{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T04:45:13Z","timestamp":1763700313826,"version":"3.45.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"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":["Magn Reson Mater Phy"],"DOI":"10.1007\/s10334-025-01268-w","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T08:17:18Z","timestamp":1751530638000},"page":"959-977","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fat\u2013water MRI separation using deep complex convolution network"],"prefix":"10.1007","volume":"38","author":[{"given":"Moorthy","family":"Ganeshkumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Devasenathipathy","family":"Kandasamy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raju","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6477-2462","authenticated-orcid":false,"given":"Amit","family":"Mehndiratta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"issue":"4","key":"1268_CR1","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1002\/jmri.22580","volume":"34","author":"SB Reeder","year":"2011","unstructured":"Reeder SB, Cruite I, Hamilton G, Sirlin CB (2011) Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging 34(4):729\u2013749. https:\/\/doi.org\/10.1002\/jmri.22580","journal-title":"J Magn Reson Imaging"},{"issue":"5","key":"1268_CR2","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1002\/mrm.21737","volume":"60","author":"H Yu","year":"2008","unstructured":"Yu H, Shimakawa A, McKenzie CA, Brodsky E, Brittain JH, Reeder SB (2008) Multiecho water\u2013fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling. Magn Reson Med 60(5):1122\u20131134. https:\/\/doi.org\/10.1002\/mrm.21737","journal-title":"Magn Reson Med"},{"key":"1268_CR3","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.pnmrs.2013.04.001","volume":"73","author":"EL Thomas","year":"2013","unstructured":"Thomas EL, Fitzpatrick JA, Malik SJ, Taylor-Robinson SD, Bell JD (2013) Whole body fat: content and distribution. Prog Nucl Magn Reson Spectrosc 73:56\u201380. https:\/\/doi.org\/10.1016\/j.pnmrs.2013.04.001","journal-title":"Prog Nucl Magn Reson Spectrosc"},{"issue":"3","key":"1268_CR4","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1002\/mrm.1910030310","volume":"3","author":"HW Park","year":"1986","unstructured":"Park HW, Cho ZH (1986) High-resolution human in vivo spectroscopic imaging using echo- time encoding technique. Magn Reson Med 3(3):448\u2013453. https:\/\/doi.org\/10.1002\/mrm.1910030310","journal-title":"Magn Reson Med"},{"issue":"1","key":"1268_CR5","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1148\/radiology.153.1.6089263","volume":"153","author":"WT Dixon","year":"1984","unstructured":"Dixon WT (1984) Simple proton spectroscopic imaging. Radiology 153(1):189\u2013194. https:\/\/doi.org\/10.1148\/radiology.153.1.6089263","journal-title":"Radiology"},{"issue":"1","key":"1268_CR6","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/mrm.24441","volume":"70","author":"J Tsao","year":"2013","unstructured":"Tsao J, Jiang Y (2013) Hierarchical IDEAL: fast, robust, and multiresolution separation of multiple chemical species from multiple echo times. Magn Reson Med 70(1):155\u2013159. https:\/\/doi.org\/10.1002\/mrm.24441","journal-title":"Magn Reson Med"},{"issue":"5","key":"1268_CR7","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1002\/mrm.25054","volume":"72","author":"X Zhong","year":"2014","unstructured":"Zhong X, Nickel MD, Kannengiesser SAR, Dale BM, Kiefer B, Bashir MR (2014) Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging: liver fat quantification with adaptive fitting. Magn Reson Med 72(5):1353\u20131365. https:\/\/doi.org\/10.1002\/mrm.25054","journal-title":"Magn Reson Med"},{"issue":"3","key":"1268_CR8","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1002\/mrm.27097","volume":"80","author":"MN Diefenbach","year":"2018","unstructured":"Diefenbach MN, Ruschke S, Eggers H, Meineke J, Rummeny EJ, Karampinos DC (2018) Improving chemical shift encoding-based water\u2013fat separation based on a detailed consideration of magnetic field contributions. Magn Reson Med 80(3):990\u20131004. https:\/\/doi.org\/10.1002\/mrm.27097","journal-title":"Magn Reson Med"},{"key":"1268_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22177","author":"D Hernando","year":"2009","unstructured":"Hernando D, Kellman P, Haldar JP, Liang Z-P (2009) Robust water\/fat separation in the presence of large field inhomogeneities using a graph cut algorithm. Magn Reson Med. https:\/\/doi.org\/10.1002\/mrm.22177","journal-title":"Magn Reson Med"},{"issue":"2","key":"1268_CR10","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TMI.2014.2361764","volume":"34","author":"J Dong","year":"2015","unstructured":"Dong J et al (2015) Simultaneous phase unwrapping and removal of chemical shift (SPURS) using graph cuts: application in quantitative susceptibility mapping. IEEE Trans Med Imaging 34(2):531\u2013540. https:\/\/doi.org\/10.1109\/TMI.2014.2361764","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"1268_CR11","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1002\/mrm.10675","volume":"51","author":"SB Reeder","year":"2004","unstructured":"Reeder SB et al (2004) Multicoil Dixon chemical species separation with an iterative least-squares estimation method. Magn Reson Med 51(1):35\u201345. https:\/\/doi.org\/10.1002\/mrm.10675","journal-title":"Magn Reson Med"},{"issue":"2","key":"1268_CR12","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1002\/mrm.25163","volume":"73","author":"SD Sharma","year":"2015","unstructured":"Sharma SD, Artz NS, Hernando D, Horng DE, Reeder SB (2015) Improving chemical shift encoded water\u2013fat separation using object-based information of the magnetic field inhomogeneity. Magn Reson Med 73(2):597\u2013604. https:\/\/doi.org\/10.1002\/mrm.25163","journal-title":"Magn Reson Med"},{"issue":"2","key":"1268_CR13","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1002\/jmri.26658","volume":"50","author":"JW Goldfarb","year":"2019","unstructured":"Goldfarb JW, Craft J, Cao JJ (2019) Water\u2013fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network: MR water\u2013fat separation. J Magn Reson Imaging 50(2):655\u2013665. https:\/\/doi.org\/10.1002\/jmri.26658","journal-title":"J Magn Reson Imaging"},{"issue":"6088","key":"1268_CR14","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536. https:\/\/doi.org\/10.1038\/323533a0","journal-title":"Nature"},{"key":"1268_CR15","doi-asserted-by":"publisher","unstructured":"Shih S-F, Kafali SG, Armstrong T, Zhong X, Calkins KL, Wu HH (2021) Deep learning-based parameter mapping with uncertainty estimation for fat quantification using accelerated free-breathing radial MRI. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI), Nice, France: IEEE, Apr 2021, pp 433\u2013437. https:\/\/doi.org\/10.1109\/ISBI48211.2021.9433938","DOI":"10.1109\/ISBI48211.2021.9433938"},{"issue":"1","key":"1268_CR16","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1002\/mrm.27697","volume":"82","author":"J Cho","year":"2019","unstructured":"Cho J, Park H (2019) Robust water\u2013fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network. Magn Reson Med 82(1):476\u2013484. https:\/\/doi.org\/10.1002\/mrm.27697","journal-title":"Magn Reson Med"},{"key":"1268_CR17","doi-asserted-by":"publisher","unstructured":"Shen C, She H, Du Y (Nov 2020) Improved robustness in water\u2013fat separation in MRI using conditional adversarial networks. In: 2020 7th international conference on biomedical and bioinformatics engineering. ACM, Kyoto, Japan, pp 31\u201335. https:\/\/doi.org\/10.1145\/3444884.3444891","DOI":"10.1145\/3444884.3444891"},{"issue":"3","key":"1268_CR18","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1002\/mrm.27786","volume":"82","author":"J Andersson","year":"2019","unstructured":"Andersson J, Ahlstr\u00f6m H, Kullberg J (2019) Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks. Magn Reson Med 82(3):1177\u20131186. https:\/\/doi.org\/10.1002\/mrm.27786","journal-title":"Magn Reson Med"},{"issue":"1","key":"1268_CR19","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abb214","volume":"2","author":"X Liang","year":"2021","unstructured":"Liang X, Nguyen D, Jiang SB (2021) Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion. Mach Learn Sci Technol 2(1):015007. https:\/\/doi.org\/10.1088\/2632-2153\/abb214","journal-title":"Mach Learn Sci Technol"},{"issue":"1","key":"1268_CR20","doi-asserted-by":"publisher","first-page":"6102","DOI":"10.1038\/s41598-021-85454-5","volume":"11","author":"J Krois","year":"2021","unstructured":"Krois J et al (2021) Generalizability of deep learning models for dental image analysis. Sci Rep 11(1):6102. https:\/\/doi.org\/10.1038\/s41598-021-85454-5","journal-title":"Sci Rep"},{"issue":"4","key":"1268_CR21","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1002\/mrm.28546","volume":"85","author":"R Jafari","year":"2021","unstructured":"Jafari R et al (2021) Deep neural network for water\/fat separation: supervised training, unsupervised training, and no training. Magn Reson Med 85(4):2263\u20132277. https:\/\/doi.org\/10.1002\/mrm.28546","journal-title":"Magn Reson Med"},{"issue":"2","key":"1268_CR22","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1002\/mrm.28733","volume":"86","author":"E Cole","year":"2021","unstructured":"Cole E, Cheng J, Pauly J, Vasanawala S (2021) Analysis of deep complex-valued convolutional neural networks for MRI reconstruction and phase-focused applications. Magn Reson Med 86(2):1093\u20131109. https:\/\/doi.org\/10.1002\/mrm.28733","journal-title":"Magn Reson Med"},{"issue":"2","key":"1268_CR23","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1002\/mrm.24369","volume":"68","author":"HH Hu","year":"2012","unstructured":"Hu HH et al (2012) ISMRM workshop on fat\u2013water separation: insights, applications and progress in MRI. Magn Reson Med 68(2):378\u2013388. https:\/\/doi.org\/10.1002\/mrm.24369","journal-title":"Magn Reson Med"},{"issue":"9","key":"1268_CR24","doi-asserted-by":"publisher","first-page":"6557","DOI":"10.1007\/s00330-023-09576-2","volume":"33","author":"JP Meneses","year":"2023","unstructured":"Meneses JP et al (2023) Liver PDFF estimation using a multi-decoder water\u2013fat separation neural network with a reduced number of echoes. Eur Radiol 33(9):6557\u20136568. https:\/\/doi.org\/10.1007\/s00330-023-09576-2","journal-title":"Eur Radiol"},{"key":"1268_CR25","unstructured":"Soliman AS (2014) Acquisition and reconstruction techniques for fat quantification using magnetic resonance imaging. The University of Western Ontario, Canada"},{"key":"1268_CR26","unstructured":"Kingma DP, Ba J (2024) Adam: a method for stochastic optimization. Jan 29, 2017. arXiv:1412.6980. Accessed: Jan 04, 2024 (Online). Available: http:\/\/arxiv.org\/abs\/1412.6980"},{"issue":"4","key":"1268_CR27","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"1268_CR28","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1002\/jmri.24568","volume":"40","author":"H Eggers","year":"2014","unstructured":"Eggers H, B\u00f6rnert P (2014) Chemical shift encoding-based water\u2013fat separation methods. J Magn Reson Imaging 40(2):251\u2013268. https:\/\/doi.org\/10.1002\/jmri.24568","journal-title":"J Magn Reson Imaging"},{"issue":"3","key":"1268_CR29","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1002\/mrm.23044","volume":"67","author":"D Hernando","year":"2012","unstructured":"Hernando D, Hines CDG, Yu H, Reeder SB (2012) Addressing phase errors in fat\u2013water imaging using a mixed magnitude\/complex fitting method. Magn Reson Med 67(3):638\u2013644. https:\/\/doi.org\/10.1002\/mrm.23044","journal-title":"Magn Reson Med"},{"issue":"1","key":"1268_CR30","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1002\/mrm.22840","volume":"66","author":"H Yu","year":"2011","unstructured":"Yu H et al (2011) Combination of complex-based and magnitude-based multiecho water\u2013fat separation for accurate quantification of fat-fraction. Magn Reson Med 66(1):199\u2013206. https:\/\/doi.org\/10.1002\/mrm.22840","journal-title":"Magn Reson Med"},{"issue":"4","key":"1268_CR31","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1002\/mrm.20654","volume":"54","author":"H Yu","year":"2005","unstructured":"Yu H, Reeder SB, Shimakawa A, Brittain JH, Pelc NJ (2005) Field map estimation with a region growing scheme for iterative 3-point water\u2013fat decomposition. Magn Reson Med 54(4):1032\u20131039. https:\/\/doi.org\/10.1002\/mrm.20654","journal-title":"Magn Reson Med"},{"issue":"4","key":"1268_CR32","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1002\/jmri.21090","volume":"26","author":"H Yu","year":"2007","unstructured":"Yu H et al (2007) Multiecho reconstruction for simultaneous water\u2013fat decomposition and T2* estimation. J Magn Reson Imaging 26(4):1153\u20131161. https:\/\/doi.org\/10.1002\/jmri.21090","journal-title":"J Magn Reson Imaging"},{"issue":"6","key":"1268_CR33","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1002\/mrm.29175","volume":"87","author":"NT Roberts","year":"2022","unstructured":"Roberts NT, Hernando D, Panagiotopoulos N, Reeder SB (2022) Addressing concomitant gradient phase errors in time-interleaved chemical shift-encoded MRI fat fraction and R2* mapping with a pass-specific phase fitting method. Magn Reson Med 87(6):2826\u20132838. https:\/\/doi.org\/10.1002\/mrm.29175","journal-title":"Magn Reson Med"},{"issue":"3","key":"1268_CR34","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1002\/mrm.26485","volume":"78","author":"S Ruschke","year":"2017","unstructured":"Ruschke S et al (2017) Correction of phase errors in quantitative water\u2013fat imaging using a monopolar time-interleaved multi-echo gradient echo sequence. Magn Reson Med 78(3):984\u2013996. https:\/\/doi.org\/10.1002\/mrm.26485","journal-title":"Magn Reson Med"},{"key":"1268_CR35","doi-asserted-by":"crossref","unstructured":"Chollet F (July 2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2017.195"},{"key":"1268_CR36","unstructured":"Yaman B, Hosseini SAH, Ak\u00e7akaya M (2021) Zero-shot self-supervised learning for MRI reconstruction. Preprint arXiv:2102.07737"}],"container-title":["Magnetic Resonance Materials in Physics, Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10334-025-01268-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10334-025-01268-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10334-025-01268-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T04:40:19Z","timestamp":1763700019000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10334-025-01268-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,3]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1268"],"URL":"https:\/\/doi.org\/10.1007\/s10334-025-01268-w","relation":{},"ISSN":["1352-8661"],"issn-type":[{"type":"electronic","value":"1352-8661"}],"subject":[],"published":{"date-parts":[[2025,7,3]]},"assertion":[{"value":"10 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trials"}},{"value":"The complete study protocol was explained, and written consent was obtained from all patients and healthy volunteers before participation.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The study protocol was approved by the Institutional Review Board at All India Institute of Medical Sciences\u2014New Delhi, India (Reference No. IEC-316\/15.06.2023, RP-11\/2023).","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}