physics · doi:10.1118/1.3589140 · Cited by 160 · see citing works
Fast GPU-Based CT Reconstruction using the Common Unified Device Architecture (CUDA)
Scherl, H., Keck, B., Kowarschik, M., & Joachim Hornegger, A. (2007) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2007.4437102 · Cited by 138 · see citing works
Motion-compensated and gated cone beam filtered back-projection for 3-D rotational X-ray angiography
Schäfer, D., Borgert, J., Rasche, V., & Graß, M. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.876147 · Cited by 124 · see citing works
CONRAD—A software framework for cone‐beam imaging in radiology
Maier, A., Hofmann, H. G., Berger, M., Fischer, P., Schwemmer, C., Wu, H., Mueller, K., Joachim Hornegger, A., Choi, J., Rieß, C., et al. (2013) · Medical Physics · doi:10.1118/1.4824926 · Cited by 119 · see citing works
Self-citationOtake, Y., Wang, A. S., Webster Stayman, J., Uneri, A., Kleinszig, G., Vogt, S., Khanna, A. J., Gokaslan, Z. L., & Siewerdsen, J. H. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/23/8535 · Cited by 85 · see citing works
Tsai, T., Lu, T., Chen, C., Kuo, M., & Hsu, H. (2010) · Medical Physics · doi:10.1118/1.3301596 · Cited by 70 · see citing works
Epipolar Consistency in Transmission Imaging
Aichert, A., Berger, M., Wang, J., Maaß, N., Doerfler, A., Joachim Hornegger, A., & Maier, A. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2426417 · Cited by 69 · see citing works
Self-citationZhu, Z., Massimini, D. F., Wang, G., Warner, J. J. P., & Li, G. (2012) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2011.12.021 · Cited by 60 · see citing works
Choi, J., Maier, A., Keil, A., Pal, S., McWalter, E. J., Beaupré, G. S., Gold, G. E., & Fahrig, R. (2014) · Medical Physics · doi:10.1118/1.4873675 · Cited by 56 · see citing works
Self-citationHansis, E., Schäfer, D., Dössel, O., & Graß, M. (2008) · Physics in Medicine and Biology · doi:10.1088/0031-9155/53/14/007 · Cited by 54 · see citing works
Zhu, Z., & Li, G. (2011) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2011.597387 · Cited by 51 · see citing works
3D–2D registration in mobile radiographs: algorithm development and preliminary clinical evaluation
Otake, Y., Wang, A. S., Uneri, A., Kleinszig, G., Vogt, S., Aygun, N., Lo, S.-F. L., Wolinsky, J.-P., Gokaslan, Z. L., & Siewerdsen, J. H. (2015) · Physics in Medicine & Biology · doi:10.1088/0031-9155/60/5/2075 · Cited by 41 · see citing works
Choi, J., Fahrig, R., Keil, A., Besier, T. F., Pal, S., McWalter, E. J., Beaupré, G. S., & Maier, A. (2013) · Medical Physics · doi:10.1118/1.4817476 · Cited by 39 · see citing works
Self-citation2D/3D registration based on volume gradients
Wein, W., Roeper, B., & Navab, N. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.595466 · Cited by 30 · see citing works
Bifulco, P., Sansone, M., Cesarelli, M., Allen, R., & Bracale, M. (2002) · Medical Engineering & Physics · doi:10.1016/s1350-4533(02)00021-8 · Cited by 29 · see citing works
Analysis of vertical and horizontal circular C-arm trajectories
Maier, A., Choi, J., Keil, A., Niebler, C., Sarmiento, M., Fieselmann, A., Gold, G. E., Delp, S. L., & Fahrig, R. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.878502 · Cited by 27 · see citing works
Self-citationResidual motion compensation in ECG-gated interventional cardiac vasculature reconstruction
Schwemmer, C., Rohkohl, C., Lauritsch, G., Mueller, K., & Joachim Hornegger, A. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/11/3717 · Cited by 24 · see citing works
Data consistency conditions for truncated fanbeam and parallel projections
Clackdoyle, R., & Desbat, L. (2015) · Medical Physics · doi:10.1118/1.4905161 · Cited by 23 · see citing works
Lin, C., Zhang, S., Frahm, J., Lu, T., Hsu, C., & Shih, T. (2013) · Medical Physics · doi:10.1118/1.4820369 · Cited by 21 · see citing works
Cao, Q., Thawait, G. K., Gang, G. J., Zbijewski, W. B., Reigel, T., Brown, T. N., Corner, B. D., Demehri, S., & Siewerdsen, J. H. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/3/947 · Cited by 19 · see citing works
Mueller, K., Schwemmer, C., Joachim Hornegger, A., Zheng, Y., Wang, Y., Lauritsch, G., Rohkohl, C., Maier, A., Schultz, C. J., & Fahrig, R. (2013) · Medical Physics · doi:10.1118/1.4789593 · Cited by 14 · see citing works
Self-citationWang, J., Borsdorf, A., Heigl, B., Köhler, T., & Joachim Hornegger, A. (2014) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2014.45 · Cited by 13 · see citing works
Unberath, M., Choi, J., Berger, M., Maier, A., & Fahrig, R. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081559 · Cited by 12 · see citing works
Self-citationOuadah, S., Stayman, J. W., Gang, G. J., Uneri, A., Ehtiati, T., & Siewerdsen, J. H. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2082538 · Cited by 8 · see citing works
Mueller, K., Berger, M., Choi, J., Maier, A., & Fahrig, R. (2015) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-19387-8_15 · Cited by 6 · see citing works
Self-citationBerger, M., Mueller, K., Choi, J., Aichert, A., Maier, A., & Fahrig, R. (2015) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-19387-8_14 · Cited by 2 · see citing works
Self-citationBifulco, P., Cesarelli, M., Allen, R., Romano, M., Fratini, A., & Pasquariello, G. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2010/806094
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Unberath, M., Gao, C., Hu, Y., Judish, M., Taylor, R. H., Armand, M., & Grupp, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.716007 · Cited by 102 · see citing works
“1 using gradient-based or gradient-free methods, such as BFGS [22, 23] or CMA-ES [24] and BOBYQA [25], respectively.”
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Kyme, A. Z., & Fulton, R. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac093b · Cited by 92 · see citing works
Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion
Sisniega, A., Stayman, J. W., Yorkston, J., Siewerdsen, J. H., & Zbijewski, W. B. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa6869 · Cited by 74 · see citing works
Ibad, H. A., Netto, C. D. C., Shakoor, D., Sisniega, A., Liu, S. Z., Siewerdsen, J. H., Carrino, J. A., Zbijewski, W. B., & Demehri, S. (2022) · Investigative Radiology · doi:10.1097/rli.0000000000000908 · Cited by 53 · see citing works
Hajek, J., Unberath, M., Fotouhi, J., Bier, B., Lee, S. C., Osgood, G. M., Maier, A., Armand, M., & Navab, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_35 · Cited by 52 · see citing works
Self-citationRigid and non-rigid motion artifact reduction in X-ray CT using attention module
Ko, Y., Moon, S., Baek, J., & Shim, H. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101883 · Cited by 50 · see citing works
Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering
Gopalakrishnan, V., Dey, N., & Golland, P. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01108 · Cited by 48 · see citing works
“A 2D intraoperative X-ray can be registered to a 3D preoperative CT volume by maximizing its similarity to a rendered X-ray with respect to the camera pose [8, 14, 34].”
BackgroundCorrection of patient motion in cone-beam CT using 3D–2D registration
Ouadah, S., Jacobson, M. W., Stayman, J. W., Ehtiati, T., Weiss, C. R., & Siewerdsen, J. H. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa9254 · Cited by 39 · see citing works
Dynamic 2-D/3-D Rigid Registration Framework Using Point-To-Plane Correspondence Model
Wang, J., Schaffert, R., Borsdorf, A., Heigl, B., Huang, X., Joachim Hornegger, A., & Maier, A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2702100 · Cited by 38 · see citing works
Self-citationLearning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences
Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., & Maier, A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2988410 · Cited by 30 · see citing works
Self-citationMotion compensation for cone-beam CT using Fourier consistency conditions
Berger, M., Xia, Y., Aichinger, W., Mentl, K., Unberath, M., Aichert, A., Rieß, C., Joachim Hornegger, A., Fahrig, R., & Maier, A. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa8129 · Cited by 28 · see citing works
Self-citationBrinch, S., Wellenberg, R. H. H., Boesen, M., Maas, M. M., Johannsen, F. E., Nybing, J. U., Turmezei, T. D., Streekstra, G. J., & Hansen, P. (2022) · Skeletal Radiology · doi:10.1007/s00256-022-04223-1 · Cited by 27 · see citing works
Jacobson, M. W., Ketcha, M. D., Capostagno, S., Martin, A. B., Uneri, A., Goerres, J., Silva, T. D., Reaungamornrat, S., Han, R., Manbachi, A., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa9910 · Cited by 26 · see citing works
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Capostagno, S., Sisniega, A., Stayman, J. W., Ehtiati, T., Weiss, C. R., & Siewerdsen, J. H. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abb16e · Cited by 25 · see citing works
Reference-free learning-based similarity metric for motion compensation in cone-beam CT
Huang, H., Siewerdsen, J. H., Zbijewski, W. B., Weiss, C. R., Unberath, M., Ehtiati, T., & Sisniega, A. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac749a · Cited by 23 · see citing works
Self-citationDeformable respiratory motion correction for hepatic rotational angiography
Klugmann, A., Bier, B., Mueller, K., Maier, A., & Unberath, M. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.03.003 · Cited by 20 · see citing works
Self-citationImage-based motion compensation for high-resolution extremities cone-beam CT
Sisniega, A., Stayman, J. W., Cao, Q., Yorkston, J., Siewerdsen, J. H., & Zbijewski, W. B. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217243 · Cited by 19 · see citing works
Tucker, E. W., Fotouhi, J., Lee, S. C., Unberath, M., Fuerst, B., Johnson, A., Armand, M., Osgood, G. M., & Navab, N. (2018) · Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2293675 · Cited by 19 · see citing works
Self-citationMotion compensation in extremity cone-beam computed tomography
Sisniega, A., Thawait, G. K., Shakoor, D., Siewerdsen, J. H., Demehri, S., & Zbijewski, W. B. (2019) · Skeletal Radiology · doi:10.1007/s00256-019-03241-w · Cited by 19 · see citing works
Co-localized augmented human and X-ray observers in collaborative surgical ecosystem
Fotouhi, J., Unberath, M., Song, T., Hajek, J., Lee, S. C., Bier, B., Maier, A., Osgood, G. M., Armand, M., & Navab, N. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02035-8 · Cited by 19 · see citing works
Self-citationHead motion correction based on filtered backprojection for x‐ray CT imaging
Jang, S., Kim, S., Kim, M., & Ra, J. B. (2017) · Medical Physics · doi:10.1002/mp.12705 · Cited by 18 · see citing works
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Bier, B., Aschoff, K., Syben, C., Unberath, M., Levenston, M., Gold, G., Fahrig, R., & Maier, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00129-2_10 · Cited by 16 · see citing works
Self-citationRadiological Insights into Sacroiliitis: A Narrative Review
Al-Mnayyis, A., Obeidat, S., Badr, A., Jouryyeh, B., Azzam, S., Bibi, H. A., Al-Gwairy, Y., Sharie, S. A., & Varrassi, G. (2024) · Clinics and Practice · doi:10.3390/clinpract14010009 · Cited by 16 · see citing works
Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients
Gu, W., Gao, C., Grupp, R., Fotouhi, J., & Unberath, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59861-7_29 · Cited by 16 · see citing works
Self-citationNardi, C., Buzzi, R., Molteni, R., Cossi, C., Lorini, C., Calistri, L., & Colagrande, S. (2017) · British Journal of Radiology · doi:10.1259/bjr.20160925 · Cited by 15 · see citing works
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Bier, B., Ravikumar, N., Unberath, M., Levenston, M. E., Gold, G. E., Fahrig, R., & Maier, A. (2018) · Journal of Imaging · doi:10.3390/jimaging4010013 · Cited by 13 · see citing works
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Schäfer, S., & Siewerdsen, J. H. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816176-0.00031-4 · Cited by 13 · see citing works
Li, G., Xue, C., You, C., Huang, X., Deng, Z., & Luo, S. (2023) · Medical Physics · doi:10.1002/mp.16257 · Cited by 12 · see citing works
Appearance Learning for Image-Based Motion Estimation in Tomography
Preuhs, A., Manhart, M., Roser, P., Hoppe, E., Huang, Y., Psychogios, M., Kowarschik, M., & Maier, A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3002695 · Cited by 12 · see citing works
Self-citationTaubmann, O., Berger, M., Bögel, M., Xia, Y., Balda, M., & Maier, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-96520-8_8 · Cited by 12 · see citing works
Self-citationGradient-based geometry learning for fan-beam CT reconstruction
Thies, M., Wagner, F., Maul, N., Folle, L., Meier, M., Rohleder, M., Schneider, L.-S., Pfaff, L., Gu, M., Utz, J., et al. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acf90e · Cited by 12 · see citing works
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Preuhs, A., Maier, A., Manhart, M. T., Kowarschik, M., Hoppe, E., Fotouhi, J., Navab, N., & Unberath, M. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02027-8 · Cited by 12 · see citing works
Self-citationMaier, J., Nitschke, M., Choi, J., Gold, G. E., Fahrig, R., Eskofier, B. M., & Maier, A. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3123673 · Cited by 10 · see citing works
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Hall, M. E., Black, M. S., Gold, G. E., & Levenston, M. E. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-1062 · Cited by 5 · see citing works
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Data-driven motion compensation in HR-pQCT
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- Authors
- Berger, M., Müller, K., Aichert, A., Unberath, M., Thies, J., Choi, J. ‐., Fahrig, R., & Maier, A.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Stanford University (US)
- Corresponding author
- Berger, M.
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2016-02-10
- Volume
- 43
- Issue
- 3
- Pages
- 1235-1248
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- PMC
- PMC4752553
- OpenAlex
- W2291673995
- Cited by
- 72
- References
- 39
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 7 · 2023: 3 · 2022: 6 · 2021: 7 · 2020: 7 · 2019: 7 · 2018: 10 · 2017: 11 · 2016: 1
- FWCI
- 6.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Medical Physics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (S10RR026714)
- Deutsche Forschungsgemeinschaft (Graduate School GRK‐1773, SAOT, Graduate School GRK-1773)
- Siemens (AX)
- National Institutes of Health (NIH) (S10RR026714)
- Deutsche Forschungsgemeinschaft (DFG) (Graduate School GRK-1773, SAOT)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Advanced X-ray and CT Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- knee joint, 2D-3D registration, motion-compensated reconstruction, thin-plate splines, gradient correlation, squatting position, streak artifacts, non-rigid deformation, back-projection
- MeSH terms
- Humans, Knee Joint, Movement, Weight-Bearing, Artifacts, Imaging, Three-Dimensional, Cone-Beam Computed Tomography
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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