{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T06:14:35Z","timestamp":1747808075501,"version":"3.37.3"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T00:00:00Z","timestamp":1615680000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T00:00:00Z","timestamp":1615680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Electromagnetic tracking (EMT) can partially replace X-ray guidance in minimally invasive procedures, reducing radiation in the OR. However, in this hybrid setting, EMT is disturbed by metallic distortion caused by the X-ray device. We plan to make hybrid navigation clinical reality to reduce radiation exposure for patients and surgeons, by compensating EMT error.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Our online compensation strategy exploits cycle-consistent generative adversarial neural networks (CycleGAN). Positions are translated from various bedside environments to their bench equivalents, by adjusting their z-component. Domain-translated points are fine-tuned on the x\u2013y plane to reduce error in the bench domain. We evaluate our compensation approach in a phantom experiment.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Since the domain-translation approach maps distorted points to their laboratory equivalents, predictions are consistent among different C-arm environments. Error is successfully reduced in all evaluation environments. Our qualitative phantom experiment demonstrates that our approach generalizes well to an unseen C-arm environment.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Adversarial, cycle-consistent training is an explicable, consistent and thus interpretable approach for online error compensation. Qualitative assessment of EMT error compensation gives a glimpse to the potential of our method for rotational error compensation.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02324-1","type":"journal-article","created":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T18:02:31Z","timestamp":1615744951000},"page":"757-765","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["CycleGAN for interpretable online EMT compensation"],"prefix":"10.1007","volume":"16","author":[{"given":"Henry","family":"Krumb","sequence":"first","affiliation":[]},{"given":"Dhritimaan","family":"Das","sequence":"additional","affiliation":[]},{"given":"Romol","family":"Chadda","sequence":"additional","affiliation":[]},{"given":"Anirban","family":"Mukhopadhyay","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,14]]},"reference":[{"key":"2324_CR1","unstructured":"Aoki T, Mansour DA, Koizumi T, Wada Y, Enami Y, Fujimori A, Kusano T, Matsuda K, Nogaki K, Tashiro Y, Hakozaki T, Shibata H, Tomioka K, Hirai T, Yamazaki T, Saito K, Goto S, Watanabe M, Otsuka K, Murakami M (2020) F. J Gastrointest Surg\u00a0230(3):1\u20138"},{"issue":"3","key":"2324_CR2","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.jvs.2010.09.039","volume":"53","author":"ML Dijkstra","year":"2011","unstructured":"Dijkstra ML, Eagleton MJ, Greenberg RK, Mastracci T, Hernandez A (2011) Intraoperative c-arm cone-beam computed tomography in fenestrated\/branched aortic endografting. J Vasc Surg 53(3):583\u2013590","journal-title":"J Vasc Surg"},{"issue":"8","key":"2324_CR3","doi-asserted-by":"publisher","first-page":"1702","DOI":"10.1109\/TMI.2014.2321777","volume":"33","author":"AM Franz","year":"2014","unstructured":"Franz AM, Haidegger T, Birkfellner W, Cleary K, Peters TM, Maier-Hein L (2014) Electromagnetic tracking in medicine-a review of technology, validation, and applications. IEEE Trans Med Imaging 33(8):1702\u20131725","journal-title":"IEEE Trans Med Imaging"},{"key":"2324_CR4","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: NEURIPS, pp 2672\u20132680"},{"key":"2324_CR5","doi-asserted-by":"publisher","first-page":"101938","DOI":"10.1016\/j.artmed.2020.101938","volume":"109","author":"S Kazeminia","year":"2020","unstructured":"Kazeminia S, Baur C, Kuijper A, van Ginneken B, Navab N, Albarqouni S, Mukhopadhyay A (2020) Gans for medical image analysis. Artif Intell Med 109:101938","journal-title":"Artif Intell Med"},{"issue":"3","key":"2324_CR6","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/BF01409422","volume":"5","author":"VV Kindratenko","year":"2000","unstructured":"Kindratenko VV (2000) A survey of electromagnetic position tracker calibration techniques. Virtual Reality 5(3):169\u2013182","journal-title":"Virtual Reality"},{"issue":"1","key":"2324_CR7","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/s10055-005-0005-3","volume":"9","author":"VV Kindratenko","year":"2005","unstructured":"Kindratenko VV, Sherman WR (2005) Neural network-based calibration of electromagnetic tracking systems. Virtual Reality 9(1):70\u201378","journal-title":"Virtual Reality"},{"key":"2324_CR8","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"2324_CR9","doi-asserted-by":"crossref","unstructured":"Krumb H, Hofmann S, K\u00fcgler D, Ghazy A, Dorweiler B, Bredemann J, Schmitt R, Sakas G, Mukhopadhyay A (2020) Leveraging spatial uncertainty for online error compensation in emt. IJCARS, pp 1\u20139","DOI":"10.1007\/s11548-020-02189-w"},{"issue":"7","key":"2324_CR10","first-page":"1127","volume":"14","author":"D K\u00fcgler","year":"2019","unstructured":"K\u00fcgler D, Krumb H, Bredemann J, Stenin I, Kristin J, Klenzner T, Schipper J, Schmitt R, Sakas G, Mukhopadhyay A (2019) High-precision evaluation of electromagnetic tracking. IJCARS 14(7):1127\u20131135","journal-title":"High-precision evaluation of electromagnetic tracking. IJCARS"},{"key":"2324_CR11","unstructured":"Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in neural information processing systems, pp 6402\u20136413"},{"key":"2324_CR12","doi-asserted-by":"crossref","unstructured":"Nash J (1951) Non-cooperative games. Ann Math 54(2):286\u2013295","DOI":"10.2307\/1969529"},{"key":"2324_CR13","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: an imperative style, high-performance deep learning library. In: Wallach H, Larochelle H, Beygelzimer A, d\u2019Alch\u00e9 Buc F, Fox E, Garnett R (eds) NEURIPS 32. Curran Associates, Inc, pp 8024\u20138035"},{"issue":"8","key":"2324_CR14","first-page":"1771","volume":"63","author":"H Sadjadi","year":"2016","unstructured":"Sadjadi H, Hashtrudi-Zaad K, Fichtinger G (2016) Simultaneous electromagnetic tracking and calibration for dynamic field distortion compensation. TBME 63(8):1771\u20131781","journal-title":"TBME"},{"key":"2324_CR15","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"2324_CR16","doi-asserted-by":"crossref","unstructured":"Tjoa E, Guan C (2020) A survey on explainable artificial intelligence (xai): towards medical xai. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2020.3027314"},{"key":"2324_CR17","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02324-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02324-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02324-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T08:56:15Z","timestamp":1621500975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02324-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,14]]},"references-count":17,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["2324"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02324-1","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"type":"print","value":"1861-6410"},{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2021,3,14]]},"assertion":[{"value":"20 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Code and data are available at  under MIT license.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"This research was partially funded by the German Research Foundation. The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}},{"value":"This article does not contain patient data.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}