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For: Rutkowski DR, Roldán-Alzate A, Johnson KM. Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data. Sci Rep 2021;11:10240. [PMID: 33986368 DOI: 10.1038/s41598-021-89636-z] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Kim D, Jen ML, Eisenmenger LB, Johnson KM. Accelerated 4D-flow MRI with 3-point encoding enabled by machine learning. Magn Reson Med 2023;89:800-11. [PMID: 36198027 DOI: 10.1002/mrm.29469] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Moradi H, Al-hourani A, Concilia G, Khoshmanesh F, Nezami FR, Needham S, Baratchi S, Khoshmanesh K. Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning. Biophys Rev 2023. [DOI: 10.1007/s12551-022-01040-7] [Reference Citation Analysis]
3 Peper ES, van Ooij P, Jung B, Huber A, Gräni C, Bastiaansen JAM. Advances in machine learning applications for cardiovascular 4D flow MRI. Front Cardiovasc Med 2022;9:1052068. [PMID: 36568555 DOI: 10.3389/fcvm.2022.1052068] [Reference Citation Analysis]
4 Gholampour S, Frim D, Yamini B. Long-term recovery behavior of brain tissue in hydrocephalus patients after shunting. Commun Biol 2022;5:1198. [DOI: 10.1038/s42003-022-04128-8] [Reference Citation Analysis]
5 Panchigar D, Kar K, Shukla S, Mathew RM, Chadha U, Selvaraj SK. Machine learning-based CFD simulations: a review, models, open threats, and future tactics. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07838-6] [Reference Citation Analysis]
6 Oechtering TH, Roberts GS, Panagiotopoulos N, Wieben O, Roldán-Alzate A, Reeder SB. Abdominal applications of quantitative 4D flow MRI. Abdom Radiol (NY) 2022;47:3229-50. [PMID: 34837521 DOI: 10.1007/s00261-021-03352-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Shit S, Zimmermann J, Ezhov I, Paetzold JC, Sanches AF, Pirkl C, Menze BH. SRflow: Deep learning based super-resolution of 4D-flow MRI data. Front Artif Intell 2022;5. [DOI: 10.3389/frai.2022.928181] [Reference Citation Analysis]
8 Gholampour S, Yamini B, Droessler J, Frim D. A New Definition for Intracranial Compliance to Evaluate Adult Hydrocephalus After Shunting. Front Bioeng Biotechnol 2022;10:900644. [DOI: 10.3389/fbioe.2022.900644] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
9 Terekhov KM. Presure Boundary Conditions in the Collocated Finite-Volume Method for the Steady Navier–Stokes Equations. Comput Math and Math Phys 2022;62:1345-55. [DOI: 10.1134/s0965542522080139] [Reference Citation Analysis]
10 Taebi A. Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances. Fluids 2022;7:197. [DOI: 10.3390/fluids7060197] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
11 He Y, Northrup H, Le H, Cheung AK, Berceli SA, Shiu YT. Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases. Front Bioeng Biotechnol 2022;10:855791. [DOI: 10.3389/fbioe.2022.855791] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Gozawa M, Watanabe N, Iwasaki K, Takamura Y, Inatani M. Application of moving particle semi-implicit (MPS) method on retro-oil fluid using three-dimensional vitreous cavity models from magnetic resonance imaging. Sci Rep 2022;12:1735. [PMID: 35110656 DOI: 10.1038/s41598-022-05886-5] [Reference Citation Analysis]
13 Roldán-Alzate A, Grist TM. Deep Learning for Optimization of Abdominopelvic 4D Flow MRI Analysis. Radiology 2021;:212702. [PMID: 34846210 DOI: 10.1148/radiol.212702] [Reference Citation Analysis]