{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:54:38Z","timestamp":1760230478767,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61772101"],"award-info":[{"award-number":["61772101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled \u2018black-box\u2019 by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity.<\/jats:p>","DOI":"10.3390\/s22155666","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T22:43:26Z","timestamp":1659048206000},"page":"5666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images"],"prefix":"10.3390","volume":"22","author":[{"given":"Yiwen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Tao","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China"},{"name":"Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science, Neusoft Institute Guangdong, Foshan 528225, China"}]},{"given":"Zhenyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China"}]},{"given":"Xiaoying","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China"}]},{"given":"Xuan","family":"He","sequence":"additional","affiliation":[{"name":"College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4367-6301","authenticated-orcid":false,"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Zhenning","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.2217\/iim.12.13","article-title":"CT artifacts: Causes and reduction techniques","volume":"4","author":"Boas","year":"2012","journal-title":"Imaging Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","article-title":"Computer-aided diagnosis in medical imaging: Historical review, current status and future potential","volume":"31","author":"Doi","year":"2007","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1111\/iej.13326","article-title":"Impact of motion artefacts and motion-artefact correction on diagnostic accuracy of apical periodontitis in CBCT images: Images: An ex vivo study in human cadavers","volume":"53","author":"Kruse","year":"2020","journal-title":"Int. Endod. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.phro.2019.05.001","article-title":"Automatic recognition and analysis of metal streak artifacts in head and neck computed tomography for radiomics modeling","volume":"10","author":"Wei","year":"2019","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_5","unstructured":"Yang, X., and Li, C. (September, January 29). Secure XML publishing without information leakage in the presence of data inference. Proceedings of the 30th VLDB Conference, Toronto, ON, Canada."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, B., and Li, C. (2008, January 9\u201312). Cost-based variable-length-gram selection for string collections to support approximate queries efficiently. Proceedings of the ACM SIGMOD International Conference on Management of Data, Vancouver, BC, Canada.","DOI":"10.1145\/1376616.1376655"},{"key":"ref_7","first-page":"1","article-title":"Graph representation learning","volume":"14","author":"Hamilton","year":"2020","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"ref_8","first-page":"84","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.neuroimage.2018.05.077","article-title":"A supervised learning approach for diffusion MRI quality control with minimal training data","volume":"178","author":"Graham","year":"2018","journal-title":"Neuroimage"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"101907","DOI":"10.1016\/j.compmedimag.2021.101907","article-title":"Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow","volume":"90","author":"Hernandez","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","article-title":"Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment","volume":"146","author":"Kawahara","year":"2017","journal-title":"NeuroImage."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"035017","DOI":"10.1088\/1361-6560\/ab63ba","article-title":"External validation and transfer learning of convolutional neural networks for computed tomography dental artifact classification","volume":"65","author":"Welch","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1103\/RevModPhys.74.47","article-title":"Statistical mechanics of complex networks","volume":"74","author":"Albert","year":"2002","journal-title":"Rev. Mod. Phys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Albert","year":"1999","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective dynamics of \u2018small-world\u2019 networks","volume":"393","author":"Watts","year":"1998","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1080\/00018732.2011.572452","article-title":"Analyzing and modeling real-world phenomena with complex networks: A survey of applications","volume":"60","author":"Costa","year":"2011","journal-title":"Adv. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.physa.2004.11.040","article-title":"The scale-free topology of market investments","volume":"350","author":"Garlaschelli","year":"2005","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.jbiotec.2010.09.902","article-title":"UNICELLSYS\u2014Understanding the cell\u2019s functional organization","volume":"150","author":"Hohmann","year":"2010","journal-title":"J. Biotechnol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1126\/science.1165821","article-title":"Network analysis in the social sciences","volume":"323","author":"Borgatti","year":"2009","journal-title":"Science"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.physa.2015.08.062","article-title":"Feature of topological properties in an earthquake network","volume":"442","author":"Min","year":"2016","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1140\/epjb\/e2007-00259-3","article-title":"Dynamical evolution of clustering in complex network of earthquakes","volume":"59","author":"Abe","year":"2007","journal-title":"Eur. Phys. J. B"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"125468","DOI":"10.1016\/j.physa.2020.125468","article-title":"Statistical analysis of complex weighted network for seismicity","volume":"563","author":"He","year":"2020","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6691880","DOI":"10.1155\/2021\/6691880","article-title":"Comparison and analysis of network construction methods for seismicity based on complex networks","volume":"2021","author":"He","year":"2021","journal-title":"Complexity"},{"key":"ref_26","unstructured":"Locatello, F., Bauer, S., Lucic, M., Gelly, S., Schlkopf, B., and Bachem, O. (2019, January 9\u201315). Challenging common assumptions in the unsupervised learning of disentangled representations. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.compmedimag.2018.09.002","article-title":"Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis","volume":"69","author":"Ger","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Stoeve, M., Aubreville, M., Oetter, N., Knipfer, C., Neumann, H., Stelzle, F., and Maier, A. (2018). Motion artifact detection in confocal laser endomicroscopy images. Bildverarbeitung f\u00fcr Die Medizin 2018, Springer.","DOI":"10.1007\/978-3-662-56537-7_85"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"190003","DOI":"10.1038\/sdata.2019.3","article-title":"Computed tomography data collection of the complete human mandible and valid clinical ground truth models","volume":"6","author":"Wallner","year":"2019","journal-title":"Sci. Data"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1148\/rg.246045065","article-title":"Artifacts in CT: Recognition and avoidance","volume":"24","author":"Barrett","year":"2004","journal-title":"RadioGraphics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1093\/schbul\/sby039","article-title":"Data-driven analysis of functional connectivity reveals a potential auditory verbal hallucination network","volume":"45","author":"Scheinost","year":"2019","journal-title":"Schizophr. Bull."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s00234-015-1537-1","article-title":"Analysis of metal artifact reduction tools for dental hardware in CT scans of the oral cavity: kVp, iterative reconstruction, dual-energy CT, metal artifact reduction software: Does it make a difference?","volume":"57","author":"Casselman","year":"2015","journal-title":"Neuroradiology"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5666\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:58:35Z","timestamp":1760140715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5666"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,28]]},"references-count":32,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155666"],"URL":"https:\/\/doi.org\/10.3390\/s22155666","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,7,28]]}}}