{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T02:51:05Z","timestamp":1771210265470,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["RGPIN-2023-04408"],"award-info":[{"award-number":["RGPIN-2023-04408"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["30729"],"award-info":[{"award-number":["30729"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canadian Foundation for Innovation","award":["RGPIN-2023-04408"],"award-info":[{"award-number":["RGPIN-2023-04408"]}]},{"name":"Canadian Foundation for Innovation","award":["30729"],"award-info":[{"award-number":["30729"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moir\u00e9-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of &lt;1 mm and &lt;1\u00b0. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.<\/jats:p>","DOI":"10.3390\/s24123737","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T08:59:06Z","timestamp":1718009946000},"page":"3737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7727-7626","authenticated-orcid":false,"given":"Marina","family":"Silic","sequence":"first","affiliation":[{"name":"Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada"},{"name":"Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fred","family":"Tam","sequence":"additional","affiliation":[{"name":"Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon J.","family":"Graham","sequence":"additional","affiliation":[{"name":"Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada"},{"name":"Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.neuroimage.2016.11.014","article-title":"Prospective motion correction in functional MRI","volume":"154","author":"Zaitsev","year":"2017","journal-title":"NeuroImage"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"R32","DOI":"10.1088\/0031-9155\/61\/5\/R32","article-title":"Motion correction in MRI of the brain","volume":"61","author":"Godenschweger","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1002\/jmri.24850","article-title":"Motion artifacts in MRI: A complex problem with many partial solutions","volume":"42","author":"Zaitsev","year":"2015","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"462471","DOI":"10.3389\/fnins.2019.00825","article-title":"Resting state fMRI: Going through the motions","volume":"13","author":"Maknojia","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1002\/mrm.1910180121","article-title":"Adaptive motion compensation in MRI: Accuracy of motion measurement","volume":"18","author":"Felmlee","year":"1991","journal-title":"Magn. Reson. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maclaren, J., Armstrong, B.S.R., Barrows, R.T., Danishad, K.A., Ernst, T., Foster, C.L., Gumus, K., Herbst, M., Kadashevich, I.Y., and Kusik, T.P. (2012). Measurement and Correction of Microscopic Head Motion during Magnetic Resonance Imaging of the Brain. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0048088"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1002\/mrm.22076","article-title":"Prospective Head Movement Correction for High Resolution MRI using an In-bore Optical Tracking System","volume":"62","author":"Qin","year":"2009","journal-title":"Magn. Reson. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.media.2011.05.018","article-title":"Self-Encoded Marker for Optical Prospective Head Motion Correction in MRI","volume":"15","author":"Forman","year":"2011","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1002\/mrm.26838","article-title":"Prospective motion correction using coil-mounted cameras: Cross-calibration considerations","volume":"79","author":"Maclaren","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1109\/TMI.2018.2866442","article-title":"Real-Time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration","volume":"38","author":"Salehi","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1002\/mrm.27771","article-title":"Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model","volume":"82","author":"Haskell","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"117756","DOI":"10.1016\/j.neuroimage.2021.117756","article-title":"Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions","volume":"230","author":"Duffy","year":"2021","journal-title":"NeuroImage"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1002\/mrm.27772","article-title":"Conditional generative adversarial network for 3D rigid-body motion correction in MRI","volume":"82","author":"Johnson","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4786","DOI":"10.1038\/s41598-020-61705-9","article-title":"Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network","volume":"10","author":"Usman","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1002\/mrm.27783","article-title":"Retrospective correction of motion-affected MR images using deep learning frameworks","volume":"82","author":"Armanious","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s42979-023-01796-z","article-title":"Deep Learning for Head Pose Estimation: A Survey","volume":"4","author":"Asperti","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108591","DOI":"10.1016\/j.patcog.2022.108591","article-title":"Head pose estimation: An extensive survey on recent techniques and applications","volume":"127","author":"Abate","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1002\/mrm.22191","article-title":"Navigator Accuracy Requirements for Prospective Motion Correction","volume":"63","author":"Maclaren","year":"2010","journal-title":"Magn. Reson. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Meza, J., Romero, L.A., and Marrugo, A.G. (2021, January 19\u201325). MarkerPose: Robust Real-Time Planar Target Tracking for Accurate Stereo Pose Estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00141"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yu, C., Cai, Z., Pham, H., and Pham, Q.-C. (2019, January 3\u20138). Siamese Convolutional Neural Network for Sub-millimeter-accurate Camera Pose Estimation and Visual Servoing. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967925"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103847","DOI":"10.1016\/j.robot.2021.103847","article-title":"Uncalibrated stereo vision with deep learning for 6-DOF pose estimation for a robot arm system","volume":"145","author":"Abdelaal","year":"2021","journal-title":"Robot. Auton. Syst."},{"key":"ref_22","unstructured":"Watec Co., Ltd. (2022, October 17). WAT-204CX Coaxial Transmission Camera Specifications. Available online: http:\/\/www.watec.com.tw\/download\/WAT-204CX.pdf."},{"key":"ref_23","unstructured":"Bailey, L. (2024, May 01). OBS Studio. Available online: https:\/\/github.com\/obsproject\/obs-studio."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tanaka, H., Sumi, Y., and Matsumoto, Y. (2014, January 14\u201318). A solution to pose ambiguity of visual markers using Moir\u00e9 patterns. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6942995"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.imavis.2018.05.004","article-title":"Speeded up detection of squared fiducial markers","volume":"76","year":"2018","journal-title":"Image Vis. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.patcog.2015.09.023","article-title":"Generation of fiducial marker dictionaries using Mixed Integer Linear Programming","volume":"51","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_29","first-page":"120","article-title":"The OpenCV library","volume":"25","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"170032","DOI":"10.1038\/sdata.2017.32","article-title":"T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 \u03bcm","volume":"4","author":"Sciarra","year":"2017","journal-title":"Sci. Data"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Stucht, D., Danishad, K.A., Schulze, P., Godenschweger, F., Zaitsev, M., and Speck, O. (2015). Highest Resolution In Vivo Human Brain MRI Using Prospective Motion Correction. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0133921"},{"key":"ref_34","unstructured":"Armstrong, B.S.R., Verron, T., Karonde, R.M., Reynolds, J., and Schmidt, K. (2007). RGR-6D: Low-Cost, High-Accuracy Measurement of 6-DOF Pose from a Single Image, University of Wisconsin."},{"key":"ref_35","unstructured":"Tournier, G.P. (2006). Six Degrees of Freedom Estimation Using Monocular Vision and Moir\u00e9 Patterns. [Master\u2019s Thesis, Massachusetts Institute of Technology]. Available online: https:\/\/dspace.mit.edu\/handle\/1721.1\/37951."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1002\/mrm.27705","article-title":"Markerless high-frequency prospective motion correction for neuroanatomical MRI","volume":"82","author":"Frost","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.14358\/PERS.72.9.1017","article-title":"Zoom-Dependent Camera Calibration in Digital Close-Range Photogrammetry","volume":"72","author":"Fraser","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1111\/j.1477-9730.2011.00648.x","article-title":"Calibration of long focal length cameras in close range photogrammetry","volume":"26","author":"Stamatopoulos","year":"2011","journal-title":"Photogramm. Rec."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1109\/34.88573","article-title":"Least-squares estimation of transformation parameters between two point patterns","volume":"13","author":"Umeyama","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","unstructured":"MRC Systems GmbH (2022, October 17). MR-Compatible Camera \u201812M-i newSensor\u2019 with Integrated LED Light. Available online: https:\/\/www.mrc-systems.de\/downloads\/en\/mri-compatible-cameras\/manual_mrcam_12m-i.pdf."},{"key":"ref_41","unstructured":"Blender Online Community (2018). Blender\u2014A 3D Modelling and Rendering Package, Stichting Blender Foundation. Available online: http:\/\/www.blender.org."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s11263-019-01260-7","article-title":"Statistical Modeling of Craniofacial Shape and Texture","volume":"128","author":"Dai","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1006\/nimg.2001.0829","article-title":"Quantifying Head Motion Associated with Motor Tasks Used in fMRI","volume":"14","author":"Seto","year":"2001","journal-title":"NeuroImage"},{"key":"ref_44","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv."},{"key":"ref_45","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1002\/mrm.23101","article-title":"Hybrid prospective and retrospective head motion correction to mitigate cross-calibration errors","volume":"67","author":"Aksoy","year":"2012","journal-title":"Magn. Reson. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.neuroimage.2006.01.039","article-title":"Magnetic resonance imaging of freely moving objects: Prospective real-time motion correction using an external optical motion tracking system","volume":"31","author":"Zaitsev","year":"2006","journal-title":"NeuroImage"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1002\/mrm.29705","article-title":"Robust retrospective motion correction of head motion using navigator-based and markerless motion tracking techniques","volume":"90","author":"Marchetto","year":"2023","journal-title":"Magn. Reson. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3737\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:55:57Z","timestamp":1760108157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3737"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,8]]},"references-count":48,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123737"],"URL":"https:\/\/doi.org\/10.3390\/s24123737","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,8]]}}}