{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T07:52:26Z","timestamp":1792050746620,"version":"4.4.0"},"reference-count":48,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"9","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Cardiovascular Imaging"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:sec>\n            <jats:title>BACKGROUND:<\/jats:title>\n            <jats:p>Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard technique for diagnosing myocardial infarction (MI), which, however, poses risks due to gadolinium contrast usage. Techniques enabling MI assessment based on contrast-free CMR are desirable to overcome the limitations associated with contrast enhancement.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS:<\/jats:title>\n            <jats:p>We introduce a novel deep generative learning method, termed cine-generated enhancement (CGE), which transforms standard contrast-free cine CMR into LGE-equivalent images for MI assessment. CGE features with multislice spatiotemporal feature extractor, enhancement contrast modulation, and sophisticated loss function. Data from 430 patients with acute MI from 3 centers were collected. After image quality control, 1525 pairs (289 patients) of center I were used for training, and 293 slices (52 patients) of the same center were reserved for internal testing. The 40 patients (401 slices) of the other 2 centers were used for external testing. The CGE robustness was further tested in 20 normal subjects in a public cine CMR data set. CGE images were compared with LGE for image quality assessment and MI quantification regarding scar size and transmurality.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS:<\/jats:title>\n            <jats:p>\n              The CGE method produced images of superior quality to LGE in both internal and external data sets. There was a significant (\n              <jats:italic toggle=\"yes\">P<\/jats:italic>\n              &lt;0.001) correlation between CGE and LGE measurements of scar size (Pearson correlation, 0.79\/0.80; intraclass correlation coefficient, 0.79\/0.77) and transmurality (Pearson correlation, 0.76\/0.64; intraclass correlation coefficient, 0.76\/0.63) in internal\/external data set. Considering all data sets, CGE demonstrated high sensitivity (91.27%) and specificity (95.83%) in detecting scars. Realistic enhancement images were obtained for the normal subjects in the public data set without false positive subjects.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSIONS:<\/jats:title>\n            <jats:p>CGE achieved superior image quality to LGE and accurate scar delineation in patients with acute MI of both internal and external data sets. CGE can significantly simplify the CMR examination, reducing scan times and risks associated with gadolinium-based contrasts, which are crucial for acute patients.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/circimaging.124.016786","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T13:32:44Z","timestamp":1726061564000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":27,"title":["Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning"],"prefix":"10.1161","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4709-5185","authenticated-orcid":true,"given":"Haikun","family":"Qi","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, China (H.Q., P.Q.)."},{"name":"Shanghai Clinical Research and Trial Center, China (H.Q.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3086-4697","authenticated-orcid":true,"given":"Pengfang","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, China (H.Q., P.Q.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Langlang","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Longyan First Hospital, Affiliated to Fujian Medical University, China (L.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2718-8416","authenticated-orcid":true,"given":"Binghua","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China (B.C., D.A., L.-M.W.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6195-2246","authenticated-orcid":true,"given":"Dongaolei","family":"An","sequence":"additional","affiliation":[{"name":"Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China (B.C., D.A., L.-M.W.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lian-Ming","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China (B.C., D.A., L.-M.W.)."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2020.11.010"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1161\/hc3501.096798"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2009.07.027"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2005.11.065"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2009.06.059"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.cir.0000027818.15792.1e"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.0000142045.22628.74"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2019.05.024"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.109.865352"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.09.001"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019182304"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101568"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101668"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110694"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.121.054432"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.122.060137"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2012.05.008"},{"key":"e_1_3_3_20_2","doi-asserted-by":"crossref","unstructured":"Liu W Anguelov D Erhan D Szegedy C Reed S Fu CY Berg AC. 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