{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:59:29Z","timestamp":1791381569175,"version":"4.3.3"},"reference-count":23,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiac real\u2010time MRI (RT\u2010MRI) in combination with MR\u2010compatible spirometry (MRcS) offers unique opportunities to study heart\u2010lung interactions. In contrast to other techniques that monitor respiration during MRI, MRcS provides quantitative respiratory data. Though MRcS is well tolerated, shortening of the scanning time with MRcS would be desirable, especially in young and sick patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The aim of the study was to predict airflow and lung volume based on RT\u2010MR images after a short learning phase of combined RT\u2010MRI and MRcS to provide respiratory data for subsequent short axis stack\u2010based volumetries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Cardiac RT\u2010MRI (1.5 T; short axis; 30 frames\/s) was acquired during free breathing in combination with MRcS in adult healthy subjects (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a010). MR images with MRcS were recorded during a learning phase to collect training data. The iterative Lucas\u2010Kanade method was applied to estimate optical flow from the captured MR images. A ridge regression model was fitted to predict airflow and thus also the lung volume from the estimated optical flow. Hyperparameters were estimated using leave\u2010one\u2010out cross validation and the performance was assessed on a held\u2010out test dataset. Different durations and compositions of the learning phase were investigated to develop the most efficient measurement protocol. Coefficient of determination (R\n                      <jats:sup>2<\/jats:sup>\n                      ), relative mean squared error (rMSE), Bland\u2010Altman analysis on absolute tidal volume difference (aTVD), and absolute maximal airflow difference (aMFD) were used to validate the predictions on held\u2010out test data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      MRI combined with MRcS can train a machine learning algorithm to provide excellent predictive quantitative respiratory volume and flow for the remaining study. The optimal trade\u2010off between predictive power and time necessary for training was reached with a shortened cardiac volumetry protocol covering only about two breaths per slice and every second slice (airflow: mean R\n                      <jats:sup>2<\/jats:sup>\n                      : 0.984, mean rMSE: 0.015, Bias aMFD: \u20100.01 L\/s with +0.084\/\u20100.1 95% CI and volume: mean R\n                      <jats:sup>2<\/jats:sup>\n                      : 0.990, mean rMSE: 0.003, Bias aTVD: 4.27\u00a0mL with +33\/\u201024 95% CI) at a total duration of 100 s. Shorter protocols or application of the algorithm to subsequent studies in the same subject or even in different subjects still provided useful qualitative data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine\u2010learning\u2010based prediction of respiratory flow and lung volume from cardiac RT\u2010MR images after a short training phase with MRcS is feasible and can help to shorten the time with MRcS while providing accurate respiratory data during RT\u2010MRI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18019","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T06:42:28Z","timestamp":1754980948000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine\u2010learning\u2010based prediction of respiratory flow and lung volume from real\u2010time cardiac MRI using MR\u2010compatible spirometry"],"prefix":"10.1002","volume":"52","author":[{"given":"Halima","family":"Malik","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology Medical Faculty and University Hospital D\u00fcsseldorf, Heinrich Heine University  D\u00fcsseldorf North Rhine\u2010Westphalia Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Uelwer","sequence":"additional","affiliation":[{"name":"Department of Computer Science Technical University of Dortmund  Dortmund North Rhine\u2010Westphalia Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lena Maria","family":"R\u00f6wer","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology Medical Faculty and University Hospital D\u00fcsseldorf, Heinrich Heine University  D\u00fcsseldorf North Rhine\u2010Westphalia Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janina","family":"Benner","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology Medical Faculty and University Hospital D\u00fcsseldorf, Heinrich Heine University  D\u00fcsseldorf North Rhine\u2010Westphalia Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo Emilio","family":"Verde","sequence":"additional","affiliation":[{"name":"Coordination Centre for Clinical Trials Heinrich Heine University  D\u00fcsseldorf North Rhine\u2010Westphalia Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Harmeling","sequence":"additional","affiliation":[{"name":"Department of Computer Science Technical University of Dortmund  Dortmund North Rhine\u2010Westphalia Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Voit","sequence":"additional","affiliation":[{"name":"Biomedical NMR, Max Planck Institute for Multidisciplinary Sciences  G\u00f6ttingen Lower Saxony Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Frahm","sequence":"additional","affiliation":[{"name":"Biomedical NMR, Max Planck Institute for Multidisciplinary Sciences  G\u00f6ttingen Lower Saxony Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Klee","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology Medical Faculty and University Hospital D\u00fcsseldorf, Heinrich Heine University  D\u00fcsseldorf North Rhine\u2010Westphalia Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Pillekamp","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology Medical Faculty and University Hospital D\u00fcsseldorf, Heinrich Heine University  D\u00fcsseldorf North Rhine\u2010Westphalia 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