{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T12:44:39Z","timestamp":1792586679009,"version":"4.5.0"},"reference-count":59,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["Grant 572737263"],"award-info":[{"award-number":["Grant 572737263"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Breath-hold triggered functional magnetic resonance imaging (bh-fMRI) is a widely available, easily implementable and inexpensive method to quantify cerebrovascular reactivity. Estimation of patients\u2019 respiratory rate is relevant, as it might influence bh-fMRI data quality, but respiratory sensors integrated in the scanner table are not always available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To propose an approach to estimate patients\u2019 respiratory rate using realignment data of a 1-min high-temporal-resolution cerebral MRI sequence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>In a prospective study, 15 patients with Moyamoya angiopathy underwent a 1-min MRI sequence during routine clinical MRI. The respiratory rate was obtained by analyzing patients\u2019 head motion from image realignment data. To obtain robust results, the respiratory rate was calculated using a multi-method approach, incorporating power spectral density and fast Fourier transform and weighted data from multiparametric rigid body transformation. The respiratory rate detected from respiratory sensors integrated in the scanner table was used as reference. Bland\u2013Altman analysis was performed and intra-class correlation coefficients (ICCs) were calculated to compare the respiratory rates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Bland\u2013Altman analysis revealed a mean bias of 0.5 breaths\/min and limits of agreement (LoA) ranging from \u22125.3 to 4.4 breaths\/min, with no outliers. The overall agreement between the respiratory rate calculated from realignment data and from scanner sensor data was excellent (ICC = 0.866, 95% confidence interval = 0.607\u20130.955;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The calculated mean bias and LoA appear to be clinically acceptable. Estimating respiratory rate from a 1-min high-temporal-resolution MRI sequence may be a feasible method when respiratory sensors integrated in the scanner are not available.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02841851261484876","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T13:41:49Z","timestamp":1789047709000},"page":"902-910","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Respiratory rate estimation by using realignment data from a 1-minute high-temporal-resolution MRI sequence: A feasibility study"],"prefix":"10.1177","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4105-5131","authenticated-orcid":false,"given":"Leonie","family":"Zerweck","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Neuroradiology, University Hospital Tuebingen, Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uwe","family":"Klose","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Neuroradiology, University Hospital Tuebingen, Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulrike","family":"Ernemann","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Neuroradiology, University Hospital Tuebingen, Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Till-Karsten","family":"Hauser","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Neuroradiology, University Hospital Tuebingen, Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.02.037"},{"key":"e_1_3_3_3_2","doi-asserted-by":"crossref","unstructured":"Clements RG Zvolanek KM Reddy NA et al. 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