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Each PROPELLER blade was split along the readout direction into two disjoint subsets: one subset for training an unrolled network, and the other for loss calculation. Following the Noise2Noise paradigm, this framework split k\u2010space into two subsets with independent, matched noise but identical underlying signal, enabling joint reconstruction and denoising without external training references. For comparison, coil\u2010wise Marchenko\u2010Pastur Principal Component Analysis (MPPCA) denoising followed by parallel imaging reconstruction was performed. The reconstructed images were evaluated by two experienced chest radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The self\u2010supervised model generated lung images with improved clarity, better delineation of parenchymal and airway structures, and maintained high fidelity in cases with available CT references. In addition, the proposed framework also enabled further reduction of scan time by reconstructing images with adequate diagnostic quality from only half the number of blades. The reader study confirmed that the proposed method outperformed MPPCA across all categories (Wilcoxon signed\u2010rank test,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), with moderate inter\u2010reader agreement (weighted Cohen's kappa\u2009=\u20090.55; percentage of exact and within \u00b11 point agreement\u2009=\u200991%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By leveraging the intrinsic data redundancy in PROPELLER sampling and extending the Noise2Noise concept, the proposed self\u2010supervised framework enabled simultaneous reconstruction and denoising of lung images at 0.55T to address the low\u2010SNR challenge at low\u2010field. It holds great potential for broad use in other low\u2010field MRI applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70224","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T03:13:22Z","timestamp":1765595602000},"page":"2930-2946","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Self\u2010Supervised Joint Reconstruction and Denoising of\n                    <scp>T2<\/scp>\n                    \u2010Weighted\n                    <scp>PROPELLER MRI<\/scp>\n                    of the Lung at\n                    <scp>0.55T<\/scp>"],"prefix":"10.1002","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6023-1033","authenticated-orcid":false,"given":"Jingjia","family":"Chen","sequence":"first","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine 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