{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T03:41:14Z","timestamp":1792467674387,"version":"4.5.0"},"reference-count":38,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"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 in Med"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Accurate image quality assessment (IQA) is essential for evaluating tumor magnetic resonance (MR) images in clinical practice and computer\u2010aided diagnosis. However, most existing IQA models are designed for natural images and require reference images for training. Variability in tissue characteristics, lesion location, and lesion morphology, together with the scarcity of high\u2010quality MR reference images, makes quality assessment of tumor MR images particularly challenging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address these challenges, we propose a no\u2010reference magnetic resonance curve estimation network named NR\u2010MRC. First, a magnetic resonance curve estimation module uses nonlinear curve mapping to estimate spatially adjusted intensity and detail features without requiring reference tumor MR images. Second, a multi\u2010scale feature estimation module extracts contextual features at different scales and fuses them with the initial estimates to generate enhanced MR images. Finally, a magnetic resonance scene statistics blind quality model evaluates feature consistency between the original images and their enhanced counterparts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experiments on publicly available brain tumor and bladder cancer datasets demonstrate that NR\u2010MRC outperforms the compared IQA and image enhancement methods. In the tested U\u2010Net and DeepLab v3 settings, training with images selected by high NR\u2010MRC scores or enhanced by NR\u2010MRC increased segmentation metrics by approximately 20%. These task\u2010specific gains do not by themselves establish improved clinical diagnostic image quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>NR\u2010MRC provides no\u2010reference quality assessment and enhancement for tumor MR images and shows potential utility in the evaluated downstream segmentation tasks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70628","type":"journal-article","created":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T03:21:44Z","timestamp":1792466504000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>NR<\/scp>\n                    \u2010\n                    <scp>MRC<\/scp>\n                    : A No\u2010Reference Magnetic Resonance Curve Estimation Network for Tumor\n                    <scp>MR<\/scp>\n                    Image Quality 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