{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:29:26Z","timestamp":1750220966420,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,8,24]],"date-time":"2019-08-24T00:00:00Z","timestamp":1566604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,8,24]]},"DOI":"10.1145\/3364836.3364904","type":"proceedings-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T13:04:52Z","timestamp":1577106292000},"page":"335-342","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["An Accurate Estimation of T2* Mapping for Fast Magnetic Resonance Imaging"],"prefix":"10.1145","author":[{"given":"Jie","family":"Yang","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen"}]},{"given":"Zhicheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese, Academy of Sciences, Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen"}]},{"given":"Yanchun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tencent, Tencent, Healthcare 10000th, Shenzhen"}]},{"given":"Yaoqin","family":"Xie","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen"}]}],"member":"320","published-online":{"date-parts":[[2019,8,24]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00256-014-1852-3"},{"key":"e_1_3_2_1_2_1","volume-title":"Zonal T2* and T1Gd assessment of knee joint cartilage in various histological grades of cartilage degeneration: an observational in vitro study","author":"Bernd B.","year":"2015","unstructured":"B. Bernd , H. S. Hosalkar , F. R. Miese , S. Jonas , D. P. K Nig , H. Monika , , \" Zonal T2* and T1Gd assessment of knee joint cartilage in various histological grades of cartilage degeneration: an observational in vitro study ,\" vol. 5 , p. e006895, 2015 . B. Bernd, H. S. Hosalkar, F. R. Miese, S. Jonas, D. P. K Nig, H. Monika, et al., \"Zonal T2* and T1Gd assessment of knee joint cartilage in various histological grades of cartilage degeneration: an observational in vitro study,\" vol. 5, p. e006895, 2015."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"S. C. L. Deoni %J Topics in Magnetic Resonance Imaging Tmri \"Quantitative relaxometry of the brain \" vol. 21 p. 101 2010.  S. C. L. Deoni %J Topics in Magnetic Resonance Imaging Tmri \"Quantitative relaxometry of the brain \" vol. 21 p. 101 2010.","DOI":"10.1097\/RMR.0b013e31821e56d8"},{"key":"e_1_3_2_1_4_1","volume-title":"o. O. R. Koff, \"Morphologic and quantitative magnetic resonance imaging of knee articular cartilage for the assessment of post-traumatic osteoarthritis","author":"Eagle S.","year":"2016","unstructured":"S. Eagle , H. G. Potter , and M. F. J. J. o. O. R. Koff, \"Morphologic and quantitative magnetic resonance imaging of knee articular cartilage for the assessment of post-traumatic osteoarthritis ,\" vol. 35 , p. 412, 2016 . S. Eagle, H. G. Potter, and M. F. J. J. o. O. R. Koff, \"Morphologic and quantitative magnetic resonance imaging of knee articular cartilage for the assessment of post-traumatic osteoarthritis,\" vol. 35, p. 412, 2016."},{"key":"e_1_3_2_1_5_1","first-page":"1695","volume-title":"Is magnetic resonance imaging reliable in predicting clinical outcome after articular cartilage repair of the knee? A systematic review and meta-analysis","author":"Windt T. S.","year":"2013","unstructured":"T. S. Windt , De, G. H. Welsch , B. Mats , L. A. Vonk , M. Stefan , T. Siegfried , , \" Is magnetic resonance imaging reliable in predicting clinical outcome after articular cartilage repair of the knee? A systematic review and meta-analysis ,\" vol. 41 , pp. 1695 -- 1702 , 2013 . T. S. Windt, De, G. H. Welsch, B. Mats, L. A. Vonk, M. Stefan, T. Siegfried, et al., \"Is magnetic resonance imaging reliable in predicting clinical outcome after articular cartilage repair of the knee? A systematic review and meta-analysis,\" vol. 41, pp. 1695--1702, 2013."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jse.2015.03.016"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2010.02.001"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00256-011-1313-1"},{"key":"e_1_3_2_1_9_1","first-page":"37","volume-title":"Articular cartilage in the knee: current MR imaging techniques and applications in clinical practice and research","author":"Crema M. D.","year":"2011","unstructured":"M. D. Crema , F. W. Roemer , M. D. Marra , D. Burstein , G. E. Gold , F. Eckstein , , \" Articular cartilage in the knee: current MR imaging techniques and applications in clinical practice and research ,\" vol. 31 , pp. 37 -- 61 , 2011 . M. D. Crema, F. W. Roemer, M. D. Marra, D. Burstein, G. E. Gold, F. Eckstein, et al., \"Articular cartilage in the knee: current MR imaging techniques and applications in clinical practice and research,\" vol. 31, pp. 37--61, 2011."},{"key":"e_1_3_2_1_10_1","first-page":"896","article-title":"Feasibility of T2* mapping for the evaluation of hip joint cartilage at 1.5T using a three-dimensional (3D), gradient-echo (GRE) sequence: a prospective study","volume":"62","author":"Bernd B.","year":"2010","unstructured":"B. Bernd , H. S. Hosalkar , H. Tim , K. Young-Jo , W. Stefan , K. A. Siebenrock , , \" Feasibility of T2* mapping for the evaluation of hip joint cartilage at 1.5T using a three-dimensional (3D), gradient-echo (GRE) sequence: a prospective study ,\" Magnetic Resonance in Medicine , vol. 62 , pp. 896 -- 901 , 2010 . B. Bernd, H. S. Hosalkar, H. Tim, K. Young-Jo, W. Stefan, K. A. Siebenrock, et al., \"Feasibility of T2* mapping for the evaluation of hip joint cartilage at 1.5T using a three-dimensional (3D), gradient-echo (GRE) sequence: a prospective study,\" Magnetic Resonance in Medicine, vol. 62, pp. 896--901, 2010.","journal-title":"Magnetic Resonance in Medicine"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2012.03.011"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22450"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00256-011-1171-x"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00256-010-1036-8"},{"key":"e_1_3_2_1_15_1","first-page":"77","volume-title":"Dual Inversion Recovery Ultrashort Echo Time (DIR-UTE) Imaging and Quantification of the Zone of Calcified Cartilage (ZCC)","author":"Du J.","year":"2013","unstructured":"J. Du , M. Carl , W. C. Bae , S. Statum , E. Y. Chang , G. M. Bydder , , \" Dual Inversion Recovery Ultrashort Echo Time (DIR-UTE) Imaging and Quantification of the Zone of Calcified Cartilage (ZCC) ,\" vol. 21 , pp. 77 -- 85 , 2013 . J. Du, M. Carl, W. C. Bae, S. Statum, E. Y. Chang, G. M. Bydder, et al., \"Dual Inversion Recovery Ultrashort Echo Time (DIR-UTE) Imaging and Quantification of the Zone of Calcified Cartilage (ZCC),\" vol. 21, pp. 77--85, 2013."},{"key":"e_1_3_2_1_16_1","first-page":"4","volume-title":"Three-dimensional ultrashort echo time cones (3D UTE-Cones) magnetic resonance imaging of entheses and tendons","author":"Chen B.","year":"2018","unstructured":"B. Chen , Y. Zhao , X. Cheng , Y. Ma , E. Y. Chang , A. Kavanaugh , , \" Three-dimensional ultrashort echo time cones (3D UTE-Cones) magnetic resonance imaging of entheses and tendons ,\" vol. 49 , pp. 4 -- 9 , 2018 . B. Chen, Y. Zhao, X. Cheng, Y. Ma, E. Y. Chang, A. Kavanaugh, et al., \"Three-dimensional ultrashort echo time cones (3D UTE-Cones) magnetic resonance imaging of entheses and tendons,\" vol. 49, pp. 4--9, 2018."},{"key":"e_1_3_2_1_17_1","volume-title":"Measurement of bound and pore water T1 relaxation times in cortical bone using three-dimensional ultrashort echo time cones sequences","author":"Chen J.","year":"2017","unstructured":"J. Chen , E. Y. Chang , M. Carl , Y. Ma , H. Shao , B. Chen , , \" Measurement of bound and pore water T1 relaxation times in cortical bone using three-dimensional ultrashort echo time cones sequences ,\" vol. 77 , 2017 . J. Chen, E. Y. Chang, M. Carl, Y. Ma, H. Shao, B. Chen, et al., \"Measurement of bound and pore water T1 relaxation times in cortical bone using three-dimensional ultrashort echo time cones sequences,\" vol. 77, 2017."},{"key":"e_1_3_2_1_18_1","first-page":"578","volume-title":"Standardized T-2* map of normal human heart in vivo to correct T-2(*) segmental artefacts","author":"Positano V.","year":"2010","unstructured":"V. Positano , A. Pepe , M. Santarelli , B. Scattini , D. De Marchi , A. Ramazzotti , , \" Standardized T-2* map of normal human heart in vivo to correct T-2(*) segmental artefacts ,\" vol. 20 , pp. 578 -- 590 , 2010 . V. Positano, A. Pepe, M. Santarelli, B. Scattini, D. De Marchi, A. Ramazzotti, et al., \"Standardized T-2* map of normal human heart in vivo to correct T-2(*) segmental artefacts,\" vol. 20, pp. 578--590, 2010."},{"key":"e_1_3_2_1_19_1","volume-title":"o. C. M. R. Firmin, \"304 An non subjective method for myocardial T2* curve fitting in thalassemia","author":"He T.","year":"2008","unstructured":"T. He , G. C. Smith , R. H. Mohiaddin , D. J. Pennell , and D. N. J. J. o. C. M. R. Firmin, \"304 An non subjective method for myocardial T2* curve fitting in thalassemia ,\" vol. 10 , p. A 107, 2008 . T. He, G. C. Smith, R. H. Mohiaddin, D. J. Pennell, and D. N. J. J. o. C. M. R. Firmin, \"304 An non subjective method for myocardial T2* curve fitting in thalassemia,\" vol. 10, p. A107, 2008."},{"key":"e_1_3_2_1_20_1","first-page":"1202","volume-title":"Generalized autocalibrating partially parallel acquisitions (GRAPPA)","author":"Griswold M. A.","year":"2002","unstructured":"M. A. Griswold , P. M. Jakob , R. M. Heidemann , M. Nittka , V. Jellus , J. Wang , , \" Generalized autocalibrating partially parallel acquisitions (GRAPPA) ,\" vol. 47 , pp. 1202 -- 1210 , 2002 . M. A. Griswold, P. M. Jakob, R. M. Heidemann, M. Nittka, V. Jellus, J. Wang, et al., \"Generalized autocalibrating partially parallel acquisitions (GRAPPA),\" vol. 47, pp. 1202--1210, 2002."},{"key":"e_1_3_2_1_21_1","volume-title":"i. M. Jakob, \"VD-AUTO-SMASH imaging","author":"Heidemann R.","year":"2001","unstructured":"R. Heidemann , M. Griswold , A. Haase , and P. J. M. R. i. M. Jakob, \"VD-AUTO-SMASH imaging ,\" vol. 45 , p. 1066, 2001 . R. Heidemann, M. Griswold, A. Haase, and P. J. M. R. i. M. Jakob, \"VD-AUTO-SMASH imaging,\" vol. 45, p. 1066, 2001."},{"key":"e_1_3_2_1_22_1","first-page":"223","volume-title":"GRAPPA: how to choose the optimal method","author":"Blaimer M.","year":"2004","unstructured":"M. Blaimer , F. Breuer , M. Mueller , R. M. Heidemann , M. A. Griswold , and P. M. J. T. M. R. I. Jakob , \"SMASH, SENSE, PILS , GRAPPA: how to choose the optimal method ,\" vol. 15 , pp. 223 -- 236 , 2004 . M. Blaimer, F. Breuer, M. Mueller, R. M. Heidemann, M. A. Griswold, and P. M. J. T. M. R. I. Jakob, \"SMASH, SENSE, PILS, GRAPPA: how to choose the optimal method,\" vol. 15, pp. 223--236, 2004."},{"key":"e_1_3_2_1_23_1","first-page":"952","volume-title":"i. M. Boesiger, \"SENSE: sensitivity encoding for fast MRI","author":"Pruessmann K. P.","year":"2015","unstructured":"K. P. Pruessmann , M. Weiger , M. B. Scheidegger , and P. J. M. R. i. M. Boesiger, \"SENSE: sensitivity encoding for fast MRI ,\" vol. 42 , pp. 952 -- 962 , 2015 . K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. J. M. R. i. M. Boesiger, \"SENSE: sensitivity encoding for fast MRI,\" vol. 42, pp. 952--962, 2015."},{"key":"e_1_3_2_1_24_1","first-page":"990","volume-title":"ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA","author":"Uecker M.","year":"2014","unstructured":"M. Uecker , P. Lai , M. J. Murphy , P. Virtue , M. Elad , J. M. Pauly , , \" ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA ,\" vol. 71 , pp. 990 -- 1001 , 2014 . M. Uecker, P. Lai, M. J. Murphy, P. Virtue, M. Elad, J. M. Pauly, et al., \"ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA,\" vol. 71, pp. 990--1001, 2014."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"M. Lustig D. L. Donoho J. M. Santos and J. M. J. I. S. P. M. Pauly \"Compressed Sensing MRI \" vol. 25 pp. 72--82 2008.  M. Lustig D. L. Donoho J. M. Santos and J. M. J. I. S. P. M. Pauly \"Compressed Sensing MRI \" vol. 25 pp. 72--82 2008.","DOI":"10.1109\/MSP.2007.914728"},{"key":"e_1_3_2_1_26_1","first-page":"1182","volume-title":"i. M. Pauly, \"Sparse MRI: The application of compressed sensing for rapid MR imaging","author":"Lustig M.","year":"2010","unstructured":"M. Lustig , D. Donoho , and J. M. J. M. R. i. M. Pauly, \"Sparse MRI: The application of compressed sensing for rapid MR imaging ,\" vol. 58 , pp. 1182 -- 1195 , 2010 . M. Lustig, D. Donoho, and J. M. J. M. R. i. M. Pauly, \"Sparse MRI: The application of compressed sensing for rapid MR imaging,\" vol. 58, pp. 1182--1195, 2010."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"A. Majumdar and R. K. J. M. R. I. Ward \"An algorithm for sparse MRI reconstruction by Schatten p -norm minimization \" vol. 29 pp. 408--417 2011.  A. Majumdar and R. K. J. M. R. I. Ward \"An algorithm for sparse MRI reconstruction by Schatten p -norm minimization \" vol. 29 pp. 408--417 2011.","DOI":"10.1016\/j.mri.2010.09.001"},{"key":"e_1_3_2_1_28_1","first-page":"1042","volume-title":"o. M. I. Jacob, \"Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR","author":"Lingala S. G.","year":"2011","unstructured":"S. G. Lingala , Y. Hu , E. Dibella , and M. J. I. T. o. M. I. Jacob, \"Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR ,\" vol. 30 , pp. 1042 -- 1054 , 2011 . S. G. Lingala, Y. Hu, E. Dibella, and M. J. I. T. o. M. I. Jacob, \"Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR,\" vol. 30, pp. 1042--1054, 2011."},{"key":"e_1_3_2_1_29_1","volume-title":"Low rank matrix recovery for real-time cardiac MRI,\" in IEEE International Conference on Biomedical Imaging: from Nano to Macro","author":"Zhao B.","year":"2010","unstructured":"B. Zhao , J. P. Haldar , C. Brinegar , and Z. P. Liang , \" Low rank matrix recovery for real-time cardiac MRI,\" in IEEE International Conference on Biomedical Imaging: from Nano to Macro , 2010 . B. Zhao, J. P. Haldar, C. Brinegar, and Z. P. Liang, \"Low rank matrix recovery for real-time cardiac MRI,\" in IEEE International Conference on Biomedical Imaging: from Nano to Macro, 2010."},{"key":"e_1_3_2_1_30_1","first-page":"668","volume-title":"o. M. I. Haldar, \"Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS) for Constrained MRI","author":"J. P. J. I.","year":"2014","unstructured":"J. P. J. I. T. o. M. I. Haldar, \"Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS) for Constrained MRI ,\" vol. 33 , pp. 668 -- 681 , 2014 . J. P. J. I. T. o. M. I. Haldar, \"Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS) for Constrained MRI,\" vol. 33, pp. 668--81, 2014."},{"key":"e_1_3_2_1_31_1","first-page":"1759","volume-title":"o. M. I. Frahm, \"Model-based iterative reconstruction for radial fast spin-echo MRI","author":"Block K. T.","year":"2009","unstructured":"K. T. Block , M. Uecker , and J. J. I. T. o. M. I. Frahm, \"Model-based iterative reconstruction for radial fast spin-echo MRI ,\" vol. 28 , pp. 1759 -- 1769 , 2009 . K. T. Block, M. Uecker, and J. J. I. T. o. M. I. Frahm, \"Model-based iterative reconstruction for radial fast spin-echo MRI,\" vol. 28, pp. 1759--1769, 2009."},{"key":"e_1_3_2_1_32_1","volume-title":"o. M. I. Liang, \"Model-Based MR Parameter Mapping With Sparsity Constraints: Parameter Estimation and Performance Bounds","author":"Zhao B.","year":"2014","unstructured":"B. Zhao , F. Lam , and Z. P. J. I. T. o. M. I. Liang, \"Model-Based MR Parameter Mapping With Sparsity Constraints: Parameter Estimation and Performance Bounds ,\" 2014 . B. Zhao, F. Lam, and Z. P. J. I. T. o. M. I. Liang, \"Model-Based MR Parameter Mapping With Sparsity Constraints: Parameter Estimation and Performance Bounds,\" 2014."},{"key":"e_1_3_2_1_33_1","first-page":"420","volume-title":"o. M. R. I. Jens, \"Model-based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin-echo MRI","author":"Sumpf T. J.","year":"2015","unstructured":"T. J. Sumpf , U. Martin , B. Susann , and F. J. J. o. M. R. I. Jens, \"Model-based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin-echo MRI ,\" vol. 34 , pp. 420 -- 428 , 2015 . T. J. Sumpf, U. Martin, B. Susann, and F. J. J. o. M. R. I. Jens, \"Model-based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin-echo MRI,\" vol. 34, pp. 420--428, 2015."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Y. Lecun Y. Bengio and G. J. N. Hinton \"Deep learning \" vol. 521 p. 436 2015.  Y. Lecun Y. Bengio and G. J. N. Hinton \"Deep learning \" vol. 521 p. 436 2015.","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_1_35_1","first-page":"1930","volume-title":"Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data","author":"Hoo-Chang S.","year":"2013","unstructured":"S. Hoo-Chang , M. R. Orton , D. J. Collins , S. J. Doran , M. O. Leach , % J IEEE Transactions on Pattern Analysis , and M. Intelligence , \" Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data ,\" vol. 35 , pp. 1930 -- 1943 , 2013 . S. Hoo-Chang, M. R. Orton, D. J. Collins, S. J. Doran, M. O. Leach, %J IEEE Transactions on Pattern Analysis, and M. Intelligence, \"Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,\" vol. 35, pp. 1930--1943, 2013."},{"key":"e_1_3_2_1_36_1","volume-title":"Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images","author":"Xu J.","year":"2016","unstructured":"J. Xu , L. Xiang , Q. Liu , H. Gilmore , J. Wu , J. Tang , , \" Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images ,\" vol. 35 , p. 119, 2016 . J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, et al., \"Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images,\" vol. 35, p. 119, 2016."},{"key":"e_1_3_2_1_37_1","volume-title":"Natural image denoising with convolutional networks,\" in International Conference on Neural Information Processing Systems","author":"Jain V.","year":"2008","unstructured":"V. Jain and H. S. Seung , \" Natural image denoising with convolutional networks,\" in International Conference on Neural Information Processing Systems , 2008 . V. Jain and H. S. Seung, \"Natural image denoising with convolutional networks,\" in International Conference on Neural Information Processing Systems, 2008."},{"key":"e_1_3_2_1_38_1","first-page":"1790","article-title":"Deep convolutional neural network for image deconvolution","author":"Xu L.","year":"2014","unstructured":"L. Xu , J. S. J. Ren , C. Liu , and J. Jia , \" Deep convolutional neural network for image deconvolution ,\" in International Conference on Neural Information Processing Systems , 2014 , pp. 1790 -- 1798 . L. Xu, J. S. J. Ren, C. Liu, and J. Jia, \"Deep convolutional neural network for image deconvolution,\" in International Conference on Neural Information Processing Systems, 2014, pp. 1790--1798.","journal-title":"International Conference on Neural Information Processing Systems"},{"key":"e_1_3_2_1_39_1","volume-title":"Image Denoising and Inpainting with Deep Neural Networks,\" in International Conference on Neural Information Processing Systems","author":"Xie J.","year":"2012","unstructured":"J. Xie , L. Xu , and E. Chen , \" Image Denoising and Inpainting with Deep Neural Networks,\" in International Conference on Neural Information Processing Systems , 2012 . J. Xie, L. Xu, and E. Chen, \"Image Denoising and Inpainting with Deep Neural Networks,\" in International Conference on Neural Information Processing Systems, 2012."},{"key":"e_1_3_2_1_40_1","volume-title":"ImageNet classification with deep convolutional neural networks,\" in International Conference on Neural Information Processing Systems","author":"Krizhevsky A.","year":"2012","unstructured":"A. Krizhevsky , I. Sutskever , and G. E. Hinton , \" ImageNet classification with deep convolutional neural networks,\" in International Conference on Neural Information Processing Systems , 2012 . A. Krizhevsky, I. Sutskever, and G. E. Hinton, \"ImageNet classification with deep convolutional neural networks,\" in International Conference on Neural Information Processing Systems, 2012."},{"key":"e_1_3_2_1_41_1","first-page":"1","volume-title":"o. M. I. Cao, \"A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution","author":"Zhang Z.","year":"2018","unstructured":"Z. Zhang , X. Liang , X. Dong , Y. Xie , and G. J. I. T. o. M. I. Cao, \"A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution ,\" vol. 37 , pp. 1 -- 1 , 2018 . Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. J. I. T. o. M. I. Cao, \"A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution,\" vol. 37, pp. 1--1, 2018."},{"key":"e_1_3_2_1_42_1","volume-title":"T2 and Proton Density Parameters from Deep Learning,\" arXiv:1806.07453","author":"Qing Lyu G. W.","year":"2018","unstructured":"G. W. Qing Lyu , \"Quantitative MRI-Absolute T1 , T2 and Proton Density Parameters from Deep Learning,\" arXiv:1806.07453 , 2018 . G. W. Qing Lyu, \"Quantitative MRI-Absolute T1, T2 and Proton Density Parameters from Deep Learning,\" arXiv:1806.07453, 2018."},{"key":"e_1_3_2_1_43_1","volume-title":"Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks","author":"Gulcehre C.","year":"2014","unstructured":"C. Gulcehre , K. Cho , R. Pascanu , and Y. Bengio , Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks , 2014 . C. Gulcehre, K. Cho, R. Pascanu, and Y. Bengio, Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks, 2014."},{"key":"e_1_3_2_1_44_1","volume-title":"Rectified linear units improve restricted boltzmann machines,\" in International Conference on International Conference on Machine Learning","author":"Nair V.","year":"2010","unstructured":"V. Nair and G. E. Hinton , \" Rectified linear units improve restricted boltzmann machines,\" in International Conference on International Conference on Machine Learning , 2010 . V. Nair and G. E. Hinton, \"Rectified linear units improve restricted boltzmann machines,\" in International Conference on International Conference on Machine Learning, 2010."},{"key":"e_1_3_2_1_45_1","volume-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","author":"Ioffe S.","year":"2015","unstructured":"S. Ioffe and C. Szegedy , \" Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ,\" 2015 . S. Ioffe and C. Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,\" 2015."},{"key":"e_1_3_2_1_46_1","first-page":"52","volume-title":"i. C. S. Hinton, \"The appeal of parallel distributed processing","author":"Mcclelland J. L.","year":"1988","unstructured":"J. L. Mcclelland , D. E. Rumelhart , and G. E. J. R. i. C. S. Hinton, \"The appeal of parallel distributed processing ,\" vol. 1 , pp. 52 -- 72 , 1988 . J. L. Mcclelland, D. E. Rumelhart, and G. E. J. R. i. C. S. Hinton, \"The appeal of parallel distributed processing,\" vol. 1, pp. 52--72, 1988."},{"key":"e_1_3_2_1_47_1","volume-title":"A Method for Stochastic Optimization","author":"Kingma D. P.","year":"2014","unstructured":"D. P. Kingma and J. J. C. S. Ba , \"Adam : A Method for Stochastic Optimization ,\" 2014 . D. P. Kingma and J. J. C. S. Ba, \"Adam: A Method for Stochastic Optimization,\" 2014."},{"key":"e_1_3_2_1_48_1","volume-title":"TensorFlow: a system for large-scale machine learning","author":"Abadi M.","year":"2016","unstructured":"M. Abadi , P. Barham , J. Chen , Z. Chen , A. Davis , J. Dean , , \" TensorFlow: a system for large-scale machine learning ,\" 2016 . M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, et al., \"TensorFlow: a system for large-scale machine learning,\" 2016."},{"key":"e_1_3_2_1_49_1","first-page":"2814","volume-title":"an in vivo preliminary magnetic resonance study and correlation with clinical score","author":"Juras V.","year":"2013","unstructured":"V. Juras , S. Apprich , P. Szomolanyi , O. Bieri , X. Deligianni , and S. J. E. R. Trattnig , \"Bi-exponential T2* analysis of healthy and diseased Achilles tendons : an in vivo preliminary magnetic resonance study and correlation with clinical score ,\" vol. 23 , pp. 2814 -- 2822 , 2013 . V. Juras, S. Apprich, P. Szomolanyi, O. Bieri, X. Deligianni, and S. J. E. R. Trattnig, \"Bi-exponential T2* analysis of healthy and diseased Achilles tendons: an in vivo preliminary magnetic resonance study and correlation with clinical score,\" vol. 23, pp. 2814--2822, 2013."},{"key":"e_1_3_2_1_50_1","first-page":"114","volume-title":"Single- and Bi-component T2* analysis of tendon before and during tensile loading, using UTE sequences","author":"Chang E. Y.","year":"2015","unstructured":"E. Y. Chang , J. Du , K. Iwasaki , R. Biswas , S. Statum , Q. He , , \" Single- and Bi-component T2* analysis of tendon before and during tensile loading, using UTE sequences ,\" vol. 42 , pp. 114 -- 120 , 2015 . E. Y. Chang, J. Du, K. Iwasaki, R. Biswas, S. Statum, Q. He, et al., \"Single- and Bi-component T2* analysis of tendon before and during tensile loading, using UTE sequences,\" vol. 42, pp. 114--120, 2015."},{"key":"e_1_3_2_1_51_1","first-page":"749","volume-title":"Ultrashort echo time (UTE) imaging with bi-component analysis: Bound and free water evaluation of bovine cortical bone subject to sequential drying","year":"2012","unstructured":"Biswas, Reni, Bae, Won, Diaz, Eric, , \" Ultrashort echo time (UTE) imaging with bi-component analysis: Bound and free water evaluation of bovine cortical bone subject to sequential drying ,\" vol. 50 , pp. 749 -- 755 , 2012 . Biswas, Reni, Bae, Won, Diaz, Eric, et al., \"Ultrashort echo time (UTE) imaging with bi-component analysis: Bound and free water evaluation of bovine cortical bone subject to sequential drying,\" vol. 50, pp. 749--755, 2012."},{"key":"e_1_3_2_1_52_1","first-page":"161","volume-title":"Ultrashort echo time spectroscopic imaging (UTESI): an efficient method for quantifying bound and free water","author":"Diaz E.","year":"2012","unstructured":"E. Diaz , C. B. Chung , W. C. Bae , S. Statum , R. Znamirowski , G. M. Bydder , , \" Ultrashort echo time spectroscopic imaging (UTESI): an efficient method for quantifying bound and free water ,\" vol. 25 , pp. 161 -- 168 , 2012 . E. Diaz, C. B. Chung, W. C. Bae, S. Statum, R. Znamirowski, G. M. Bydder, et al., \"Ultrashort echo time spectroscopic imaging (UTESI): an efficient method for quantifying bound and free water,\" vol. 25, pp. 161--168, 2012."},{"key":"e_1_3_2_1_53_1","volume-title":"i. M. Yan, \"Maximum likelihood estimation of signal amplitude and noise variance from MR data","author":"Sun J.","year":"2010","unstructured":"J. Sun , L. L. Zhang , and C. G. J. M. R. i. M. Yan, \"Maximum likelihood estimation of signal amplitude and noise variance from MR data ,\" vol. 51 , p. 586, 2010 . J. Sun, L. L. Zhang, and C. G. J. M. R. i. M. Yan, \"Maximum likelihood estimation of signal amplitude and noise variance from MR data,\" vol. 51, p. 586, 2010."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"crossref","unstructured":"J. D. O'Sullivan %J Medical Imaging IEEE Transactions on \"A fast sinc function gridding algorithm for fourier inversion in computer tomography \" vol. 4 pp. 200--207 1985.  J. D. O'Sullivan %J Medical Imaging IEEE Transactions on \"A fast sinc function gridding algorithm for fourier inversion in computer tomography \" vol. 4 pp. 200--207 1985.","DOI":"10.1109\/TMI.1985.4307723"}],"event":{"name":"ISICDM 2019: The Third International Symposium on Image Computing and Digital Medicine","sponsor":["Xidian University"],"location":"Xi'an China","acronym":"ISICDM 2019"},"container-title":["Proceedings of the Third International Symposium on Image Computing and Digital Medicine"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3364836.3364904","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3364836.3364904","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:23Z","timestamp":1750204463000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3364836.3364904"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,24]]},"references-count":54,"alternative-id":["10.1145\/3364836.3364904","10.1145\/3364836"],"URL":"https:\/\/doi.org\/10.1145\/3364836.3364904","relation":{},"subject":[],"published":{"date-parts":[[2019,8,24]]},"assertion":[{"value":"2019-08-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}