{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:38:16Z","timestamp":1783021096352,"version":"3.54.6"},"reference-count":32,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a robust deep learning framework for noncontrast\u2010enhanced functional lung MRI, overcoming the limitations of spectral decomposition in the presence of physiological nonstationarity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We introduce VQ\u2010Wave (Ventilation\/Q\u2010perfusion Waveform\u2010based Assessment of Variable Evolutions), a physics\u2010driven spatiotemporal inception neural network trained on synthetic signal models to estimate ventilation and perfusion parameters. By processing local spatial context alongside temporal evolution, the network learns to robustly decouple physiological signals from noise. The training generator simulated realistic nonstationary dynamics, including amplitude modulations, frequency drifts, and noise. Performance was validated against matrix pencil (MP) decomposition using numerical phantoms and in vivo functional lung MRI acquired in four healthy volunteers and two children with cystic fibrosis (CF) at 1.5\u2009T. Robustness was assessed across varying noise levels, physiological instabilities, and scan durations (truncating the acquisition from\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009140 to\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200940 images).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In numerical benchmarks, VQ\u2010Wave demonstrated superior robustness to nonstationarity, maintaining low global and regional error rates where MP exhibited stochastic instability due to spectral leakage. In vivo, VQ\u2010Wave accurately captured functional defects in patients with CF yielding diagnostically consistent ventilation and perfusion maps with high quantitative stability (global mean variation &lt;\u200912%) even when scan time was reduced from 45\u2009s to 15\u2009s (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200940). Conversely, under irregular physiology and short scan lengths, MP decomposition severely degraded, exhibiting systematic amplitude instability, overestimation bias, and regional signal dropouts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>VQ\u2010Wave offers a robust, physics\u2010driven neural network\u2010based alternative to spectral decomposition. By effectively handling physiological irregularity and noise, it enables reliable functional lung imaging with substantially shortened acquisition protocols.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70423","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T03:46:16Z","timestamp":1778471176000},"page":"1427-1442","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>VQ<\/scp>\n                    \u2010Wave: A Physics\u2010Driven Spatiotemporal Deep Learning Approach for Noncontrast\u2010Enhanced Lung Ventilation and Perfusion\n                    <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"96","author":[{"given":"Grzegorz","family":"Bauman","sequence":"first","affiliation":[{"name":"Division of Radiological Physics, Department of Radiology University of Basel Hospital  Basel Switzerland"},{"name":"Department of Biomedical Engineering University of Basel  Allschwil Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4112-5292","authenticated-orcid":false,"given":"Pavlos","family":"Panos","sequence":"additional","affiliation":[{"name":"Division of Radiological Physics, Department of Radiology University of Basel Hospital  Basel Switzerland"},{"name":"Department of Biomedical Engineering University of Basel  Allschwil Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Latzin","sequence":"additional","affiliation":[{"name":"Pediatric Respiratory Medicine, Department of Pediatrics Inselspital, Bern University Hospital, University of Bern  Bern Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6755-5495","authenticated-orcid":false,"given":"Oliver","family":"Bieri","sequence":"additional","affiliation":[{"name":"Division of Radiological Physics, Department of Radiology University of Basel Hospital  Basel Switzerland"},{"name":"Department of Biomedical Engineering University of Basel  Allschwil Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-42617-4"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13244\u2010012\u20100176\u2010x"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.25.32637"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13244\u2010011\u20100140\u20101"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10020271"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.38493"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474\u20104422(17)30158\u20108"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.22375"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2016.09.015"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/1465\u20109921\u20107\u2010106"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.2463\/mrms.1.137"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22031"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26096"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26893"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.3134"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29656"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10334\u2010017\u20100650\u2010z"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2022.102085"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010017\u20100013\u20101"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1418052"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.598577"},{"key":"e_1_2_9_23_1","unstructured":"S.Bai J. Z.Kolter andV.Koltun \u201cAn Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling \u201dpreprint arXiv 2018 1803.01271."},{"key":"e_1_2_9_24_1","volume-title":"31st Conference on Neural Information Processing Systems (NIPS). Advances in Neural Information Processing Systems","author":"Vaswani A.","year":"2017"},{"key":"e_1_2_9_25_1","doi-asserted-by":"crossref","unstructured":"M.RavanelliandY.Bengio \u201cSpeaker Recognition From Raw Waveform With SincNet \u201d2018 IEEE Spoken Language Technology Workshop (SLT) 1021\u20131028.","DOI":"10.1109\/SLT.2018.8639585"},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"J.Hu L.Shen andG.Sun \u201cSqueeze\u2010and\u2010Excitation Networks \u201dProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618\u2010020\u201000710"},{"key":"e_1_2_9_28_1","first-page":"8024","article-title":"PyTorch: An Imperative Style, High\u2010Performance Deep Learning Library","volume":"32","author":"Paszke A.","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29184"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25697"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-92258-4_3"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.zemedi.2024.08.001"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24930"}],"container-title":["Magnetic Resonance in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mrm.70423","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mrm.70423","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mrm.70423","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:37:30Z","timestamp":1783017450000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/mrm.70423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,10]]},"references-count":32,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1002\/mrm.70423"],"URL":"https:\/\/doi.org\/10.1002\/mrm.70423","archive":["Portico"],"relation":{},"ISSN":["0740-3194","1522-2594"],"issn-type":[{"value":"0740-3194","type":"print"},{"value":"1522-2594","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,10]]},"assertion":[{"value":"2026-02-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}