{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T18:47:55Z","timestamp":1793731675823,"version":"4.6.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071913"],"award-info":[{"award-number":["82071913"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22161142024"],"award-info":[{"award-number":["22161142024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1805261"],"award-info":[{"award-number":["U1805261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11775184"],"award-info":[{"award-number":["11775184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"DOI":"10.1007\/s00330-023-09417-2","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T11:03:58Z","timestamp":1674558238000},"page":"4938-4948","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction"],"prefix":"10.1007","volume":"33","author":[{"given":"Xi","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixing","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinqin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qizhi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjian","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingceng","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingliang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0600-8594","authenticated-orcid":false,"given":"Congbo","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhui","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhui","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"key":"9417_CR1","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1007\/s00330-015-3994-7","volume":"26","author":"JH Yoon","year":"2016","unstructured":"Yoon JH, Lee JM, Paek M, Han JK, Choi BI (2016) Quantitative assessment of hepatic function: modified look-locker inversion recovery (MOLLI) sequence for T1 mapping on Gd-EOB-DTPA-enhanced liver MR imaging. Eur Radiol 26:1775\u20131782","journal-title":"Eur Radiol"},{"key":"9417_CR2","doi-asserted-by":"publisher","first-page":"2478","DOI":"10.1007\/s00330-012-2506-2","volume":"22","author":"B Henninger","year":"2012","unstructured":"Henninger B, Kremser C, Rauch S et al (2012) Evaluation of MR imaging with T1 and T2* mapping for the determination of hepatic iron overload. Eur Radiol 22:2478\u20132486","journal-title":"Eur Radiol"},{"key":"9417_CR3","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1148\/radiol.211180","volume":"303","author":"D Wu","year":"2022","unstructured":"Wu D, Jiang K, Li H et al (2022) Time-dependent diffusion MRI for quantitative microstructural mapping of prostate cancer. Radiology 303:578\u2013587","journal-title":"Radiology"},{"key":"9417_CR4","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1002\/jmri.22184","volume":"31","author":"YE Chung","year":"2010","unstructured":"Chung YE, Park MS, Kim MS et al (2010) Quantification of superparamagnetic iron oxide-mediated signal intensity change in patients with liver cirrhosis using T2 and T2* mapping: a preliminary report. J Magn Reson Imaging 31:1379\u20131386","journal-title":"J Magn Reson Imaging"},{"key":"9417_CR5","doi-asserted-by":"publisher","first-page":"8376","DOI":"10.1007\/s00330-021-07805-0","volume":"31","author":"W Yang","year":"2021","unstructured":"Yang W, Kim JE, Choi HC et al (2021) T2 mapping in gadoxetic acid-enhanced MRI: utility for predicting decompensation and death in cirrhosis. Eur Radiol 31:8376\u20138387","journal-title":"Eur Radiol"},{"key":"9417_CR6","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1111\/j.1365-2141.1990.tb02596.x","volume":"74","author":"JP Kaltwasser","year":"1990","unstructured":"Kaltwasser JP, Gottschalk R, Schalk KP, Hartl W (1990) Non-invasive quantitation of liver iron-overload by magnetic resonance imaging. Br J Haematol 74:360\u2013363","journal-title":"Br J Haematol"},{"key":"9417_CR7","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1002\/jmri.20758","volume":"24","author":"SW Farraher","year":"2006","unstructured":"Farraher SW, Jara H, Chang KJ, Ozonoff A, Soto JA (2006) Differentiation of hepatocellular carcinoma and hepatic metastasis from cysts and hemangiomas with calculated T2 relaxation times and the T1\/T2 relaxation times ratio. J Magn Reson Imaging 24:1333\u20131341","journal-title":"J Magn Reson Imaging"},{"key":"9417_CR8","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1007\/s00330-012-2519-x","volume":"22","author":"A Cieszanowski","year":"2012","unstructured":"Cieszanowski A, Anysz-Grodzicka A, Szeszkowski W et al (2012) Characterization of focal liver lesions using quantitative techniques: comparison of apparent diffusion coefficient values and T2 relaxation times. Eur Radiol 22:2514\u20132524","journal-title":"Eur Radiol"},{"key":"9417_CR9","doi-asserted-by":"publisher","first-page":"3728","DOI":"10.1007\/s00330-015-4146-9","volume":"26","author":"YS Kim","year":"2016","unstructured":"Kim YS, Song JS, Lee HK, Han YM (2016) Hypovascular hypointense nodules on hepatobiliary phase without T2 hyperintensity on gadoxetic acid-enhanced MR images in patients with chronic liver disease: long-term outcomes and risk factors for hypervascular transformation. Eur Radiol 26:3728\u20133736","journal-title":"Eur Radiol"},{"key":"9417_CR10","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1007\/s00330-002-1366-6","volume":"12","author":"A Cieszanowski","year":"2002","unstructured":"Cieszanowski A, Szeszkowski W, Golebiowski M, Bielecki DK, Grodzicki M, Pruszynski B (2002) Discrimination of benign from malignant hepatic lesions based on their T2-relaxation times calculated from moderately T2-weighted turbo SE sequence. Eur Radiol 12:2273\u20132279","journal-title":"Eur Radiol"},{"key":"9417_CR11","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1007\/s00259-018-4084-2","volume":"45","author":"OA Catalano","year":"2018","unstructured":"Catalano OA, Umutlu L, Fuin N et al (2018) Comparison of the clinical performance of upper abdominal PET\/DCE-MRI with and without concurrent respiratory motion correction (MoCo). Eur J Nucl Med Mol Imaging 45:2147\u20132154","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"9417_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08439-y","author":"D Bencikova","year":"2021","unstructured":"Bencikova D, Han F, Kannengieser S et al (2021) Evaluation of a single-breath-hold radial turbo-spin-echo sequence for T2 mapping of the liver at 3T. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-021-08439-y","journal-title":"Eur Radiol"},{"key":"9417_CR13","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.2214\/ajr.160.5.8470568","volume":"160","author":"MA Goldberg","year":"1993","unstructured":"Goldberg MA, Hahn PF, Saini S et al (1993) Value of T1 and T2 relaxation times from echoplanar MR imaging in the characterization of focal hepatic lesions. AJR Am J Roentgenol 160:1011\u20131017","journal-title":"AJR Am J Roentgenol"},{"key":"9417_CR14","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1148\/radiol.2261011904","volume":"226","author":"B Taouli","year":"2003","unstructured":"Taouli B, Vilgrain V, Dumont E, Daire JL, Fan B, Menu Y (2003) Evaluation of liver diffusion isotropy and characterization of focal hepatic lesions with two single-shot echo-planar MR imaging sequences: prospective study in 66 patients. Radiology 226:71\u201378","journal-title":"Radiology"},{"key":"9417_CR15","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1002\/jmri.25972","volume":"48","author":"T Hilbert","year":"2018","unstructured":"Hilbert T, Sumpf TJ, Weiland E et al (2018) Accelerated T(2) mapping combining parallel MRI and model-based reconstruction: GRAPPATINI. J Magn Reson Imaging 48:359\u2013368","journal-title":"J Magn Reson Imaging"},{"key":"9417_CR16","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1002\/jmri.26612","volume":"50","author":"N Vietti Violi","year":"2019","unstructured":"Vietti Violi N, Hilbert T, Bastiaansen JAM et al (2019) Patient respiratory-triggered quantitative T(2) mapping in the pancreas. J Magn Reson Imaging 50:410\u2013416","journal-title":"J Magn Reson Imaging"},{"key":"9417_CR17","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1002\/jmri.27673","volume":"54","author":"SS Huang","year":"2021","unstructured":"Huang SS, Boyacioglu R, Bolding R, Macaskill C, Chen Y, Griswold MA (2021) Free-breathing abdominal magnetic resonance fingerprinting using a pilot tone navigator. J Magn Reson Imaging 54:1138\u20131151","journal-title":"J Magn Reson Imaging"},{"key":"9417_CR18","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1002\/mrm.20809","volume":"55","author":"RW Fischer","year":"2006","unstructured":"Fischer RW, Botnar RM, Nehrke K, Boesiger P, Manning WJ, Peters DC (2006) Analysis of residual coronary artery motion for breath hold and navigator approaches using real-time coronary MRI. Magn Reson Med 55:612\u2013618","journal-title":"Magn Reson Med"},{"key":"9417_CR19","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1038\/s41551-018-0217-y","volume":"2","author":"AG Christodoulou","year":"2018","unstructured":"Christodoulou AG, Shaw JL, Nguyen C et al (2018) Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging. Nat Biomed Eng 2:215\u2013226","journal-title":"Nat Biomed Eng"},{"key":"9417_CR20","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1002\/mrm.28970","volume":"87","author":"N Wang","year":"2022","unstructured":"Wang N, Cao T, Han F et al (2022) Free-breathing multitasking multi-echo MRI for whole-liver water-specific T(1), proton density fat fraction, and R2\u2217 quantification. Magn Reson Med 87:120\u2013137","journal-title":"Magn Reson Med"},{"key":"9417_CR21","doi-asserted-by":"publisher","first-page":"2450","DOI":"10.1002\/mrm.27574","volume":"81","author":"JL Shaw","year":"2019","unstructured":"Shaw JL, Yang Q, Zhou Z et al (2019) Free-breathing, non-ECG, continuous myocardial T(1) mapping with cardiovascular magnetic resonance multitasking. Magn Reson Med 81:2450\u20132463","journal-title":"Magn Reson Med"},{"key":"9417_CR22","doi-asserted-by":"publisher","first-page":"2202","DOI":"10.1002\/mrm.27205","volume":"80","author":"C Cai","year":"2018","unstructured":"Cai C, Wang C, Zeng Y et al (2018) Single-shot T(2) mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network. Magn Reson Med 80:2202\u20132214","journal-title":"Magn Reson Med"},{"key":"9417_CR23","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1109\/TMI.2019.2896085","volume":"38","author":"J Zhang","year":"2019","unstructured":"Zhang J, Wu J, Chen S et al (2019) Robust single-shot T(2) mapping via multiple overlapping-echo acquisition and deep neural network. IEEE Trans Med Imaging 38:1801\u20131811","journal-title":"IEEE Trans Med Imaging"},{"key":"9417_CR24","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29128","author":"S Li","year":"2022","unstructured":"Li S, Wu J, Ma L, Cai S, Cai C (2022) A simultaneous multi-slice T(2) mapping framework based on overlapping-echo detachment planar imaging and deep learning reconstruction. Magn Reson Med. https:\/\/doi.org\/10.1002\/mrm.29128","journal-title":"Magn Reson Med"},{"key":"9417_CR25","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1002\/mrm.27023","volume":"80","author":"L Ma","year":"2018","unstructured":"Ma L, Cai C, Yang H et al (2018) Motion-tolerant diffusion mapping based on single-shot overlapping-echo detachment (OLED) planar imaging. Magn Reson Med 80:200\u2013210","journal-title":"Magn Reson Med"},{"key":"9417_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3179981","author":"Q Yang","year":"2022","unstructured":"Yang Q, Lin Y, Wang J et al (2022) MOdel-based SyntheTic Data-driven Learning (MOST-DL): application in single-shot T2 mapping with severe head motion using overlapping-echo acquisition. IEEE Trans Med Imaging. https:\/\/doi.org\/10.1109\/TMI.2022.3179981","journal-title":"IEEE Trans Med Imaging"},{"key":"9417_CR27","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15820","author":"B Ouyang","year":"2022","unstructured":"Ouyang B, Yang Q, Wang X et al (2022) Single-shot T(2) mapping via multi-echo-train multiple overlapping-echo detachment planar imaging and multitask deep learning. Med Phys. https:\/\/doi.org\/10.1002\/mp.15820","journal-title":"Med Phys"},{"key":"9417_CR28","first-page":"514","volume":"2016","author":"S Wang","year":"2016","unstructured":"Wang S, Su Z, Ying L et al (2016) Accelerating magnetic resonance imaging via deep learning. Proc IEEE Int Symp Biomed Imaging 2016:514\u2013517","journal-title":"Proc IEEE Int Symp Biomed Imaging"},{"key":"9417_CR29","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","volume":"146","author":"J Kawahara","year":"2017","unstructured":"Kawahara J, Brown CJ, Miller SP et al (2017) BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. Neuroimage 146:1038\u20131049","journal-title":"Neuroimage"},{"key":"9417_CR30","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1007\/s00429-021-02303-x","volume":"226","author":"H Li","year":"2021","unstructured":"Li H, Yan G, Luo W et al (2021) Mapping fetal brain development based on automated segmentation and 4D brain atlasing. Brain Struct Funct 226:1961\u20131972","journal-title":"Brain Struct Funct"},{"key":"9417_CR31","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.mri.2020.09.021","volume":"74","author":"F Liu","year":"2020","unstructured":"Liu F, Kijowski R, Feng L, El Fakhri G (2020) High-performance rapid MR parameter mapping using model-based deep adversarial learning. Magn Reson Imaging 74:152\u2013160","journal-title":"Magn Reson Imaging"},{"key":"9417_CR32","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2016.IMMQW8UQ","author":"BJ Erickson","year":"2016","unstructured":"Erickson BJ, Kirk S, Lee Y, Bathe O, Kearns M, Gerdes C (2016) Radiology data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma [TCGA-LIHC] collection. The Cancer Imaging Archive. https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.IMMQW8UQ","journal-title":"The Cancer Imaging Archive"},{"key":"9417_CR33","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu J, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks.\u00a02017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"key":"9417_CR34","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1109\/TMI.2019.2901750","volume":"38","author":"SU Dar","year":"2019","unstructured":"Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, Cukur T (2019) Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans Med Imaging 38:2375\u20132388","journal-title":"IEEE Trans Med Imaging"},{"key":"9417_CR35","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1109\/TMI.2016.2620961","volume":"36","author":"F Liu","year":"2017","unstructured":"Liu F, Velikina JV, Block WF, Kijowski R, Samsonov AA (2017) Fast realistic MRI simulations based on generalized multi-pool exchange tissue model. IEEE Trans Med Imaging 36:527\u2013537","journal-title":"IEEE Trans Med Imaging"},{"key":"9417_CR36","first-page":"234","volume-title":"U-Net: convolutional networks for biomedical image segmentation","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Springer International Publishing, Cham, pp 234\u2013241"},{"key":"9417_CR37","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations (ICLR 2015), 1\u201314"},{"key":"9417_CR38","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/TPAMI.2020.3012103","volume":"44","author":"Gd Maggiora","year":"2022","unstructured":"Maggiora Gd, Castillo-Passi C, Qiu W et al (2022) DeepSPIO: Super paramagnetic iron oxide particle quantification using deep learning in magnetic resonance imaging. IEEE Trans Pattern Anal Mach Intell 44:143\u2013153","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9417_CR39","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1002\/mrm.27545","volume":"81","author":"WC Lo","year":"2019","unstructured":"Lo WC, Chen Y, Jiang Y et al (2019) Realistic 4D MRI abdominal phantom for the evaluation and comparison of acquisition and reconstruction techniques. Magn Reson Med 81:1863\u20131875","journal-title":"Magn Reson Med"},{"key":"9417_CR40","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","volume":"32","author":"E Tjoa","year":"2021","unstructured":"Tjoa E, Guan C (2021) A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Transactions on Neural Networks and Learning Systems 32:4793\u20134813","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"9417_CR41","doi-asserted-by":"crossref","unstructured":"Singh A, Sengupta S, Lakshminarayanan V (2020) Explainable deep learning models in medical image analysis.\u00a0J Imaging\u00a06:52","DOI":"10.3390\/jimaging6060052"},{"key":"9417_CR42","doi-asserted-by":"crossref","unstructured":"Huff DT, Weisman AJ, Jeraj R (2021) Interpretation and visualization techniques for deep learning models in medical imaging.\u00a0Phys Med Biol\u00a066:04TR01","DOI":"10.1088\/1361-6560\/abcd17"},{"key":"9417_CR43","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.neucom.2019.04.093","volume":"361","author":"F Zhang","year":"2019","unstructured":"Zhang F, Li Z, Zhang B, Du H, Wang B, Zhang X (2019) Multi-modal deep learning model for auxiliary diagnosis of Alzheimer\u2019s disease. Neurocomputing 361:185\u2013195","journal-title":"Neurocomputing"},{"key":"9417_CR44","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-030-02628-8_13","volume-title":"Understanding and interpreting machine learning in medical image computing applications","author":"P Van Molle","year":"2018","unstructured":"Van Molle P, De Strooper M, Verbelen T, Vankeirsbilck B, Simoens P, Dhoedt B (2018) Visualizing convolutional neural networks to improve decision support for skin lesion classification. In: Stoyanov D, Taylor Z, Kia SM et al (eds) Understanding and interpreting machine learning in medical image computing applications. Springer International Publishing, Cham, pp 115\u2013123"},{"key":"9417_CR45","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1148\/radiol.2303021331","volume":"230","author":"CM de Bazelaire","year":"2004","unstructured":"de Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC (2004) MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology 230:652\u2013659","journal-title":"Radiology"},{"key":"9417_CR46","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1002\/mrm.22203","volume":"63","author":"CL Hoad","year":"2010","unstructured":"Hoad CL, Cox EF, Gowland PA (2010) Quantification of T2 in the abdomen at 3.0 T using a T2 -prepared balanced turbo field echo sequence. Magn Reson Med 63:356\u2013364","journal-title":"Magn Reson Med"},{"key":"9417_CR47","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-319-12715-6_13","volume-title":"Neuroimaging pharmacopoeia","author":"H Haroyan","year":"2015","unstructured":"Haroyan H, Ginat DT (2015) Gadolinium-based contrast agents. In: Ginat DT, Small JE, Schaefer PW (eds) Neuroimaging pharmacopoeia. Springer International Publishing, Cham, pp 105\u2013110"},{"key":"9417_CR48","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1097\/00004728-200003000-00004","volume":"24","author":"HM Fenlon","year":"2000","unstructured":"Fenlon HM, Tello R, deCarvalho VL, Yucel EK (2000) Signal characteristics of focal liver lesions on double echo T2-weighted conventional spin echo MRI: observer performance versus quantitative measurements of T2 relaxation times. J Comput Assist Tomogr 24:204\u2013211","journal-title":"J Comput Assist Tomogr"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-09417-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-023-09417-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-09417-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T22:15:31Z","timestamp":1687558531000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-023-09417-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,24]]},"references-count":48,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["9417"],"URL":"https:\/\/doi.org\/10.1007\/s00330-023-09417-2","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,24]]},"assertion":[{"value":"12 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The scientific guarantor of this publication is Professor Congbo Cai.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was obtained from all subjects (patients) in this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"\u2022 retrospective\u2022 experimental\u2022 multicenter study","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}