{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T09:35:41Z","timestamp":1792920941723,"version":"4.5.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T00:00:00Z","timestamp":1792713600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T00:00:00Z","timestamp":1792713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02431-y","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T15:45:00Z","timestamp":1792770300000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cardiac Image Segmentation Under Respiratory Motion Artifacts Using Different Modified U-Net Models"],"prefix":"10.1007","author":[{"given":"Ravindra Prathap","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Nagabhooshanam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yogendra","family":"Thakur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhilasha","family":"Jadhav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. Ramesh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G. Vijaya","family":"Lakshmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Rajaram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1623-8389","authenticated-orcid":false,"given":"Hassan Ali","family":"Almubarak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"issue":"5","key":"2431_CR1","doi-asserted-by":"publisher","first-page":"2403","DOI":"10.1002\/mrm.28507","volume":"85","author":"M Henningsson","year":"2021","unstructured":"Henningsson, M., Grigoratos, C., Dell\u2019Omodarme, M., et al. (2021). Pilot tone\u2013based motion correction for prospective respiratory compensated cardiac cine MRI. Magnetic Resonance in Medicine, 85(5), 2403\u20112416. https:\/\/doi.org\/10.1002\/mrm.28507","journal-title":"Magnetic Resonance in Medicine"},{"key":"2431_CR2","doi-asserted-by":"publisher","unstructured":"Patel, A., Knight, D., Montalt-Tordera, J., Arridge, S., jaubert, O., & Schaeffter, T. (2022). Real-time pilot-tone guided motion correction in free-breathing cardiac MRI. Magnetic Resonance in Medicine, 88(2), 389\u2013401. https:\/\/doi.org\/10.1002\/mrm.29260","DOI":"10.1002\/mrm.29260"},{"key":"2431_CR3","doi-asserted-by":"publisher","unstructured":"Amirrajab, S., Al Khalil, Y., Pluim, J., Breeuwer, M., & Scannell, C. M. (2023). Cardiac MR image segmentation and quality control in the presence of respiratory motion artifacts using simulated data. In O. Camara et al. (Eds.), Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers (LNCS, Vol. 13593, pp.\u202f466\u2013475). Springer. https:\/\/doi.org\/10.1007\/978-3-031-23443-9_44","DOI":"10.1007\/978-3-031-23443-9_44"},{"key":"2431_CR4","doi-asserted-by":"publisher","unstructured":"Garcia-Cabrera, C., Arazo, E., Curran, K. M., O\u2019Connor, N. E., & McGuinness, K. (2022). Cardiac segmentation using transfer learning under respiratory motion artifacts. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2209.09714","DOI":"10.48550\/arXiv.2209.09714"},{"key":"2431_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13908","volume":"24","author":"B Li","year":"2023","unstructured":"Li,\u202fB., Yang,\u202fT., & Zhao,\u202fX. (2023). NVTrans-UNet: Neighborhood vision transformer-based U-Net for multi-modal cardiac MRI segmentation. Journal of Applied Clinical Medical Physics, 24, e13908. https:\/\/doi.org\/10.1002\/acm2.13908","journal-title":"Journal of Applied Clinical Medical Physics"},{"key":"2431_CR6","doi-asserted-by":"publisher","unstructured":"He, Z., Smith, J., Wang, L., Kumar, A., Lee, S., Patel, R., \u2026 Johnson, M. (2022). Transformer-U-Net hybrid for rapid cardiac MRI segmentation under motion. IEEE Journal of Biomedical and Health Informatics, 26(10), 4478\u20134489. https:\/\/doi.org\/10.1109\/JBHI.2022.3167894","DOI":"10.1109\/JBHI.2022.3167894"},{"key":"2431_CR7","doi-asserted-by":"publisher","unstructured":"Grzeszczyk, M. K., P\u0142otka, S., & Sitek, A. (2022). Multi\u2011task Swin Transformer for motion artifacts classification and cardiac MRI segmentation. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2209.02470","DOI":"10.48550\/arXiv.2209.02470"},{"issue":"5","key":"2431_CR8","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.1002\/mp.16042","volume":"51","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Liu, X., Wang, H., Zhao, J., Chen, M., & Li, Q. (2024). Temporally guided U\u2011Net for motion\u2011robust cardiac cine segmentation. Medical Physics, 51(5), 2103\u20132115. https:\/\/doi.org\/10.1002\/mp.16042","journal-title":"Medical Physics"},{"key":"2431_CR9","doi-asserted-by":"crossref","unstructured":"Dong, S., Zhao, J., Zhang, M., Shi, Z., Deng, J., Shi, Y., Tian, M., & Zhuo, C. (2022). DeU\u2011Net 2.0: Enhanced deformable U\u2011Net for 3D cardiac cine MRI segmentation. Biomedical Signal Processing and Control, 81, 103874.","DOI":"10.1016\/j.media.2022.102389"},{"key":"2431_CR10","doi-asserted-by":"publisher","unstructured":"DeSPPNet team. (2025). DeSPPNet: A multiscale deep learning model for cardiac MRI segmentation. bioRxiv. https:\/\/doi.org\/10.1101\/2025.01.06.116746","DOI":"10.1101\/2025.01.06.116746"},{"issue":"2","key":"2431_CR11","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1002\/mrm.29345","volume":"88","author":"O Jaubert","year":"2022","unstructured":"Jaubert, O., Montalt-Tordera, J., Brown, J., Knight, D., Arridge, S., Steeden, J. A., & Muthurangu, V. (2022). Real-time spiral PCMR reconstruction and segmentation with deep learning. Magnetic Resonance in Medicine, 88(2), 456\u2013467. https:\/\/doi.org\/10.1002\/mrm.29345","journal-title":"Magnetic Resonance in Medicine"},{"key":"2431_CR12","doi-asserted-by":"publisher","unstructured":"Jaubert, O., Montalt-Tordera, J., Brown, J., Knight, D., Arridge, S., Steeden, J., & Muthurangu, V. (2022). FReSCO: Flow Reconstruction and Segmentation for low-latency cardiac output monitoring using deep artifact suppression and segmentation [Preprint]. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2203.13729","DOI":"10.48550\/arXiv.2203.13729"},{"issue":"5","key":"2431_CR13","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1002\/mrm.29374","volume":"88","author":"O Jaubert","year":"2022","unstructured":"Jaubert, O., Montalt-Tordera, J., Brown, J., Knight, D., Arridge, S., Steeden, J. A., & Muthurangu, V. (2022). Low-latency deep learning framework for cardiac output monitoring. Magnetic Resonance in Medicine, 88(5), 2179\u20132189. https:\/\/doi.org\/10.1002\/mrm.29374","journal-title":"Magnetic Resonance in Medicine"},{"key":"2431_CR14","doi-asserted-by":"publisher","unstructured":"Arxiv 3D U\u2011Net improved for cardiac segmentation. (2024). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2410.06818","DOI":"10.48550\/arXiv.2410.06818"},{"key":"2431_CR15","doi-asserted-by":"publisher","unstructured":"Deep learning automated LV segmentation and flow quantification. (2023). Journal of Cardiovascular Magnetic Resonance, 25, 68. https:\/\/doi.org\/10.1186\/s12968\u2011023\u201100944\u2011x","DOI":"10.1186\/s12968\u2011023\u201100944\u2011x"},{"key":"2431_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107676","volume":"167","author":"W Chen","year":"2024","unstructured":"Chen, W., et al. (2024). Joint image reconstruction and segmentation of real\u2011time cardiac MRI without gating. Computers in Biology and Medicine, 167, 107676. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.107676","journal-title":"Computers in Biology and Medicine"},{"issue":"4","key":"2431_CR17","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1002\/mrm.28834","volume":"86","author":"O Jaubert","year":"2021","unstructured":"Jaubert, O., Montalt-Tordera, J., Knight, D., Coghlan, G. J., Arridge, S., Steeden, J. A., & Muthurangu, V. (2021). Real-time deep artifact suppression using recurrent U-Nets for low-latency cardiac MRI. Magnetic Resonance in Medicine, 86(4), 1904\u20131916. https:\/\/doi.org\/10.1002\/mrm.28834","journal-title":"Magnetic Resonance in Medicine"},{"issue":"1","key":"2431_CR18","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s10334-023-01020-5","volume":"36","author":"J Hughes","year":"2023","unstructured":"Hughes, J., Jaubert, O., & Delingette, H. (2023). Real-time deep artifact suppression in cardiac cine imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 36(1), 41\u201356. https:\/\/doi.org\/10.1007\/s10334\u2011023\u201101020\u20115","journal-title":"Magnetic Resonance Materials in Physics, Biology and Medicine"},{"issue":"3","key":"2431_CR19","doi-asserted-by":"publisher","first-page":"227","DOI":"10.3390\/bioengineering11030227","volume":"11","author":"S-H Kang","year":"2024","unstructured":"Kang, S.-H., & Lee, Y. (2024). Motion artifact reduction using U\u2011Net model with three\u2011dimensional simulation\u2011based datasets for brain magnetic resonance images. Bioengineering, 11(3), 227. https:\/\/doi.org\/10.3390\/bioengineering11030227","journal-title":"Bioengineering"},{"key":"2431_CR20","doi-asserted-by":"publisher","unstructured":"Kou, Y., Ge, R., & Zhang, D. (2023). 3D MRI cardiac segmentation under respiratory motion artifacts. In O. Camara et al. (Eds.), Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers (LNCS, Vol. 13593, pp.\u202f457\u2013465). Springer. https:\/\/doi.org\/10.1007\/978-3-031-23443-9_43","DOI":"10.1007\/978-3-031-23443-9_43"},{"key":"2431_CR21","doi-asserted-by":"crossref","unstructured":"Li, H., et al. (2023). NVTrans\u2011UNet: Neighborhood vision transformer-based U\u2011Net for multi\u2011modal cardiac MR image segmentation. Journal of Applied Clinical Medical Physics.","DOI":"10.1002\/acm2.13908"},{"key":"2431_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-23443-9_37","author":"A Mora Rubio","year":"2023","unstructured":"Mora Rubio, A., Noga, M. L., & Punithakumar, K. (2023). Deep learning-based classification and segmentation for cardiac magnetic resonance imaging with respiratory motion artifacts. In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers (LNCS). https:\/\/doi.org\/10.1007\/978\u20113\u2011031\u201123443\u20119_37","journal-title":"In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers (LNCS)."},{"key":"2431_CR23","doi-asserted-by":"publisher","first-page":"54164","DOI":"10.1038\/s41598-024-54164-z","volume":"14","author":"Nature Study Team","year":"2024","unstructured":"Nature Study Team. (2024). Assessment of deep learning segmentation for real\u2011time free\u2011breathing cardiac MRI. Scientific Reports, 14, 54164. https:\/\/doi.org\/10.1038\/s41598\u2011024\u201154164\u2011z","journal-title":"Scientific Reports"},{"issue":"12","key":"2431_CR24","doi-asserted-by":"publisher","first-page":"4001","DOI":"10.1109\/TMI.2020.3008930","volume":"39","author":"I Oksuz","year":"2020","unstructured":"Oksuz, I., Clough, J. R., Ruijsink, B., Puyol Anton, E., Bustin, A., & Cruz, G. (2020). Deep learning\u2013based detection and correction of cardiac MR motion artefacts during reconstruction for high\u2011quality segmentation. IEEE Transactions on Medical Imaging, 39(12), 4001\u20134010.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2431_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.102608","volume":"90","author":"Segmentation-aware MRI subsampling for efficient cardiac MRI","year":"2024","unstructured":"Segmentation\u2011aware MRI subsampling for efficient cardiac MRI. (2024). Medical Image Analysis, 90, 102608. https:\/\/doi.org\/10.1016\/j.media.2024.102608","journal-title":"Medical Image Analysis"},{"key":"2431_CR26","doi-asserted-by":"publisher","unstructured":"Sun, Y., Gao, K., Lin, W., Li, G., Niu, S., & Wang, L. (2021). Multi\u2011scale self\u2011supervised learning for pediatric brain MR image segmentation with motion\/Gibbs artifacts. In Machine Learning in Medical Imaging, 171\u2013179. Springer. https:\/\/doi.org\/10.1007\/978\u20113\u2011030\u201189212\u20118_18","DOI":"10.1007\/978\u20113\u2011030\u201189212\u20118_18"},{"key":"2431_CR27","doi-asserted-by":"publisher","unstructured":"Swin Transformer and U-Net architecture to correct motion artifacts in MRI. (2023). Journal of Imaging, 10, 897298. https:\/\/doi.org\/10.1155\/2024\/8972980","DOI":"10.1155\/2024\/8972980"},{"key":"2431_CR28","doi-asserted-by":"publisher","unstructured":"Systematic review of DL in cardiac CT segmentation. (2024). Frontiers in Cardiovascular Medicine, 11, 132346. https:\/\/doi.org\/10.3389\/fcvm.2024.1323461","DOI":"10.3389\/fcvm.2024.1323461"},{"key":"2431_CR29","doi-asserted-by":"publisher","unstructured":"Wang, S., Qin, C., Wang, C., Wang, K., Wang, H., Chen, C., Ouyang, C., Kuang, X., Dai, C., Mo, Y., Shi, Z., Dai, C., Chen, X., Wang, H., & Bai, W. (2022). The extreme cardiac MRI analysis challenge under respiratory motion (CMRxMotion). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2210.06385","DOI":"10.48550\/arXiv.2210.06385"},{"issue":"3","key":"2431_CR30","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TMI.2022.3214567","volume":"42","author":"L Zhao","year":"2023","unstructured":"Zhao, L., & Liu, X. (2023). GAN\u2011based motion artifact correction for cardiac MRI. IEEE Transactions on Medical Imaging, 42(3), 574\u2011583. https:\/\/doi.org\/10.1109\/TMI.2022.3214567","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"2431_CR31","first-page":"111","volume":"14","author":"A Rajaram","year":"2024","unstructured":"A. Rajaram, K. Padmavathi, A. Karthik, and K. Sivasankari, \"Enhancing energy forecasting in combined cycle power plants using a hybrid ConvLSTM and FC neural network model,\" Int. J. Renew. Energy Res. (IJRER), vol. 14, no. 1, pp. 111\u2013126, 2024.","journal-title":"Int. J. Renew. Energy Res. (IJRER)"},{"key":"2431_CR32","doi-asserted-by":"crossref","unstructured":"M. G. Asuti, S. Ramesh, D. Prabakar, G. Balachandran, T. Anuja, A. Rajaram, and H. K. Jabbar, \"Innovative approaches to remote health monitoring systems for enhanced patient care and accessibility,\" in Next-Generation Therapeutics Using Internet of Things and Machine Learning, IGI Global Scientific Publishing, pp. 111\u2013126, 2025.","DOI":"10.4018\/979-8-3373-1022-0.ch006"},{"issue":"27","key":"2431_CR33","doi-asserted-by":"publisher","first-page":"31919","DOI":"10.1007\/s11042-024-20420-7","volume":"84","author":"KN Raju","year":"2025","unstructured":"K. N. Raju, V. Sudha, P. R. Kshirsagar, V. Tirth, and A. Rajaram, \"Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities,\" Multimed. Tools Appl., vol. 84, no. 27, pp. 31919\u201331937, 2025.","journal-title":"Multimed. Tools Appl."},{"key":"2431_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106872","volume":"100","author":"A Karthik","year":"2025","unstructured":"A. Karthik, S. K. Sahoo, A. Kumar, N. Patel, P. Chinnaraj, L. P. Maguluri, and A. Rajaram, \"Unified approach for accurate brain tumor multi-classification and segmentation through fusion of advanced methodologies,\" Biomed. Signal Process. Control, vol. 100, p. 106872, 2025.","journal-title":"Biomed. Signal Process. Control"},{"key":"2431_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106286","volume":"95","author":"A Karthik","year":"2024","unstructured":"A. Karthik, S. S. Aalam, M. Sivakumar, M. R. Sundari, J. D. Rose, M. Elangovan, and A. Rajaram, \"Improving brain tumor treatment with better imaging and real-time therapy using quantum dots,\" Biomed. Signal Process. Control, vol. 95, p. 106286, 2024.","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02431-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02431-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02431-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T09:01:57Z","timestamp":1792918917000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02431-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,23]]},"references-count":35,"alternative-id":["2431"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02431-y","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,23]]},"assertion":[{"value":"26 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"No participation of humans takes place in this implementation process.","order":1,"name":"Ethics","label":"Ethics Approval and Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No violation of Human and Animal Rights is involved.","order":2,"name":"Ethics","label":"Human and Animal Rights","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Conflict of Interest is not applicable in this work.","order":3,"name":"Ethics","label":"Conflict of Interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}