{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:03:52Z","timestamp":1773029032623,"version":"3.50.1"},"reference-count":47,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"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 Imaging"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cystic fibrosis (CF) monitoring relies on computed tomography (CT), but ultra\u2010short echo time MRI (UTE\u2010MRI) offers a radiation\u2010free alternative. However, its clinical adoption is hindered by the laborious and subjective manual analysis, which prevents standardized quantification of bronchial abnormalities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a deep learning (DL) system for the segmentation of CF bronchial abnormalities on UTE\u2010MRI and assess clinical relevance in patients undergoing cystic fibrosis transmembrane conductance regulator (CFTR) modulator treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>\n                      One\u2010hundred and sixty\u2010six CF patients were included (age\u2009=\u200923\u2009\u00b1\u200911, 48% male), comprising a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200997), a test set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200925), and an independent clinical validation cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200944).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>1.5T\/UTE\u2010MRI 3D gradient\u2010echo Spiral Volume Interpolated Breath\u2010hold Examination (VIBE) sequence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>The RiSeNet architecture was trained using paired UTE\u2010MRI and CT scans. Its technical performance was evaluated against expert\u2010refined segmentations and compared to state\u2010of\u2010the\u2010art segmentation models using topology\u2010aware metrics: Normalized Surface Dice (NSD) and CenterLine Dice (clDice). Clinical validation was performed by correlating automated measurements at baseline (M0) and 1\u2010year post\u2010CFTR modulator treatment (M12) with Bhalla scores and pulmonary function tests (FEV1%p).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Student's\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test, Mann\u2013Whitney, Wilcoxon, and Chi\u2010square tests were used for group comparisons. The Spearman test was used to assess correlations. A\n                      <jats:italic>p<\/jats:italic>\n                      value\u2009&lt;\u20090.05 was considered statistically significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the test group, RiSeNet achieved significantly superior performance versus state\u2010of\u2010the\u2010art with NSD scores of 0.84 for bronchiectasis, 0.90 for wall thickening, and 0.75 for mucus; and clDice scores of 0.69, 0.61, and 0.64, respectively. In the clinical validation group, significant correlations with Bhalla (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u2009=\u2009\u22120.92\/\u22120.85) and FEV1%p (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u2009=\u2009\u22120.68\/\u22120.67) were observed pre\/post\u2010CFTR modulator. Post\u2010CFTR modulator, FEV1%p improved (69%\u201392%) with significant reductions in bronchiectasis (3.88\u20131.25), wall thickening (30.43\u20133.05), and mucus (53.30\u201311.80).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>RiSeNet may enable semantic segmentation of CF abnormalities on radiation\u2010free UTE\u2010MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>3<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>4<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.70196","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T13:26:23Z","timestamp":1764681983000},"page":"1190-1200","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["<scp>3D<\/scp>\n                    Automated Segmentation of Bronchial Abnormalities on Ultrashort Echo Time\n                    <scp>MRI<\/scp>\n                    : A Quantitative\n                    <scp>MR<\/scp>\n                    Outcome in Cystic Fibrosis"],"prefix":"10.1002","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8148-8581","authenticated-orcid":false,"given":"Amel Imene Hadj","family":"Bouzid","sequence":"first","affiliation":[{"name":"Univ. 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