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A model has been proposed to estimate\n                      <jats:italic>T<\/jats:italic>\n                      <jats:sub>L<\/jats:sub>\n                      from\n                      <jats:sup>129<\/jats:sup>\n                      Xe MRI metrics, but this approach has not been fully validated and does not utilise the spatial information provided by three-dimensional\n                      <jats:sup>129<\/jats:sup>\n                      Xe MRI.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Three models for predicting\n                      <jats:italic>T<\/jats:italic>\n                      <jats:sub>L<\/jats:sub>\n                      from\n                      <jats:sup>129<\/jats:sup>\n                      Xe MRI metrics were compared: 1) a previously-published physiology-based model, 2) multivariable linear regression and 3) random forest regression. Models were trained on data from 150 patients with asthma and\/or COPD. The random forest model was applied voxel-wise to\n                      <jats:sup>129<\/jats:sup>\n                      Xe images to yield regional\n                      <jats:italic>T<\/jats:italic>\n                      <jats:sub>L<\/jats:sub>\n                      maps.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Coefficients of the physiological model were found to differ from previously reported values. All models had good prediction accuracy with small mean absolute error (MAE): 1) 1.24\u00b10.15\u2005mmol\u00b7min\n                      <jats:sup>\u22121<\/jats:sup>\n                      \u00b7kPa\n                      <jats:sup>\u22121<\/jats:sup>\n                      , 2) 1.01\u00b10.06\u2005mmol\u00b7min\n                      <jats:sup>\u22121<\/jats:sup>\n                      \u00b7kPa\n                      <jats:sup>\u22121<\/jats:sup>\n                      , 3) 0.995\u00b10.129\u2005mmol\u00b7min\n                      <jats:sup>\u22121<\/jats:sup>\n                      \u00b7kPa\n                      <jats:sup>\u22121<\/jats:sup>\n                      . The random forest model performed well when applied to a validation group of post-COVID-19 patients and healthy volunteers (MAE=0.840\u2005mmol\u00b7min\n                      <jats:sup>\u22121<\/jats:sup>\n                      \u00b7kPa\n                      <jats:sup>\u22121<\/jats:sup>\n                      ), suggesting good generalisability. The feasibility of producing regional maps of predicted\n                      <jats:italic>T<\/jats:italic>\n                      <jats:sub>L<\/jats:sub>\n                      was demonstrated and the whole-lung sum of the\n                      <jats:italic>T<\/jats:italic>\n                      <jats:sub>L<\/jats:sub>\n                      maps agreed with measured\n                      <jats:italic>T<\/jats:italic>\n                      <jats:sub>LCO<\/jats:sub>\n                      (MAE=1.18\u2005mmol\u00b7min\n                      <jats:sup>\u22121<\/jats:sup>\n                      \u00b7kPa\n                      <jats:sup>\u22121<\/jats:sup>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The best prediction of\n                      <jats:italic>T<\/jats:italic>\n                      <jats:sub>LCO<\/jats:sub>\n                      from\n                      <jats:sup>129<\/jats:sup>\n                      Xe MRI metrics was with a random forest regression framework. Applying this model on a voxel-wise level to create parametric\n                      <jats:italic>T<\/jats:italic>\n                      <jats:sub>L<\/jats:sub>\n                      maps provides a useful tool for regional visualisation and clinical interpretation of\n                      <jats:sup>129<\/jats:sup>\n                      Xe gas exchange MRI.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1183\/23120541.00442-2024","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T13:06:03Z","timestamp":1726146363000},"page":"00442-2024","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A framework for modelling whole-lung and regional transfer factor of the lung for carbon monoxide using hyperpolarised xenon-129 lung magnetic resonance imaging"],"prefix":"10.1183","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5343-2446","authenticated-orcid":false,"given":"Jemima H.","family":"Pilgrim-Morris","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5769-423X","authenticated-orcid":false,"given":"Laurie J.","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7425-1449","authenticated-orcid":false,"given":"Helen","family":"Marshall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilal A.","family":"Tahir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guilhem J.","family":"Collier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8358-394X","authenticated-orcid":false,"given":"Neil J.","family":"Stewart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jim M.","family":"Wild","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"2025030304052470000_11.1.00442-2024.1","unstructured":"Modi P , Cascella M . 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