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The accurate acquisition of standardized and anatomy-specific projections is essential in this process. The corresponding iterative positioning of the C-arm is error prone and involves repeated manual acquisitions or even continuous fluoroscopy. To reduce time and radiation exposure for patients and clinical staff and to avoid errors in fracture reduction or implant placement, we aim at guiding\u2014and in the long-run automating\u2014this procedure.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In contrast to the state of the art, we tackle this inherently ill-posed problem without requiring patient-individual prior information like preoperative computed tomography (CT) scans, without the need of registration and without requiring additional technical equipment besides the projection images themselves. We propose learning the necessary anatomical hints for efficient C-arm positioning from<jats:italic>in silico<\/jats:italic>simulations, leveraging masses of 3D CTs. Specifically, we propose a convolutional neural network regression model that predicts 5 degrees of freedom pose updates directly from a first X-ray image. The method is generalizable to different anatomical regions and standard projections.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Quantitative and qualitative validation was performed for two clinical applications involving two highly dissimilar anatomies, namely the lumbar spine and the proximal femur. Starting from one initial projection, the mean absolute pose error to the desired standard pose is iteratively reduced across different anatomy-specific standard projections. Acquisitions of both hip joints on 4 cadavers allowed for an evaluation on clinical data, demonstrating that the approach generalizes without retraining.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Overall, the results suggest the feasibility of an efficient deep learning-based automated positioning procedure, which is trained on simulations. Our proposed 2-stage approach for C-arm positioning significantly improves accuracy on synthetic images. In addition, we demonstrated that learning based on simulations translates to acceptable performance on real X-rays.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s11548-020-02204-0","type":"journal-article","created":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T05:19:07Z","timestamp":1591939147000},"page":"1095-1105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Toward automatic C-arm positioning for standard projections in orthopedic surgery"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1636-1663","authenticated-orcid":false,"given":"Lisa","family":"Kausch","sequence":"first","affiliation":[]},{"given":"Sarina","family":"Thomas","sequence":"additional","affiliation":[]},{"given":"Holger","family":"Kunze","sequence":"additional","affiliation":[]},{"given":"Maxim","family":"Privalov","sequence":"additional","affiliation":[]},{"given":"Sven","family":"Vetter","sequence":"additional","affiliation":[]},{"given":"Jochen","family":"Franke","sequence":"additional","affiliation":[]},{"given":"Andreas H.","family":"Mahnken","sequence":"additional","affiliation":[]},{"given":"Lena","family":"Maier-Hein","sequence":"additional","affiliation":[]},{"given":"Klaus","family":"Maier-Hein","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,12]]},"reference":[{"issue":"8","key":"2204_CR1","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1007\/s00264-014-2362-6","volume":"38","author":"C Bahrs","year":"2014","unstructured":"Bahrs C, Stojicevic T, Blumenstock G, Brorson S, Badke A, St\u00f6ckle U, Rolauffs B, Freude T (2014) Trends in epidemiology and patho-anatomical pattern of proximal humeral fractures. 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For this type of study, formal consent is not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The acquisition of data from living patients had a medical indication, and informed consent was not required. The acquired datasets of cadavers were available retrospectively after they had been generated during surgical courses for physicians. The corresponding consent for body donation for these purposes has been obtained.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}