{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:59:19Z","timestamp":1785250759449,"version":"3.55.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiovascular magnetic resonance (CMR) techniques provide detailed myocardial tissue characterization. However, LGE requires the administration of contrast agents, while T1\/T2 mapping involves prolonged acquisition times, sensitivity to motion artifacts, and protocol complexity. LGE can also be limited in differentiating cardiac amyloidosis from hypertrophic cardiomyopathy (HCM) due to overlapping enhancement patterns. Oxygenation-sensitive CMR (OS-CMR), offers a rapid, contrast-free alternative but lacks direct quantitative outputs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose a unified AI framework integrating DeepOxyMap and a Residual Generative Adversarial Network (R-GAN) to extract latent features and generate synthetic T1 maps from OS-CMR. DeepOxyMap was trained on 117 OS-CMR images using a VGG19-based architecture to identify fibrosis-related myocardial patterns via heatmaps. The R-GAN was subsequently trained on 2,044 OS-CMR images from two independent cohorts to generate T1-parametric maps directly from OS-CMR. Performance was assessed using image similarity metrics, ROI-based clinical validation, T1 recovery curve analysis, and Extended Phase Graph simulations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      DeepOxyMap achieved an accuracy of of 83.5%, with a precision of 85.5% and a recall of 79.7%.AUC values were 0.93 (ICMP), 0.88 (\n                      <jats:italic>N<\/jats:italic>\n                      ICMP), and 0.97 (healthy). Heatmaps showed strong spatial correspondence with fibrosis regions on LGE images. In the second stage, the R-GAN outperformed Pix2Pix (SSIM: 0.810, PSNR: 16.01, PCC: 0.748). EPG-based validation demonstrated near-perfect agreement between synthetic and theoretical T1 recovery curves (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&amp;gt;\u20090.9994). T1 curve agreement was also strong in ROI-based analysis. HCM: MAE\u2009=\u20090.0970, MSE\u2009=\u20090.0106, RMSE\u2009=\u20090.1030,\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.7764, Pearson\u2009=\u20090.9658, Spearman\u2009=\u20090.9286. Amyloidosis: MAE\u2009=\u20090.0988, MSE\u2009=\u20090.0141, RMSE\u2009=\u20090.1187,\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.8599, Pearson\u2009=\u20090.9718, Spearman\u2009=\u20090.9762. Synthetic T1 maps preserved disease-specific signal patterns. Synthetic and native T1 values demonstrated excellent agreement (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.976,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001) with minimal bias (\u22126.7\u2005ms).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates that OS-CMR contains latent tissue information that can be leveraged to generate quantitative T1 maps using AI. The proposed contrast-free framework enables rapid acquisition, eliminates the need for gadolinium, and preserves clinically relevant myocardial patterns, offering a promising approach for non-invasive cardiac tissue characterization in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1855952","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:05:55Z","timestamp":1785247555000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic T1 mapping using artificial intelligence and oxygenation-sensitive cardiovascular magnetic resonance for myocardial tissue characterization in hypertrophic cardiomyopathy and cardiac amyloidosis"],"prefix":"10.3389","volume":"13","author":[{"given":"Faezeh","family":"Lotfikazemi","sequence":"first","affiliation":[{"name":"Department of Experimental Medicine, McGill University","place":["Montreal, QC, 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