{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T08:54:09Z","timestamp":1791968049387,"version":"4.3.4"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 CA216840\u201001A1"],"award-info":[{"award-number":["R01 CA216840\u201001A1"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U54 CA199092"],"award-info":[{"award-number":["U54 CA199092"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Cardiovascular magnetic resonance (CMR) is a vital diagnostic tool in the management of cardiovascular diseases. The advent of advanced CMR technologies combined with artificial intelligence (AI) has the potential to simplify imaging, reduce image acquisition time without compromising image quality (IQ), and improve magnetic field uniformity. Here, we aim to implement two AI\u2010based deep learning techniques for automatic slice alignment and cardiac shimming and evaluate their performance in clinical cardiac magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Two deep neural networks were developed, trained, and validated on pre\u2010acquired cardiac MRI datasets (&gt;500 subjects) to achieve automatic slice planning and shimming (implemented in the scanner) for CMR. To examine the performance of our automated cardiac planning (EasyScan) and AI\u2010based shim (AI shim), two prospective studies were performed subsequently. For the EasyScan validation, 10 healthy subjects underwent two identical CMR protocols: with manual cardiac planning and with AI\u2010based EasyScan to assess protocol scan time difference and accuracy of cardiac plane prescriptions on a 1.5 T clinical MRI scanner. For the AI shim validation, a total of 20 subjects were recruited: 10 healthy and 10 cardio\u2010oncology patients with referrals for a CMR examination. Cine images were obtained with standard cardiac volume shim and with AI shim to assess signal\u2010to\u2010noise ratio (SNR), contrast\u2010to\u2010noise ratio (CNR), overall IQ (sharpness and MR image degradation), ejection fraction (EF), and absolute wall thickening. A hybrid statistical method using of nonparametric (Wilcoxon) and parametric (\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test) assessments was employed for statistical analyses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      CMR protocol with AI\u2010based plane prescriptions, EasyScan, minimized operator dependence and reduced overall scanning time by over 2 min (\u223c13\u00a0% faster,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001) compared to the protocol with manual cardiac planning. EasyScan plane prescriptions also demonstrated more accurate (less plane angulation errors from planes manually prescribed by a certified cardiac MRI technologist) cardiac planes than previously reported strategies. Additionally, AI shim resulted in improved B0 field homogeneity. Cine images obtained with AI shim revealed a significantly higher SNR (12.49%;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.002) than those obtained with volume shim (volume shim: 32.90\u00a0\u00b1 7.42\u00a0vs. AI shim: 37.01\u00a0\u00b1 8.87) for the left ventricle (LV) myocardium. LV myocardium CNR was 12.48% higher for cine imaging with AI shim (149.02\u00a0\u00b1 39.15) than volume shim (132.49\u00a0\u00b1 33.94). Images obtained with AI shim resulted in sharper images than those obtained with volume shim (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.012). The LVEF and absolute wall thickening also showed that differences exist between the two shimming methods. The LVEF by AI shim was shown to be slightly larger than LVEF by volume shim in two groups: 2.87% higher with AI shim for the healthy group and 1.70% higher with AI shim for the patient group. The LV absolute wall thickening (in mm) also showed that differences exist between shimming methods for each group with larger changes observed in the patient group (healthy: 3.31%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.234 and patient group: 7.29%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.059).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>CMR exams using EasyScan for cardiac planning demonstrated accelerated cardiac exam compared to the CMR protocol with manual cardiac planning. Improved and more uniform B0 magnetic field homogeneity also achieved using AI shim technique compared to volume shimming.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.15327","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T12:39:50Z","timestamp":1636375190000},"page":"129-143","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Implementation and prospective clinical validation of AI\u2010based planning and shimming techniques in cardiac MRI"],"prefix":"10.1002","volume":"49","author":[{"given":"Masoud","family":"Edalati","sequence":"first","affiliation":[{"name":"United Imaging Healthcare America, Inc.  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare America, Inc.  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary P.","family":"Watkins","sequence":"additional","affiliation":[{"name":"Cardiology Division Washington University School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Chen","sequence":"additional","affiliation":[{"name":"Cardiology Division Washington University School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Liu","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare America, Inc.  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare America, Inc.  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanli","family":"Song","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare America, Inc.  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samira","family":"Soleymani","sequence":"additional","affiliation":[{"name":"Department of Statistical and Actuarial Sciences University of Western Ontario  London Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel J.","family":"Lenihan","sequence":"additional","affiliation":[{"name":"Cardiology Division Washington University School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory M.","family":"Lanza","sequence":"additional","affiliation":[{"name":"Cardiology Division Washington University School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0b013e3181d44a8f"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2006.03.072"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/1532-429X-15-91"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-018-0484-5"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.764893"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2212010177"},{"key":"e_1_2_10_8_1","first-page":"541","article-title":"Automatic planning of the acquisition of cardiac MR images","volume":"2878","author":"Jackson CE","year":"2003","journal-title":"Med Image Comput Assist Interv"},{"key":"e_1_2_10_9_1","unstructured":"DwivediS VaidyaV MullickR FooT HoV.Methodology for rapid cardiac axis generation from non\u2010gated cardiac MR images. 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