{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T04:18:57Z","timestamp":1744258737093,"version":"3.40.4"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"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 Supercomput"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s11227-022-04595-0","type":"journal-article","created":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T12:03:14Z","timestamp":1654689794000},"page":"18598-18615","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Angle prediction model when the imaging plane is tilted about z-axis"],"prefix":"10.1007","volume":"78","author":[{"given":"Zheng","family":"Fang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bichao","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunren","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianyi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"issue":"6","key":"4595_CR1","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1148\/rg.246045065","volume":"24","author":"JF Barrett","year":"2004","unstructured":"Barrett JF, Keat N (2004) Artifacts in CT: recognition and avoidance. Radiographics 24(6):1679\u20131691","journal-title":"Radiographics"},{"issue":"8","key":"4595_CR2","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1109\/TBME.2007.891166","volume":"54","author":"Y Sun","year":"2007","unstructured":"Sun Y, Hou Y, Hu J (2007) Reduction of artifacts induced by misaligned geometry in cone-beam CT. IEEE Trans Biomed Eng 54(8):1461\u20131471","journal-title":"IEEE Trans Biomed Eng"},{"key":"4595_CR3","doi-asserted-by":"crossref","unstructured":"Guo J, Vidal V, Baskurt A et al. (2015) Evaluating the local visibility of geometric artifacts[C]. In: Proceedings of the Acm Siggraph Symposium on Applied Perception pp. 91\u201398.","DOI":"10.1145\/2804408.2804418"},{"issue":"4","key":"4595_CR4","doi-asserted-by":"publisher","first-page":"2122","DOI":"10.1118\/1.3556590","volume":"38","author":"J Baek","year":"2011","unstructured":"Baek J, Pelc NJ (2011) Local and global 3D noise power spectrum in cone-beam CT system with FDK reconstruction. Med Phys 38(4):2122\u20132131","journal-title":"Med Phys"},{"issue":"2","key":"4595_CR5","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1148\/radiol.2015132766","volume":"276","author":"LL Geyer","year":"2015","unstructured":"Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276(2):339\u2013357","journal-title":"Radiology"},{"issue":"11","key":"4595_CR6","doi-asserted-by":"publisher","first-page":"3489","DOI":"10.1088\/0031-9155\/45\/11\/327","volume":"45","author":"F Noo","year":"2000","unstructured":"Noo F, Clackdoyle R, Mennessier C et al (2000) Analytic method based on identification of ellipse parameters for scanner calibration in cone-beam tomography. Phys Med Biol 45(11):3489","journal-title":"Phys Med Biol"},{"issue":"4","key":"4595_CR7","doi-asserted-by":"publisher","first-page":"e0216054","DOI":"10.1371\/journal.pone.0216054","volume":"14","author":"CH Chang","year":"2019","unstructured":"Chang CH, Ni YC, Huang SY et al (2019) A geometric calibration method for the digital chest tomosynthesis with dual-axis scanning geometry. PLoS One 14(4):e0216054","journal-title":"PLoS One"},{"issue":"4","key":"4595_CR8","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1118\/1.1869652","volume":"32","author":"Y Cho","year":"2005","unstructured":"Cho Y, Moseley DJ, Siewerdsen JH et al (2005) Accurate technique for complete geometric calibration of cone-beam computed tomography systems. Med Phys 32(4):968\u2013983","journal-title":"Med Phys"},{"issue":"1","key":"4595_CR9","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1002\/mp.13278","volume":"46","author":"G Li","year":"2019","unstructured":"Li G, Luo S, You C et al (2019) A novel calibration method incorporating nonlinear optimization and ball-bearing markers for cone-beam CT with a parameterized trajectory. Med Phys 46(1):152\u2013164","journal-title":"Med Phys"},{"issue":"9","key":"4595_CR10","doi-asserted-by":"publisher","first-page":"4934","DOI":"10.1118\/1.3609096","volume":"38","author":"A Kingston","year":"2011","unstructured":"Kingston A, Sakellariou A, Varslot T et al (2011) Reliable automatic alignment of tomographic projection data by passive auto-focus. Med Phys 38(9):4934\u20134945","journal-title":"Med Phys"},{"issue":"2","key":"4595_CR11","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1109\/TMI.2012.2224360","volume":"32","author":"Y Meng","year":"2012","unstructured":"Meng Y, Gong H, Yang X (2012) Online geometric calibration of cone-beam computed tomography for arbitrary imaging objects. IEEE Trans Med Imaging 32(2):278\u2013288","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"4595_CR12","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1007\/s10278-017-9980-7","volume":"30","author":"P Lakhani","year":"2017","unstructured":"Lakhani P (2017) Deep convolutional neural networks for endotracheal tube position and X-ray image classification: challenges and opportunities. J Digit Imaging 30(4):460\u2013468","journal-title":"J Digit Imaging"},{"issue":"1","key":"4595_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-019-0196-x","volume":"5","author":"F Oviedo","year":"2019","unstructured":"Oviedo F, Ren Z, Sun S et al (2019) Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks. npj Comput Mater 5(1):1\u20139","journal-title":"npj Comput Mater"},{"key":"4595_CR14","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.cmpb.2019.06.005","volume":"177","author":"JC Souza","year":"2019","unstructured":"Souza JC, Diniz JOB, Ferreira JL et al (2019) An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. Comput Methods Programs Biomed 177:285\u2013296","journal-title":"Comput Methods Programs Biomed"},{"issue":"4","key":"4595_CR15","doi-asserted-by":"publisher","first-page":"213","DOI":"10.3390\/jpm10040213","volume":"10","author":"KS Lee","year":"2020","unstructured":"Lee KS, Kim JY, Jeon E et al (2020) Evaluation of scalability and degree of fine-tuning of deep convolutional neural networks for COVID-19 screening on chest x-ray images using explainable deep-learning algorithm. J Personal Med 10(4):213","journal-title":"J Personal Med"},{"key":"4595_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-020-00865-4","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. https:\/\/doi.org\/10.1007\/s13246-020-00865-4","journal-title":"Phys Eng Sci Med"},{"issue":"4\u20131","key":"4595_CR17","first-page":"1","volume":"6","author":"L Nguyen","year":"2017","unstructured":"Nguyen L (2017) Tutorial on support vector machine. Appl Comput Math 6(4\u20131):1\u201315","journal-title":"Appl Comput Math"},{"key":"4595_CR18","doi-asserted-by":"publisher","first-page":"619","DOI":"10.3233\/XST-200666","volume":"28","author":"L Shi","year":"2020","unstructured":"Shi L, Liu B, Yu H et al (2020) Review of CT image reconstruction open source toolkits. J X-Ray Sci Technol 28:619\u2013639","journal-title":"J X-Ray Sci Technol"},{"issue":"3","key":"4595_CR19","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3311\/PPtr.11480","volume":"47","author":"C Lin","year":"2019","unstructured":"Lin C, Li L, Luo W et al (2019) Transfer learning based traffic sign recognition using inception-v3 model. Period Polytech Transp Eng 47(3):242\u2013250","journal-title":"Period Polytech Transp Eng"},{"issue":"1","key":"4595_CR20","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/s00521-018-3627-6","volume":"32","author":"MZ Alom","year":"2020","unstructured":"Alom MZ, Hasan M, Yakopcic C et al (2020) Improved inception-residual convolutional neural network for object recognition. Neural Comput Appl 32(1):279\u2013293","journal-title":"Neural Comput Appl"},{"key":"4595_CR21","doi-asserted-by":"publisher","first-page":"146533","DOI":"10.1109\/ACCESS.2019.2946000","volume":"7","author":"C Wang","year":"2019","unstructured":"Wang C, Chen D, Hao L et al (2019) Pulmonary image classification based on inception-v3 transfer learning model[J]. IEEE Access 7:146533\u2013146541","journal-title":"IEEE Access"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04595-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-022-04595-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04595-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:02:25Z","timestamp":1744203745000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-022-04595-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,8]]},"references-count":21,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["4595"],"URL":"https:\/\/doi.org\/10.1007\/s11227-022-04595-0","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"type":"print","value":"0920-8542"},{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2022,6,8]]},"assertion":[{"value":"8 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}