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Although chemical exchange saturation transfer (CEST) MRI can detect the saturation transfer effect from PCr, quantification of the underlying PCr f\n                      <jats:sub>s<\/jats:sub>\n                      and k\n                      <jats:sub>sw<\/jats:sub>\n                      , particularly at low fields, remains challenging due to significant overlapping confounding effects in tissues when using conventional fitting approaches. Deep learning (DL) presents a promising alternative, yet traditional DL models often struggle to capture subtle PCr\u2010specific variations induced by changes in\n                      <jats:italic>f<\/jats:italic>\n                      <jats:sub>s<\/jats:sub>\n                      or\n                      <jats:italic>k<\/jats:italic>\n                      <jats:sub>sw<\/jats:sub>\n                      . Furthermore, these models are typically trained on either fully synthetic data, which may not adequately mimic tissues, or in vivo data which lack ground truth.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study introduces a global\u2013local two\u2010branch DL model to effectively eliminate confounding effects and capture subtle variations in the PCr CEST effect. Furthermore, our model was trained on partially synthetic data that offers both simulation flexibility and fidelity. Model accuracy was evaluated by using both digital and physical phantoms, and the model was applied to skeletal muscle of healthy rats and rats with amyotrophic lateral sclerosis (ALS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Phantom experiments demonstrate that our approach surpasses all fitting methods, the\n                      <jats:italic>state\u2010of\u2010the\u2010art<\/jats:italic>\n                      model, and other combinations of DL models and training data. In vivo, the model identified a significant reduction in PCr\n                      <jats:italic>f<\/jats:italic>\n                      <jats:sub>s<\/jats:sub>\n                      in ALS rats, which other methods fail to detect.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our global\u2013local two\u2010branch DL model trained using partially synthetic data enhances PCr quantification in skeletal muscle.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70386","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T03:36:26Z","timestamp":1775878586000},"page":"741-755","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhanced Quantitative Phosphocreatine\n                    <scp>MR<\/scp>\n                    Imaging of Skeletal Muscle Using a Global\u2013Local Two\u2010Branch Deep Learning Model"],"prefix":"10.1002","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9522-0780","authenticated-orcid":false,"given":"Malvika","family":"Viswanathan","sequence":"first","affiliation":[{"name":"Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center  Nashville Tennessee USA"},{"name":"Department of Biomedical Engineering Vanderbilt University  Nashville Tennessee USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leqi","family":"Yin","sequence":"additional","affiliation":[{"name":"Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center  Nashville Tennessee USA"},{"name":"School of Engineering Vanderbilt University  Nashville Tennessee 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