{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T02:47:27Z","timestamp":1784688447890,"version":"3.55.0"},"reference-count":31,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R21\u2010HL132263"],"award-info":[{"award-number":["1R21\u2010HL132263"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A novel deep learning\u2013based approach is proposed to estimate pressure gradient across stenosis from four\u2010dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity\u2010to\u2010Presure Network (4Dflow\u2010VP\u2010Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD\u2010simulated velocity data, in vitro 4D flow MRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow\u2010VP\u2010Net was compared to catheter\u2010based pressure measurements for available flow rates, in vitro and Doppler echocardiography\u2013based pressure measurement, in vivo.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Relative pressures calculated by 4Dflow\u2010VP\u2010Net and in vitro pressure catheterization revealed strong correlation (<jats:italic>r<\/jats:italic><jats:sup>2<\/jats:sup>\u2009=\u20090.91). Correlations analysis of TVPG from reference CFD and 4Dflow\u2010VP\u2010Net for 450 simulated flow conditions showed strong correlation (<jats:italic>r<\/jats:italic><jats:sup>2<\/jats:sup>\u2009=\u20090.99). TVPG from in vitro MRI had a correlation coefficient of <jats:italic>r<\/jats:italic><jats:sup>2<\/jats:sup>\u2009=\u20090.98 with reference CFD. 4Dflow\u2010VP\u2010Net, applied to 4D flow MRI in 16 patients, showed comparable TVPG measurement with Doppler echocardiography (<jats:italic>r<\/jats:italic><jats:sup>2<\/jats:sup>\u2009=\u20090.85). Bland\u2013Altman analysis of TVPG measurements showed mean bias and limits of agreement of \u22120.20\u2009\u00b1\u20092.07\u2009mmHg and 0.19\u2009\u00b1\u20090.45\u2009mmHg for CFD\u2010simulated velocities and in vitro 4D flow velocities. In patients, overestimation of Doppler echocardiography relative to TVPG from 4Dflow\u2010VP\u2010Net (10.99\u2009\u00b1\u20096.77\u2009mmHg) was observed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.29791","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T04:40:47Z","timestamp":1690432847000},"page":"2175-2189","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["4Dflow\u2010VP\u2010Net: A deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI"],"prefix":"10.1002","volume":"90","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1797-064X","authenticated-orcid":false,"given":"Ruponti","family":"Nath","sequence":"first","affiliation":[{"name":"Medical Imaging Lab, Department of Electrical and Computer Engineering University of Louisville  Louisville Kentucky USA"},{"name":"Robley Rex Veterans Affairs Medical Center  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6967-3893","authenticated-orcid":false,"given":"Amirkhosro","family":"Kazemi","sequence":"additional","affiliation":[{"name":"Medical Imaging Lab, Department of Electrical and Computer Engineering University of Louisville  Louisville Kentucky USA"},{"name":"Robley Rex Veterans Affairs Medical Center  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4387-1001","authenticated-orcid":false,"given":"Sean","family":"Callahan","sequence":"additional","affiliation":[{"name":"Medical Imaging Lab, Department of Electrical and Computer Engineering University of Louisville  Louisville Kentucky USA"},{"name":"Robley Rex Veterans Affairs Medical Center  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus F.","family":"Stoddard","sequence":"additional","affiliation":[{"name":"Robley Rex Veterans Affairs Medical Center  Louisville Kentucky USA"},{"name":"Cardiovascular Division University of Louisville School of Medicine  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9698-8989","authenticated-orcid":false,"given":"Amir A.","family":"Amini","sequence":"additional","affiliation":[{"name":"Medical Imaging Lab, Department of Electrical and Computer Engineering University of Louisville  Louisville Kentucky USA"},{"name":"Robley Rex Veterans Affairs Medical Center  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.7.765"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/ejechocard\/jen303"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/hrt.43.3.284"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25097"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-015-0174-5"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910360404"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.20152"},{"key":"e_1_2_8_9_1","article-title":"Noninvasive 3D pressure calculation from PC\u2010MRI via non\u2010iterative harmonics\u2010based orthogonal projection: constant flow experiment","author":"Negahdar MJ","year":"2013","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102195"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101627"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.08.012"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2012.07.017"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0151540"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-61450-z"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.5024595"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.108925"},{"key":"e_1_2_8_18_1","first-page":"545","volume-title":"International Conference on Information Processing in Medical Imaging (IPMI 20210). 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