{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:52:27Z","timestamp":1784166747126,"version":"3.55.0"},"reference-count":34,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Cardiovascular Imaging"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>This study aims to develop a diffusion model-based framework for generating late gadolinium enhancement (LGE)-like images without contrast. The resulting synthetic images are then comprehensively evaluated for subjective and objective image quality, as well as their clinical utility for quantifying scar in acute myocardial infarction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>In this retrospective study, we developed a diffusion mode-based framework, multisequence guided diffusion to generate synthetic native enhancement (SNE) images from cine magnetic resonance imaging, and T2 short tau inversion recovery sequences. Data were collected from 331 patients with acute myocardial infarction across 3 centers from January 2014 to July 2024. Subjective and objective image qualities were assessed using Likert scoring and contrast ratio analyses on both internal and external cohorts, comparing SNE with standard LGE to evaluate group differences. Myocardial contours were manually delineated, and scar size and transmurality were quantified using the full-width at half-maximum method to assess the accuracy of myocardial infarction detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      In comparisons with general generative models and multimodal fusion\u2013based generative approaches, multisequence guided diffusion demonstrated more favorable visual perceptual quality and the closest data distribution alignment to conventional LGE. SNE demonstrated significantly higher quality than LGE (internal: 4.250 [4.000\u20134.750] versus 4.000 [3.750\u20134.500]; external: 4.250 [4.000\u20134.750] versus 4.000 [3.500\u20134.250];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.05) and improved contrast ratios (blood pool versus myocardium: 9.010 [6.938\u201312.761] versus 8.767 [6.361\u201311.745] internally and 16.871 [12.546\u201324.237] versus 13.472 [9.380\u201319.599] externally;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.05). SNE showed strong agreement with LGE for scar size (internal\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      =0.839; external\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      =0.816;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001) and transmurality (internal\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      =0.792; external\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      =0.758;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001) with minimal biases (scar size: 2.490% internal, 2.222% external; transmurality: 2.984% internal, 2.225% external), indicating accurate scar depiction and robust generalizability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>SNE demonstrated strong agreement with LGE in quantitative assessment of acute myocardial infarction scar, with comparable or improved image quality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circimaging.125.018967","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T10:00:16Z","timestamp":1767002416000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":3,"title":["Synthetic Contrast-Free LGE via Diffusion-Based Framework in Acute MI for Image Quality and Quantitative Scar Analysis"],"prefix":"10.1161","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7851-4341","authenticated-orcid":true,"given":"Jing","family":"Qi","sequence":"first","affiliation":[{"name":"Medical Big Data Research Center, Chinese PLA General Hospital, Beijing (J.Q., X.Y., M.H., J.L., K.H.)."},{"name":"Chinese PLA Medical 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Beijing."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianing","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Radiology (J.C., Y.Z., T.L.), The First Medical Center, Chinese PLA General Hospital, Beijing."},{"name":"Department of Radiology, Beijing Jishuitan Hospital, Capital Medical University, China (J.C.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiology (J.C., Y.Z., T.L.), The First Medical Center, Chinese PLA General Hospital, Beijing."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianan","family":"Li","sequence":"additional","affiliation":[{"name":"Medical Big Data Research Center, Chinese PLA General Hospital, Beijing (J.Q., X.Y., M.H., J.L., K.H.)."},{"name":"Chinese PLA Medical School (J.Q., X.Y., M.H., J.L.), The First Medical Center, Chinese PLA General Hospital, 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