{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T18:15:15Z","timestamp":1770142515116,"version":"3.49.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T00:00:00Z","timestamp":1583798400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T00:00:00Z","timestamp":1583798400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772241, 61702225"],"award-info":[{"award-number":["61772241, 61702225"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA196687"],"award-info":[{"award-number":["R01CA196687"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Grid Computing"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s10723-020-09513-3","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T09:03:27Z","timestamp":1583831007000},"page":"211-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Estimating CT from MR Abdominal Images Using Novel Generative Adversarial Networks"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5596-3694","authenticated-orcid":false,"given":"Pengjiang","family":"Qian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiankun","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atallah","family":"Baydoun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqing","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan","family":"Traughber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"Jr.","given":"Raymond F.","family":"Muzic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,10]]},"reference":[{"issue":"22","key":"9513_CR1","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1056\/NEJMra072149","volume":"357","author":"DJ Brenner","year":"2007","unstructured":"Brenner, D.J., Hall, E.J.: Computed tomography\u2014an increasing source of radiation exposure. N. Engl. J. Med. 357(22), 2277\u20132284 (2007)","journal-title":"N. Engl. J. Med."},{"issue":"20S1","key":"9513_CR2","doi-asserted-by":"publisher","first-page":"S180","DOI":"10.1016\/S1353-8020(13)70042-7","volume":"20","author":"AJ Stoessl","year":"2014","unstructured":"Stoessl, A.J.: Developments in neuroimaging: positron emission tomography. Parkinsonism Relat. Disord. 20(20S1), S180\u2013S183 (2014)","journal-title":"Parkinsonism Relat. Disord."},{"issue":"8","key":"9513_CR3","first-page":"1369","volume":"41","author":"T Beyer","year":"2000","unstructured":"Beyer, T., Townsend, D.W., Brun, T., Kinahan, P.E., Charron, M., Roddy, R., Jerin, J., Young, J., Byars, L., Nutt, R.: A combined PET\/CT scanner for clinical oncology. J. Nucl. Med. 41(8), 1369\u20131379 (2000)","journal-title":"J. Nucl. Med."},{"issue":"6","key":"9513_CR4","first-page":"1059","volume":"47","author":"LK Shankar","year":"2016","unstructured":"Shankar, L.K., Hoffman, J.M., Bacharach, S., Graham, M.M., Karp, J., Lammertsma, A.A., Larson, S., Mankoff, D.A., Siegel, B.A., Van den Abbeele, A., Yap, J., Sullivan, D.: Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute trials. J. Nucl. Med. 47(6), 1059\u20131066 (2016)","journal-title":"J. Nucl. Med."},{"key":"9513_CR5","unstructured":"FDG-PET\/CT Technical Committee: QIBA Profile: FDG-PET\/CT as an Imaging Biomarker Measuring Response to Cancer Therapy Profile (V 1.05) (2013)"},{"issue":"6","key":"9513_CR6","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.1007\/s00259-013-2652-z","volume":"41","author":"JC Dickson","year":"2014","unstructured":"Dickson, J.C., O'Meara, C., Barnes, A.: A comparison of CT- and MR-based attenuation correction in neurological PET. Eur. J. Nucl. Med. Mol. Imaging. 41(6), 1176\u20131189 (2014)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"5","key":"9513_CR7","doi-asserted-by":"publisher","first-page":"812","DOI":"10.2967\/jnumed.109.065425","volume":"51","author":"V Keereman","year":"2010","unstructured":"Keereman, V., Fierens, Y., Broux, T., De Deene, Y., Lonneux, M., Vandenberghe, S.: MRI-based attenuation correction for PET\/MRI using ultrashort echo time sequences. J. Nucl. Med. 51(5), 812\u2013818 (2010)","journal-title":"J. Nucl. Med."},{"issue":"5","key":"9513_CR8","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1097\/RLI.0b013e318283292f","volume":"48","author":"BK Navalpakkam","year":"2013","unstructured":"Navalpakkam, B.K., Braun, H., Kuwert, T., Quick, H.H.: Magnetic resonance-based attenuation correction for PET\/MR hybrid imaging using continuous valued attenuation maps. Investig. Radiol. 48(5), 323\u2013332 (2013)","journal-title":"Investig. Radiol."},{"issue":"6","key":"9513_CR9","doi-asserted-by":"publisher","first-page":"923","DOI":"10.2967\/jnumed.113.126813","volume":"55","author":"S Hitz","year":"2014","unstructured":"Hitz, S., Habekost, C., Furst, S., Delso, G., Forster, S., Ziegler, S., Nekolla, S.G., Souvatzoglou, M., Beer, A.J., Grimmer, T., Eiber, M., Schwaiger, M., Drzezga, A.: Systematic comparison of the performance of integrated whole-body PET\/MR imaging to conventional PET\/CT for 18F-FDG brain imaging in subjects examined for suspected dementia. J. Nucl. Med. 55(6), 923\u2013931 (2014)","journal-title":"J. Nucl. Med."},{"issue":"5","key":"9513_CR10","doi-asserted-by":"publisher","first-page":"796","DOI":"10.2967\/jnumed.111.092577","volume":"53","author":"Y Berker","year":"2012","unstructured":"Berker, Y., Franke, J., Salomon, A., Palmowski, M., Donker, H.C., Temur, Y., Mottaghy, F.M., Kuhl, C., Izquierdo-Garcia, D., Fayad, Z.A., Kiessling, F., Schulz, V.: MRI-based attenuation correction for hybrid PET\/MRI systems: a 4-class tissue segmentation technique using a combined ultrashort-echo-time\/Dixon MRI sequence. J. Nucl. Med. 53(5), 796\u2013804 (2012)","journal-title":"J. Nucl. Med."},{"issue":"1","key":"9513_CR11","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s10334-014-0445-4","volume":"28","author":"G Schramm","year":"2015","unstructured":"Schramm, G., Langner, J., Hofheinz, F., Petr, J., Beuthien-Baumann, B., Platzek, I., Steinbach, J., Kotzerke, J., van den Hoff, J.: Erratum to: Quantitative accuracy of attenuation correction in the Philips Ingenuity TF whole-body PET\/MR system: a direct comparison with transmission-based attenuation correction. Magnetic Resonance Mater. Phys. Biol. Med. 28(1), 101 (2015)","journal-title":"Magnetic Resonance Mater. Phys. Biol. Med."},{"issue":"1","key":"9513_CR12","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10334-012-0328-5","volume":"26","author":"G Schramm","year":"2013","unstructured":"Schramm, G., Langner, J., Hofheinz, F., Petr, J., Beuthien-Baumann, B., Platzek, I., Steinbach, J., Kotzerke, J., van den Hoff, J.: Quantitative accuracy of attenuation correction in the Philips Ingenuity TF whole-body PET\/MR system: a direct comparison with transmission based attenuation correction. Magnetic Resonance Materials in Physics, Biology and Medicine. 26(1), 115\u2013126 (2013)","journal-title":"Magnetic Resonance Materials in Physics, Biology and Medicine"},{"issue":"7","key":"9513_CR13","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1007\/s00259-012-2113-0","volume":"39","author":"A Samarin","year":"2012","unstructured":"Samarin, A., Burger, C., Wollenweber, S.D., Crook, D.W., Burger, I.A., Schmid, D.T., von Schulthess, G.K., Kuhn, F.P.: PET\/MR imaging of bone lesions--implications for PET quantification from imperfect attenuation correction. Eur. J. Nucl. Med. Mol. Imaging. 39(7), 1154\u20131160 (2012)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"2","key":"9513_CR14","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/s11307-014-0777-5","volume":"17","author":"H Arabi","year":"2015","unstructured":"Arabi, H., Rager, O., Alem, A., Varoquaux, A., Becker, M., Zaidi, H.: Clinical assessment of MR-guided 3-class and 4-class attenuation correction in PET\/MR. Mol. Imaging Biol. 17(2), 264\u2013276 (2015)","journal-title":"Mol. Imaging Biol."},{"issue":"7","key":"9513_CR15","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1016\/j.ejrad.2014.03.022","volume":"83","author":"MC Aznar","year":"2014","unstructured":"Aznar, M.C., Sersar, R., Saabye, J., Ladefoged, C.N., Andersen, F.L., Rasmussen, J.H., L\u00f6fgren, J., Beyer, T.: Whole-body PET\/MRI: the effect of bone attenuation during MR-based attenuation correction in oncology imaging. Eur. J. Radiol. 83(7), 1177\u20131183 (2014)","journal-title":"Eur. J. Radiol."},{"issue":"8","key":"9513_CR16","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1007\/s00259-014-2751-5","volume":"41","author":"D Izquierdo-Garcia","year":"2014","unstructured":"Izquierdo-Garcia, D., Sawiak, S.J., Knesaurek, K., Narula, J., Fuster, V., Machac, J., Fayad, Z.A.: Comparison of MR-based attenuation correction and CT-based attenuation correction of whole-body PET\/MR imaging. Eur. J. Nucl. Med. Mol. Imaging. 41(8), 1574\u20131584 (2014)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"10","key":"9513_CR17","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.2967\/jnumed.112.113209","volume":"54","author":"I Bezrukov","year":"2013","unstructured":"Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Scholkopf, B., Pichler, B.J.: MR-based attenuation correction methods for improved PET quantification in lesions within bone and susceptibility artifact regions. J. Nucl. Med. 54(10), 1768\u20131774 (2013)","journal-title":"J. Nucl. Med."},{"issue":"1","key":"9513_CR18","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s00259-008-1007-7","volume":"36","author":"M Hofmann","year":"2009","unstructured":"Hofmann, M., Pichler, B., Sch\u00f6lkopf, B., et al.: Towards quantitative PET\/MRI: a review of MR-based attenuation correction techniques. European Journal of Nuclear Medicine & Molecular Imaging. 36(1), 93\u2013104 (2009)","journal-title":"European Journal of Nuclear Medicine & Molecular Imaging."},{"issue":"2","key":"9513_CR19","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1088\/0031-9155\/60\/2\/825","volume":"60","author":"J Sj\u00f6lund","year":"2015","unstructured":"Sj\u00f6lund, J., Forsberg, D., Andersson, M., Knutsson, H.: Generating subject specific pseudo-CT of the head from MR using atlas-based regression. Phys. Med. Biol. 60(2), 825\u2013839 (2015)","journal-title":"Phys. Med. Biol."},{"issue":"1","key":"9513_CR20","doi-asserted-by":"publisher","first-page":"e5","DOI":"10.1016\/j.ijrobp.2011.11.056","volume":"83","author":"JA Dowling","year":"2012","unstructured":"Dowling, J.A., Lambert, J., Parker, J., Salvado, O., Fripp, J., Capp, A., Wratten, C., Denham, J.W., Greer, P.B.: An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 83(1), e5\u2013e11 (2012)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"8","key":"9513_CR21","doi-asserted-by":"publisher","first-page":"4974","DOI":"10.1118\/1.4926756","volume":"42","author":"KH Su","year":"2015","unstructured":"Su, K.H., Hu, L., Stehning, C., Helle, M., Qian, P., Thompson, C.L., Pereira, G.C., Jordan, D.W., Herrmann, K.A., Traughber, M., Muzic Jr., R.F., Traughber, B.J.: Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering. Medical Physics. 42(8), 4974\u20134986 (2015)","journal-title":"Medical Physics"},{"issue":"6Part22","key":"9513_CR22","doi-asserted-by":"publisher","first-page":"3881","DOI":"10.1118\/1.4735847","volume":"39","author":"S Hsu","year":"2012","unstructured":"Hsu, S., Cao, Y., Balter, J.: MO-G-BRA-02: Investigation of a method for generating synthetic CT models from MRI scans for radiation therapy. Med. Phys. 39(6Part22), 3881\u20133881 (2012)","journal-title":"Med. Phys."},{"issue":"19","key":"9513_CR23","doi-asserted-by":"publisher","first-page":"7814","DOI":"10.1088\/1361-6560\/aa8851","volume":"62","author":"M Khalif\u00e9","year":"2017","unstructured":"Khalif\u00e9, M., Fernandez, B., Jaubert, O., Soussan, M., Brulon, V., Buvat, I., Comtat, C.: Subject-specific bone attenuation correction for brain PET\/MR: can ZTE-MRI substitute CT scan accurately? Phys. Med. Biol. 62(19), 7814\u20137832 (2017)","journal-title":"Phys. Med. Biol."},{"key":"9513_CR24","first-page":"987","volume-title":"Proc IEEE 11th International Symposium on Biomedical Imaging","author":"A Jog","year":"2014","unstructured":"Jog, A., Carass, A., Prince, J.L.: Improving magnetic resonance resolution with supervised learning. In: Proc IEEE 11th International Symposium on Biomedical Imaging, pp. 987\u2013990 (2014)"},{"issue":"1","key":"9513_CR25","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TMI.2015.2461533","volume":"35","author":"T Huynh","year":"2015","unstructured":"Huynh, T., Gao, Y., Kang, J., Wang, L., Zhang, P., Lian, J., Shen, D.: Estimating CT image from MRI data using structured random forest and auto-context model. IEEE Trans. Med. Imaging. 35(1), 174\u2013183 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"9513_CR26","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1016\/j.ijrobp.2015.08.045","volume":"93","author":"JA Dowling","year":"2015","unstructured":"Dowling, J.A., Sun, J., Pichler, P., Rivest-H\u00e9nault, D., Ghose, S., Richardson, H., FRANZCR, C.W., Martin, J., Arm, J., Best, L., Chandra, S.S., Fripp, J., Menk, F.W., Greer, P.B.: Automatic substitute computed tomography generation and contouring for magnetic resonance imaging (MRI)-alone external beam radiation therapy from standard MRI sequences. Int. J. Radiat. Oncol. Biol. Phys. 93(5), 1144\u20131153 (2015)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"9513_CR27","doi-asserted-by":"publisher","unstructured":"Qian, P., Chen, Y., Kuo, J.W., Zhang, Y.D., Jiang, Y., Zhao, K., Helo, R.A., Friel, H., Baydoun, A., Zhou, F., Heo, J.U., Avril, N., Herrmann, K., Ellis, R., Traughber, B., Jones, R.S., Wang, S., Su, K.H., Muzic Jr., R.F.: mDixon-based synthetic CT generation for PET attenuation correction on abdomen and pelvis jointly using transfer fuzzy clustering and active learning-based classification. IEEE Trans. Med. Imaging. (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2935916","DOI":"10.1109\/TMI.2019.2935916"},{"issue":"1","key":"9513_CR28","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.ijrobp.2017.08.043","volume":"100","author":"E Johnstone","year":"2018","unstructured":"Johnstone, E., Wyatt, J.J., Henry, A.M., Short, S.C., Sebag-Montefiore, D., Murray, L., Kelly, C.G., McCallum, H.M., Speight, R.: Systematic review of synthetic computed tomography generation methodologies for use in magnetic resonance imaging-only radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 100(1), 199\u2013217 (2018)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"9513_CR29","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature. 521, 436\u2013444 (2015)","journal-title":"Nature."},{"key":"9513_CR30","first-page":"770","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)"},{"key":"9513_CR31","first-page":"2642","volume-title":"Proceedings of the 34th International Conference on Machine Learning, 70","author":"A Odena","year":"2017","unstructured":"Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning, 70, pp. 2642\u20132651 (2017)"},{"key":"9513_CR32","first-page":"1060","volume-title":"Proceedings of the 33rd International Conference on Machine Learning, 48","author":"S Reed","year":"2016","unstructured":"Reed, S., Akata, Z., Yan, X., Logeswaran, L.: Generative adversarial text to image synthesis. In: Proceedings of the 33rd International Conference on Machine Learning, 48, pp. 1060\u20131069 (2016)"},{"key":"9513_CR33","doi-asserted-by":"publisher","unstructured":"Narasimha, R., Fern, X.Z., Raich, R.: Simultaneous segmentation and classification of bird song using CNN. In: 2017 IEEE international conference on acoustics, Speech Signal Process, vol. 2017. https:\/\/doi.org\/10.1109\/ICASSP.2017.7952135","DOI":"10.1109\/ICASSP.2017.7952135"},{"key":"9513_CR34","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10723-018-9450-6","volume":"17","author":"M Nauman","year":"2019","unstructured":"Nauman, M., Rehman, H.U., Politano, G., Benso, A.: Beyond homology transfer: deep learning for automated annotation of proteins. J. Grid Comput. 17, 225\u2013237 (2019)","journal-title":"J. Grid Comput."},{"issue":"4","key":"9513_CR35","first-page":"640","volume":"39","author":"J Long","year":"2014","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9513_CR36","first-page":"2487","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition","author":"H Chen","year":"2016","unstructured":"Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: Deep contour-aware networks for accurate gland segmentation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2487\u20132496 (2016)"},{"key":"9513_CR37","unstructured":"Kitchen, A., Seah, J.: Deep generative adversarial neural networks for realistic prostate lesion MRI synthesis. arXiv. 1708.00129 (2017)"},{"key":"9513_CR38","unstructured":"Kohl, S., Bonekamp, D., Schlemmer, H.P., Yaqubi, K., Hohenfellner, M., Hadaschik, B., Radtke, J.P., Maier-Hein, K.: Adversarial networks for the detection of aggressive prostate cancer. arXiv. 1702.08014 (2017)"},{"key":"9513_CR39","doi-asserted-by":"publisher","unstructured":"Fu, J., Yang, Y., Singhrao, K., Ruan, D., Chu, F.I., Low, D.A., Lewis, J.H.: Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging. Med. Phys. (2019). https:\/\/doi.org\/10.1002\/mp.13672","DOI":"10.1002\/mp.13672"},{"issue":"8","key":"9513_CR40","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1002\/mp.13047","volume":"45","author":"H Emami","year":"2018","unstructured":"Emami, H., Dong, M., Nejad-Davarani, S.P., Glide-Hurst, C.K.: Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Med. Phys. 45(8), 3627\u20133636 (2018)","journal-title":"Med. Phys."},{"issue":"4","key":"9513_CR41","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1002\/mp.12155","volume":"44","author":"X Han","year":"2017","unstructured":"Han, X.: MR-based synthetic CT generation using a deep convolutional neural network method. Med. Phys. 44(4), 1408\u20131419 (2017)","journal-title":"Med. Phys."},{"key":"9513_CR42","doi-asserted-by":"publisher","unstructured":"Zia, T., Razzaq, S.: Residual recurrent highway networks for learning deep sequence prediction models. J. Grid Comput. 1\u20138 (2018). https:\/\/doi.org\/10.1007\/s10723-018-9444-4","DOI":"10.1007\/s10723-018-9444-4"},{"key":"9513_CR43","first-page":"234","volume-title":"MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234\u2013241 (2015)"},{"key":"9513_CR44","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv. 1411.1784 (2014)"},{"key":"9513_CR45","first-page":"2672","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems. 2","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2, pp. 2672\u20132680 (2014)"},{"key":"9513_CR46","volume-title":"2015 ICLR","author":"A Radford","year":"2015","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: 2015 ICLR (2015)"},{"key":"9513_CR47","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv. 1701.07875 (2017)"},{"key":"9513_CR48","doi-asserted-by":"publisher","first-page":"5967","DOI":"10.1109\/CVPR.2017.632","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition","author":"P Isola","year":"2017","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5967\u20135976 (2017)"},{"key":"9513_CR49","first-page":"448","volume-title":"Proceedings of the 32nd International Conference on Machine Learning, 37","author":"S Ioffe","year":"2015","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, 37, pp. 448\u2013456 (2015)"},{"key":"9513_CR50","unstructured":"Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv. 1505.00853 (2015)"},{"key":"9513_CR51","doi-asserted-by":"publisher","DOI":"10.1109\/ICEC.1995.487455","volume-title":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","author":"GH Gates Jr","year":"2002","unstructured":"Gates Jr., G.H., Merkle, L.D., Lamont, G., Pachter, R.: Simple genetic algorithm parameter selection for protein structure prediction. In: Proceedings of 1995 IEEE International Conference on Evolutionary Computation (2002). https:\/\/doi.org\/10.1109\/ICEC.1995.487455"},{"key":"9513_CR52","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s10723-012-9210-y","volume":"10","author":"KZ Gkoutioudi","year":"2012","unstructured":"Gkoutioudi, K.Z., Karatza, H.D.: Multi-criteria job scheduling in grid using an accelerated genetic algorithm. J. Grid Comput. 10, 311\u2013323 (2012)","journal-title":"J. Grid Comput."},{"key":"9513_CR53","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s10723-014-9306-7","volume":"12","author":"H Khajemohammadi","year":"2014","unstructured":"Khajemohammadi, H., Fanian, A., Gulliver, T.A.: Efficient workflow scheduling for grid computing using a leveled multi-objective genetic algorithm. J. Grid Comput. 12, 637\u2013663 (2014)","journal-title":"J. Grid Comput."},{"issue":"8","key":"9513_CR54","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1002\/mp.13574","volume":"46","author":"KH Su","year":"2019","unstructured":"Su, K.H., Friel, H.T., Kuo, J.W., Helo, R.A., Baydoun, A., Stehning, C., Crisan, A.N., Devaraj, A., Jordan, D.W., Qian, P., Leisser, A., Ellis, R.J., Herrmann, K.A., Avril, N., Traughber, B.J., Muzic Jr., R.F.: UTE-mDixon-based thorax synthetic CT generation. Med. Phys. 46(8), 3520\u20133531 (2019)","journal-title":"Med. Phys."},{"key":"9513_CR55","doi-asserted-by":"publisher","first-page":"Art. ID 891585","DOI":"10.1155\/2011\/891585","volume":"2011","author":"G Janssens","year":"2011","unstructured":"Janssens, G., Jacques, L., de Xivry, J.O., Geets, X., Macq, B.: Diffeomorphic registration of images with variable contrast enhancement. Int. J. Biomed. Imaging. 2011, Art. ID 891585 (2011)","journal-title":"Int. J. Biomed. Imaging"},{"key":"9513_CR56","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.patcog.2015.08.009","volume":"50","author":"P Qian","year":"2016","unstructured":"Qian, P., Sun, S., Jiang, Y., Su, K.-H., Ni, T., Wang, S., Muzic Jr., R.F.: Cross-domain, soft-partition clustering with diversity measure and knowledge reference. Pattern Recogn. 50, 155\u2013177 (2016)","journal-title":"Pattern Recogn."},{"key":"9513_CR57","volume-title":"Proceedings of the 3rd International Conference on Learning Representations","author":"DP Kingma","year":"2015","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (2015)"}],"container-title":["Journal of Grid Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10723-020-09513-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10723-020-09513-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10723-020-09513-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:24:53Z","timestamp":1615335893000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10723-020-09513-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,10]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["9513"],"URL":"https:\/\/doi.org\/10.1007\/s10723-020-09513-3","relation":{},"ISSN":["1570-7873","1572-9184"],"issn-type":[{"value":"1570-7873","type":"print"},{"value":"1572-9184","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,10]]},"assertion":[{"value":"30 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}