{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T14:57:19Z","timestamp":1788274639966,"version":"build-2803163510"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["R01 HL11526"],"award-info":[{"award-number":["R01 HL11526"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute of Health","award":["S10 OD0122336"],"award-info":[{"award-number":["S10 OD0122336"]}]},{"DOI":"10.13039\/100018718","name":"Purdue Institute for Integrative Neuroscience, Purdue University","doi-asserted-by":"publisher","award":["Grand Challenges Award"],"award-info":[{"award-number":["Grand Challenges Award"]}],"id":[{"id":"10.13039\/100018718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Biomed Eng"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10439-024-03606-w","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T20:21:23Z","timestamp":1725308483000},"page":"331-347","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks"],"prefix":"10.1007","volume":"53","author":[{"given":"Neal M.","family":"Patel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily R.","family":"Bartusiak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean M.","family":"Rothenberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. J.","family":"Schwichtenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward J.","family":"Delp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5615-3063","authenticated-orcid":false,"given":"Vitaliy L.","family":"Rayz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"on behalf of the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"issue":"9","key":"3606_CR1","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1177\/0271678x20982388","volume":"41","author":"TM Kylkilahti","year":"2021","unstructured":"Kylkilahti, T. M., et al. Achieving brain clearance and preventing neurodegenerative diseases: A glymphatic perspective. Journal of Cerebral Blood Flow & Metabolism. 41(9):2137\u20132149, 2021. https:\/\/doi.org\/10.1177\/0271678x20982388.","journal-title":"Journal of Cerebral Blood Flow & Metabolism"},{"key":"3606_CR2","doi-asserted-by":"publisher","first-page":"103931","DOI":"10.1016\/j.ebiom.2022.103931","volume":"78","author":"AN Bramall","year":"2022","unstructured":"Bramall, A. N., E. S. Anton, K. T. Kahle, and P. E. Fecci. Navigating the ventricles: Novel insights into the pathogenesis of hydrocephalus. eBioMedicine. 78:103931, 2022. https:\/\/doi.org\/10.1016\/j.ebiom.2022.103931.","journal-title":"eBioMedicine"},{"issue":"6","key":"3606_CR3","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1212\/wnl.0000000000003583","volume":"88","author":"H Israelsson","year":"2017","unstructured":"Israelsson, H., et al. Vascular risk factors in INPH. Neurology. 88(6):577\u2013585, 2017. https:\/\/doi.org\/10.1212\/wnl.0000000000003583.","journal-title":"Neurology"},{"key":"3606_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12916-014-0218-y","volume":"12","author":"JT O\u2019Brien","year":"2014","unstructured":"O\u2019Brien, J. T., and H. S. Markus. Vascular risk factors and Alzheimer\u2019s disease. BMC Medicine. 12:1, 2014. https:\/\/doi.org\/10.1186\/s12916-014-0218-y.","journal-title":"BMC Medicine"},{"key":"3606_CR5","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1146\/annurev-bioeng-100219-110055","volume":"22","author":"G Soulat","year":"2020","unstructured":"Soulat, G., P. McCarthy, and M. Markl. 4D Flow with MRI. Annu Rev Biomed Eng. 22:103\u2013126, 2020. https:\/\/doi.org\/10.1146\/annurev-bioeng-100219-110055.","journal-title":"Annu Rev Biomed Eng"},{"issue":"1","key":"3606_CR6","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3174\/ajnr.a1308","volume":"30","author":"S Stoquart-El-Sankari","year":"2009","unstructured":"Stoquart-El-Sankari, S., et al. Phase-Contrast MR Imaging Support for the Diagnosis of Aqueductal Stenosis. American Journal of Neuroradiology. 30(1):209\u2013214, 2009. https:\/\/doi.org\/10.3174\/ajnr.a1308.","journal-title":"American Journal of Neuroradiology"},{"issue":"1","key":"3606_CR7","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/2045-8118-8-12","volume":"8","author":"S El Sankari","year":"2011","unstructured":"El Sankari, S., et al. Cerebrospinal fluid and blood flow in mild cognitive impairment and Alzheimer\u2019s disease: a differential diagnosis from idiopathic normal pressure hydrocephalus. Fluids and Barriers of the CNS. 8(1):12, 2011. https:\/\/doi.org\/10.1186\/2045-8118-8-12.","journal-title":"Fluids and Barriers of the CNS"},{"issue":"8","key":"3606_CR8","doi-asserted-by":"publisher","first-page":"4454","DOI":"10.1007\/s00330-020-06825-6","volume":"30","author":"S Yamada","year":"2020","unstructured":"Yamada, S., et al. Cerebrospinal fluid dynamics in idiopathic normal pressure hydrocephalus on four-dimensional flow imaging. Eur Radiol. 30(8):4454\u20134465, 2020. https:\/\/doi.org\/10.1007\/s00330-020-06825-6.","journal-title":"Eur Radiol"},{"issue":"3","key":"3606_CR9","doi-asserted-by":"publisher","first-page":"479","DOI":"10.3174\/ajnr.A6941","volume":"42","author":"S Yamada","year":"2021","unstructured":"Yamada, S., et al. Quantification of Oscillatory Shear Stress from Reciprocating CSF Motion on 4D Flow Imaging. AJNR Am J Neuroradiol. 42(3):479\u2013486, 2021. https:\/\/doi.org\/10.3174\/ajnr.A6941.","journal-title":"AJNR Am J Neuroradiol"},{"key":"3606_CR10","doi-asserted-by":"publisher","unstructured":"P. D. Tafti, R. Delgado-Gonzalo, A. F. Stalder, and M. Unser, \"Variational enhancement and denoising of flow field images,\" in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 30 March-2 April 2011 2011, pp. 1061-1064, https:\/\/doi.org\/10.1109\/ISBI.2011.5872584.","DOI":"10.1109\/ISBI.2011.5872584"},{"key":"3606_CR11","doi-asserted-by":"publisher","unstructured":"E. Bostan, O. Vardoulis, D. Piccini, P. D. Tafti, N. Stergiopulos, and M. Unser, \"Spatio-temporal regularization of flow-fields,\" in 2013 IEEE 10th International Symposium on Biomedical Imaging, 7-11 April 2013 2013, pp. 836-839, https:\/\/doi.org\/10.1109\/ISBI.2013.6556605.","DOI":"10.1109\/ISBI.2013.6556605"},{"issue":"1","key":"3606_CR12","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1002\/mrm.24221","volume":"69","author":"J Busch","year":"2013","unstructured":"Busch, J., D. Giese, L. Wissmann, and S. Kozerke. Reconstruction of divergence-free velocity fields from cine 3D phase-contrast flow measurements. Magn Reson Med. 69(1):200\u2013210, 2013. https:\/\/doi.org\/10.1002\/mrm.24221.","journal-title":"Magn Reson Med"},{"issue":"2","key":"3606_CR13","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1002\/mrm.25176","volume":"73","author":"F Ong","year":"2015","unstructured":"Ong, F., et al. Robust 4D flow denoising using divergence-free wavelet transform. Magn Reson Med. 73(2):828\u2013842, 2015. https:\/\/doi.org\/10.1002\/mrm.25176.","journal-title":"Magn Reson Med"},{"key":"3606_CR14","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.jbiomech.2017.05.004","volume":"58","author":"A Bakhshinejad","year":"2017","unstructured":"Bakhshinejad, A., A. Baghaie, A. Vali, D. Saloner, V. L. Rayz, and R. M. D\u2019Souza. Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression. J Biomech. 58:162\u2013173, 2017. https:\/\/doi.org\/10.1016\/j.jbiomech.2017.05.004.","journal-title":"J Biomech"},{"issue":"9","key":"3606_CR15","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.3381","volume":"36","author":"I Perez-Raya","year":"2020","unstructured":"Perez-Raya, I., M. F. Fathi, A. Baghaie, R. H. Sacho, K. M. Koch, and R. M. D\u2019Souza. Towards multi-modal data fusion for super-resolution and denoising of 4D-Flow MRI. Int J Numer Method Biomed Eng.36(9):e3381, 2020. https:\/\/doi.org\/10.1002\/cnm.3381.","journal-title":"Int J Numer Method Biomed Eng"},{"issue":"1","key":"3606_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.3152","volume":"35","author":"SW Funke","year":"2019","unstructured":"Funke, S. W., M. Nordaas, O. Evju, M. S. Alnaes, and K. A. Mardal. Variational data assimilation for transient blood flow simulations: Cerebral aneurysms as an illustrative example. Int J Numer Method Biomed Eng.35(1):e3152, 2019. https:\/\/doi.org\/10.1002\/cnm.3152.","journal-title":"Int J Numer Method Biomed Eng"},{"key":"3606_CR17","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s12938-015-0104-7","volume":"14","author":"VC Rispoli","year":"2015","unstructured":"Rispoli, V. C., J. F. Nielsen, K. S. Nayak, and J. L. Carvalho. Computational fluid dynamics simulations of blood flow regularized by 3D phase contrast MRI. Biomed Eng Online. 14:110, 2015. https:\/\/doi.org\/10.1186\/s12938-015-0104-7.","journal-title":"Biomed Eng Online"},{"issue":"4","key":"3606_CR18","doi-asserted-by":"publisher","first-page":"2231","DOI":"10.1002\/mrm.28269","volume":"84","author":"J Toger","year":"2020","unstructured":"Toger, J., M. J. Zahr, N. Aristokleous, K. Markenroth-Bloch, M. Carlsson, and P. O. Persson. Blood flow imaging by optimal matching of computational fluid dynamics to 4D-flow data. Magn Reson Med. 84(4):2231\u20132245, 2020. https:\/\/doi.org\/10.1002\/mrm.28269.","journal-title":"Magn Reson Med"},{"key":"3606_CR19","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1098\/rsif.2021.0751","volume":"19","author":"J Zhang","year":"2022","unstructured":"Zhang, J., M. Brindise, S. Rothenberger, M. Markl, V. Rayz, and P. Vlachos. A multi-modality approach for enhancing 4D flow magnetic resonance imaging via sparse representation. Journal of The Royal Society Interface. 19:4, 2022. https:\/\/doi.org\/10.1098\/rsif.2021.0751.","journal-title":"Journal of The Royal Society Interface"},{"key":"3606_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105729","volume":"197","author":"MF Fathi","year":"2020","unstructured":"Fathi, M. F., et al. Super-resolution and denoising of 4D-Flow MRI using physics-Informed deep neural nets. Computer Methods and Programs in Biomedicine.197:105729, 2020. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105729.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"3606_CR21","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3389\/fphy.2020.00138","volume":"8","author":"E Ferdian","year":"2020","unstructured":"Ferdian, E., et al. 4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics. Frontiers in Physics. 8:9, 2020. https:\/\/doi.org\/10.3389\/fphy.2020.00138.","journal-title":"Frontiers in Physics"},{"issue":"7","key":"3606_CR22","doi-asserted-by":"publisher","DOI":"10.1063\/5.0054312","volume":"33","author":"H Gao","year":"2021","unstructured":"Gao, H., L. Sun, and J.-X. Wang. Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels. Physics of Fluids.33(7):073603, 2021. https:\/\/doi.org\/10.1063\/5.0054312.","journal-title":"Physics of Fluids"},{"issue":"1","key":"3606_CR23","doi-asserted-by":"publisher","first-page":"10240","DOI":"10.1038\/s41598-021-89636-z","volume":"11","author":"DR Rutkowski","year":"2021","unstructured":"Rutkowski, D. R., A. Roldan-Alzate, and K. M. Johnson. Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data. Sci Rep. 11(1):10240, 2021. https:\/\/doi.org\/10.1038\/s41598-021-89636-z.","journal-title":"Sci Rep"},{"key":"3606_CR24","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2022.928181","author":"S Shit","year":"2022","unstructured":"Shit, S., J. Zimmermann, I. Ezhov, J. Paetzold, A. Sanches, C. Pirkl, and B. Menze. SRflow: Deep learning based super-resolution of 4D-flow MRI data. Frontiers in Artificial Intelligence. 2022. https:\/\/doi.org\/10.3389\/frai.2022.928181.","journal-title":"Frontiers in Artificial Intelligence"},{"key":"3606_CR25","doi-asserted-by":"publisher","unstructured":"Y. Chen, A. G. Christodoulou, Z. Zhou, F. Shi, Y. Xie, and D. Li, \"MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better,\" 2020, https:\/\/doi.org\/10.48550\/arXiv.2003.01217.","DOI":"10.48550\/arXiv.2003.01217"},{"key":"3606_CR26","doi-asserted-by":"publisher","unstructured":"Y. L. Ze Liu, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo, \"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,\" 2021, https:\/\/doi.org\/10.48550\/arXiv.2103.14030.","DOI":"10.48550\/arXiv.2103.14030"},{"key":"3606_CR27","doi-asserted-by":"publisher","unstructured":"A. Daw, Karpatne, A, Watkins, W, Read, J, Kumar, V, \"Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling,\" 2017, https:\/\/doi.org\/10.48550\/arXiv.1710.11431.","DOI":"10.48550\/arXiv.1710.11431"},{"issue":"3","key":"3606_CR28","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich, P. A., et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage. 31(3):1116\u20131128, 2006. https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.015.","journal-title":"NeuroImage"},{"key":"3606_CR29","unstructured":"HyperMesh. (2017). Altair Engineering, Inc., Troy, MI, USA."},{"issue":"8","key":"3606_CR30","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1177\/0284185113508112","volume":"55","author":"J Daouk","year":"2014","unstructured":"Daouk, J., et al. Relationship between cerebrospinal fluid flow, ventricles morphology, and DTI properties in internal capsules: differences between Alzheimer\u2019s disease and normal-pressure hydrocephalus. Acta Radiologica. 55(8):992\u2013999, 2014. https:\/\/doi.org\/10.1177\/0284185113508112.","journal-title":"Acta Radiologica"},{"key":"3606_CR31","unstructured":"Ansys 19.2. (2018). Ansys\u00ae, Canonsburg, PA, USA."},{"issue":"1","key":"3606_CR32","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/0021-9991(79)90145-1","volume":"32","author":"B van Leer","year":"1979","unstructured":"van Leer, B. Towards the ultimate conservative difference scheme. V. A second-order sequel to Godunov\u2019s method. Journal of Computational Physics. 32(1):101\u2013136, 1979. https:\/\/doi.org\/10.1016\/0021-9991(79)90145-1.","journal-title":"Journal of Computational Physics"},{"issue":"7","key":"3606_CR33","doi-asserted-by":"publisher","first-page":"1802","DOI":"10.1109\/tmi.2022.3149421","volume":"41","author":"SM Rothenberger","year":"2022","unstructured":"Rothenberger, S. M., et al. Modeling Bias Error in 4D Flow MRI Velocity Measurements. IEEE Transactions on Medical Imaging. 41(7):1802\u20131812, 2022. https:\/\/doi.org\/10.1109\/tmi.2022.3149421.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"5","key":"3606_CR34","doi-asserted-by":"publisher","first-page":"1668","DOI":"10.1109\/TMI.2019.2954697","volume":"39","author":"J Zhang","year":"2020","unstructured":"Zhang, J., et al. 4D Flow MRI Pressure Estimation Using Velocity Measurement-Error-Based Weighted Least-Squares. IEEE Trans Med Imaging. 39(5):1668\u20131680, 2020. https:\/\/doi.org\/10.1109\/TMI.2019.2954697.","journal-title":"IEEE Trans Med Imaging"},{"key":"3606_CR35","doi-asserted-by":"crossref","unstructured":"Y. C. Fung, \"Blood Flow in Arteries,\" in Biomechanics: Circulation, Y. C. Fung Ed. New York, NY: Springer, 1997, pp. 108-205.","DOI":"10.1007\/978-1-4757-2696-1_3"},{"issue":"6","key":"3606_CR36","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1118\/1.597729","volume":"23","author":"AH Andersen","year":"1996","unstructured":"Andersen, A. H., and J. E. Kirsch. Analysis of noise in phase contrast MR imaging. Medical Physics. 23(6):857\u2013869, 1996. https:\/\/doi.org\/10.1118\/1.597729.","journal-title":"Medical Physics"},{"key":"3606_CR37","doi-asserted-by":"publisher","unstructured":"J. Zhang et al., \"Divergence-Free Constrained Phase Unwrapping and Denoising for 4D Flow MRI Using Weighted Least-Squares,\" IEEE Trans Med Imaging, vol. PP, Jun 4 2021, https:\/\/doi.org\/10.1109\/TMI.2021.3086331.","DOI":"10.1109\/TMI.2021.3086331"},{"issue":"6","key":"3606_CR38","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1002\/cmr.a.20124","volume":"32A","author":"A C\u00e1rdenas-Blanco","year":"2008","unstructured":"C\u00e1rdenas-Blanco, A., C. Tejos, P. Irarrazaval, and I. Cameron. Noise in magnitude magnetic resonance images. Concepts in Magnetic Resonance Part A. 32A(6):409\u2013416, 2008. https:\/\/doi.org\/10.1002\/cmr.a.20124.","journal-title":"Concepts in Magnetic Resonance Part A"},{"key":"3606_CR39","unstructured":"D. G. Nishimura, Principles of Magnetic Resonance Imaging. 2016."},{"key":"3606_CR40","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. van der Maaten, and K. Weinberger, \"Densely Connected Convolutional Networks,\" CVPR 2017.","DOI":"10.1109\/CVPR.2017.243"},{"key":"3606_CR41","doi-asserted-by":"publisher","unstructured":"I. Misra, A. Shrivastava, A. Gupta, and M. Hebert, \"Cross-stitch Networks for Multi-task Learning,\" 2016, https:\/\/doi.org\/10.48550\/arXiv.1604.03539.","DOI":"10.48550\/arXiv.1604.03539"},{"key":"3606_CR42","unstructured":"A. Paszke, et al, \"PyTorch: An Imperative Style, High-Performance Deep Learning Library,\" Advances in Neural Information Processing Systems., pp. 8024-8035, 2019. Available: http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf."},{"key":"3606_CR43","unstructured":"R. Liaw, Liang, E, Nishihara, R, Moritz, P, Gonzalez, J, and Stoica, I, \"Tune: A Research Platform for Distributed Model Selection and Training,\" 2018. Available: https:\/\/arxiv.org\/pdf\/1807.05118.pdf."},{"key":"3606_CR44","unstructured":"L. Li, Jamieson, K., Rostamizadeh, A., Gonina, E., Ben-Tzur, J., Hardt, M., Recht, B., Talwalkar, A., \"A System for Massively Parallel Hyperparameter Tuning,\" presented at the MLSys Conference, Austin, TX, USA, 2020."},{"issue":"1","key":"3606_CR45","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1002\/jmri.25595","volume":"46","author":"S Schnell","year":"2017","unstructured":"Schnell, S., et al. Accelerated dual-<i>venc<\/i> 4D flow MRI for neurovascular applications. Journal of Magnetic Resonance Imaging. 46(1):102\u2013114, 2017. https:\/\/doi.org\/10.1002\/jmri.25595.","journal-title":"Journal of Magnetic Resonance Imaging"},{"key":"3606_CR46","unstructured":"Geomagic Design X. (2019). 3D Systems, Circle Rock Hill, SC, USA."},{"key":"3606_CR47","doi-asserted-by":"crossref","unstructured":"J. P. Ahrens, B. Geveci, and C. C. Law, \"ParaView: An End-User Tool for Large-Data Visualization,\" in The Visualization Handbook, 2005.","DOI":"10.1016\/B978-012387582-2\/50038-1"},{"key":"3606_CR48","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-021-01920-w","author":"S Gholampour","year":"2021","unstructured":"Gholampour, S., and N. Fatouraee. Boundary conditions investigation to improve computer simulation of cerebrospinal fluid dynamics in hydrocephalus patients. Communications Biology. 2021. https:\/\/doi.org\/10.1038\/s42003-021-01920-w.","journal-title":"Communications Biology"},{"issue":"1","key":"3606_CR49","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neuroimage.2010.01.110","volume":"51","author":"A Stadlbauer","year":"2010","unstructured":"Stadlbauer, A., E. Salomonowitz, W. van der Riet, M. Buchfelder, and O. Ganslandt. \u201cInsight into the patterns of cerebrospinal fluid flow in the human ventricular system using MR velocity mapping,\u201d (in eng). Neuroimage. 51(1):42\u201352, 2010. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.01.110.","journal-title":"Neuroimage"},{"key":"3606_CR50","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.5082","author":"LA Rivera-Rivera","year":"2024","unstructured":"Rivera-Rivera, L. A., T. Vikner, L. Eisenmenger, S. C. Johnson, and K. M. Johnson. Four-dimensional flow MRI for quantitative assessment of cerebrospinal fluid dynamics: Status and opportunities. NMR in Biomedicine. 2024. https:\/\/doi.org\/10.1002\/nbm.5082.","journal-title":"NMR in Biomedicine"},{"issue":"10","key":"3606_CR51","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.1177\/0271678x15617171","volume":"36","author":"LA Rivera-Rivera","year":"2016","unstructured":"Rivera-Rivera, L. A., et al. 4D flow MRI for intracranial hemodynamics assessment in Alzheimer\u2019s disease. Journal of Cerebral Blood Flow & Metabolism. 36(10):1718\u20131730, 2016. https:\/\/doi.org\/10.1177\/0271678x15617171.","journal-title":"Journal of Cerebral Blood Flow & Metabolism"},{"issue":"2","key":"3606_CR52","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1002\/mrm.1910220255","volume":"22","author":"D Nishimura","year":"1991","unstructured":"Nishimura, D., J. Jackson, and J. Pauly. On the nature and reduction of the displacement artifact in flow images. Magn Reson Med. 22(2):481\u2013492, 1991. https:\/\/doi.org\/10.1002\/mrm.1910220255.","journal-title":"Magn Reson Med"},{"issue":"6465","key":"3606_CR53","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1126\/science.aax5440","volume":"366","author":"NE Fultz","year":"2019","unstructured":"Fultz, N. E., et al. Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep. Science. 366(6465):628\u2013631, 2019. https:\/\/doi.org\/10.1126\/science.aax5440.","journal-title":"Science"},{"key":"3606_CR54","doi-asserted-by":"crossref","unstructured":"B. Jung and M. Markl, \"Phase-Contrast MRI and Flow Quantification,\" in Magnetic Resonance Angiography: Springer New York, 2012, pp. 51-64.","DOI":"10.1007\/978-1-4419-1686-0_3"},{"issue":"1","key":"3606_CR55","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1002\/mrm.20309","volume":"53","author":"JM Peeters","year":"2005","unstructured":"Peeters, J. M., C. Bos, and C. J. G. Bakker. Analysis and correction of gradient nonlinearity and B0 inhomogeneity related scaling errors in two-dimensional phase contrast flow measurements. Magnetic Resonance in Medicine. 53(1):126\u2013133, 2005. https:\/\/doi.org\/10.1002\/mrm.20309.","journal-title":"Magnetic Resonance in Medicine"},{"issue":"4","key":"3606_CR56","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1002\/mrm.10582","volume":"50","author":"M Markl","year":"2003","unstructured":"Markl, M., et al. Generalized reconstruction of phase contrast MRI: analysis and correction of the effect of gradient field distortions. Magn Reson Med. 50(4):791\u2013801, 2003. https:\/\/doi.org\/10.1002\/mrm.10582.","journal-title":"Magn Reson Med"},{"issue":"2","key":"3606_CR57","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1002\/mrm.1910390218","volume":"39","author":"MA Bernstein","year":"1998","unstructured":"Bernstein, M. A., et al. \u201cConcomitant gradient terms in phase contrast MR: analysis and correction,\u201d (in eng). Magn Reson Med. 39(2):300\u2013308, 1998. https:\/\/doi.org\/10.1002\/mrm.1910390218.","journal-title":"Magn Reson Med"},{"issue":"11","key":"3606_CR58","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1007\/s11548-018-1837-0","volume":"13","author":"FJ Detmer","year":"2018","unstructured":"Detmer, F. J., et al. Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics. Int J Comput Assist Radiol Surg. 13(11):1767\u20131779, 2018. https:\/\/doi.org\/10.1007\/s11548-018-1837-0.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"1","key":"3606_CR59","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1161\/STROKEAHA.110.592923","volume":"42","author":"J Xiang","year":"2011","unstructured":"Xiang, J., et al. Hemodynamic-morphologic discriminants for intracranial aneurysm rupture. Stroke. 42(1):144\u2013152, 2011. https:\/\/doi.org\/10.1161\/STROKEAHA.110.592923.","journal-title":"Stroke"},{"issue":"1","key":"3606_CR60","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1136\/neurintsurg-2014-011477","volume":"8","author":"J Xiang","year":"2016","unstructured":"Xiang, J., J. Yu, K. V. Snyder, E. I. Levy, A. H. Siddiqui, and H. Meng. Hemodynamic-morphological discriminant models for intracranial aneurysm rupture remain stable with increasing sample size. J Neurointerv Surg. 8(1):104\u2013110, 2016. https:\/\/doi.org\/10.1136\/neurintsurg-2014-011477.","journal-title":"J Neurointerv Surg"}],"container-title":["Annals of Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-024-03606-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10439-024-03606-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-024-03606-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T06:24:44Z","timestamp":1738995884000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10439-024-03606-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,2]]},"references-count":60,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["3606"],"URL":"https:\/\/doi.org\/10.1007\/s10439-024-03606-w","relation":{},"ISSN":["0090-6964","1573-9686"],"issn-type":[{"value":"0090-6964","type":"print"},{"value":"1573-9686","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,2]]},"assertion":[{"value":"13 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}