Idilman IS, Aniktar H, Idilman R et al (2013) Hepatic steatosis: quantification by proton density fat fraction with MR imaging versus liver biopsy. Radiology 267:767–775. https://doi.org/10.1148/radiol.13121360
Gofton C, Upendran Y, Zheng MH, George J (2023) MAFLD: How is it different from NAFLD? Clin Mol Hepatol 29:S17–S31
Yokoo T, Serai SD, Pirasteh A et al (2018) Linearity, bias, and precision of hepatic proton density fat fraction measurements by using MR imaging: a meta-analysis. Radiology 286:486–498. https://doi.org/10.1148/radiol.2017170550
Yokoo T, Bydder M, Hamilton G et al (2009) Diagnostic and fat-grading accuracy of low-flip-angle multiecho gradient recalled echo MR imaging at 1.5 T. Radiology 251:67–76
Article PubMed PubMed Central Google Scholar
Hernando D, Kellman P, Haldar JP, Liang ZP (2010) Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm. Magn Reson Med 63:79–90. https://doi.org/10.1002/mrm.22177
Article PubMed PubMed Central Google Scholar
Daudé P, Roussel T, Troalen T et al (2023) Comparative review of algorithms and methods for chemical‐shift‐encoded quantitative fat‐water imaging. Magn Reson Med. https://doi.org/10.1002/mrm.29860
Hernando D, Sharma SD, Aliyari Ghasabeh M et al (2017) Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5 T and 3 T using a fat–water phantom. Magn Reson Med 77:1516–1524. https://doi.org/10.1002/mrm.26228
Article CAS PubMed Google Scholar
Jafari R, Spincemaille P, Zhang J et al (2021) Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training. Magn Reson Med. https://doi.org/10.1002/mrm.28546
Liu K, Li X, Li Z et al (2020) Robust water–fat separation based on deep learning model exploring multi-echo nature of mGRE. Magn Reson Med. https://doi.org/10.1002/mrm.28586
Andersson J, Ahlström H, Kullberg J (2019) Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks. Magn Reson Med 82:1177–1186. https://doi.org/10.1002/mrm.27786
Article PubMed PubMed Central Google Scholar
Cho JJ, Park HW (2019) Robust water–fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network. Magn Reson Med 82:476–484. https://doi.org/10.1002/mrm.27697
Goldfarb JW, Craft J, Cao JJ (2019) Water–fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network. J Magn Reson Imaging 50:655–665. https://doi.org/10.1002/jmri.26658
Meneses JP, Arrieta C, della Maggiora G et al (2023) Liver PDFF estimation using a multi-decoder water–fat separation neural network with a reduced number of echoes. Eur Radiol. https://doi.org/10.1007/s00330-023-09576-2
Shih SF, Kafali SG, Armstrong T et al (2021) Deep learning-based parameter mapping with uncertainty estimation for fat quantification using accelerated free-breathing radial MRI. In: Proceedings of the international symposium on biomedical imaging. IEEE Computer Society, pp 433–437
Meneses JP, Arrieta C, della Maggiora G et al (2021) Optimal transport driven cycle-consistent generative adversarial network (OT-CycleGAN) for an accurate MR water–fat separation. ISMRM Annual Meeting, London
Weingärtner S, Desmond KL, Obuchowski NA et al (2022) Development, validation, qualification, and dissemination of quantitative MR methods: overview and recommendations by the ISMRM quantitative MR study group. Magn Reson Med 87:1184–1206. https://doi.org/10.1002/mrm.29084
Article CAS PubMed Google Scholar
Knoll F, Hammernik K, Kobler E et al (2019) Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Magn Reson Med 81:116–128. https://doi.org/10.1002/mrm.27355
Eche T, Schwartz LH, Mokrane FZ, Dercle L (2021) Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification. Radiol Artif Intell. https://doi.org/10.1148/ryai.2021210097
Maleki F, Ovens K, Gupta R et al (2023) Generalizability of machine learning models: quantitative evaluation of three methodological pitfalls. Radiol Artif Intell. https://doi.org/10.1148/ryai.220028
Jha A, Kumar A, Pande S et al (2020) MT-UNET: a novel U-Net based multi-task architecture for visual scene understanding. In: 2020 IEEE international conference on image processing (ICIP). IEEE, pp 2191–2195
Lee GW, Kim HK (2020) Multi-task learning U-Net for single-channel speech enhancement and mask-based voice activity detection. Appl Sci. https://doi.org/10.3390/app10093230
Ramachandran P, Bello I, Parmar N et al (2019) Stand-alone self-attention in vision models. Adv Neural Inf Process Syst 32
Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: 36th international conference on machine learning. ICML, pp 12744–12753
Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE international conference on computer vision. IEEE, pp 1510–1519. https://doi.org/10.1109/ICCV.2017.167
Abadi M, Barham P, Chen J et al (2016) TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX symposium on operating systems design and implementation. OSDI, Savannah
Fernández-Verdejo R, Malo-Vintimilla L, Gutiérrez-Pino J et al (2021) Similar metabolic health in overweight/obese individuals with contrasting metabolic flexibility to an oral glucose tolerance test. Front Nutr 8:1–11. https://doi.org/10.3389/fnut.2021.745907
Hernando D, Sharma S, Aliyari M et al (2016) Multi-site fat–water phantom MRI data. Magn Reson Med. 77:1516–1524
Pineda AR, Reeder SB, Wen Z, Pelc NJ (2005) Cramér–Rao bounds for three-point decomposition of water and fat. Magn Reson Med 54:625–635. https://doi.org/10.1002/mrm.20623
Yu H, McKenzie CA, Shimakawa A et al (2007) Multiecho reconstruction for simultaneous water–fat decomposition and T2* estimation. J Magn Reson Imaging 26:1153–1161. https://doi.org/10.1002/jmri.21090
Schneider E, Remer EM, Obuchowski NA et al (2021) Long-term inter-platform reproducibility, bias, and linearity of commercial PDFF MRI methods for fat quantification: a multi-center, multi-vendor phantom study. Eur Radiol 31:7566–7574
Pinyopornpanish K, Tantiworawit A, Leerapun A et al (2023) Secondary iron overload and the liver: a comprehensive review. J Clin Transl Hepatol 11:932–941
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