{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T11:00:40Z","timestamp":1784890840108,"version":"3.55.0"},"reference-count":25,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"132","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>\n                    <jats:bold>Objective:<\/jats:bold>\n                    This study aimed to investigate the effects of pre-magnetic resonance imaging (MRI) gradient noise exposure desensitization training on anxiety and MRI image quality in patients with brain tumors and to provide clinical evidence for optimizing MRI protocols.\n                    <jats:bold>Methods:<\/jats:bold>\n                    We conducted a retrospective analysis of 100 patients with brain tumors who underwent brain MRI, dividing them into an observation group (\n                    <jats:italic toggle=\"yes\">n\u2009<\/jats:italic>\n                    =\u200950, desensitization training\u2009+\u2009routine nursing) and a control group (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    \u2009=\u200950, routine nursing alone). Group comparisons were performed using analysis of covariance or the chi-square test. We evaluated self-rating anxiety scale (SAS) scores, MRI image quality (signal-to-noise ratio [SNR], artifact grade, and anatomical clarity), incidence of adverse reactions, and examination duration.\n                    <jats:bold>Results:<\/jats:bold>\n                    No significant difference in SAS score was observed between the two groups 15\u2009min before examination (\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    &gt; 0.05). After the examination, the observation group had a significantly lower adjusted mean SAS score (38.62, 95% CI: 37.48\u201339.76) than the control group (45.53, 95% CI: 44.39\u201346.67) (\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    &lt; 0.001), a higher proportion of excellent image quality (42\/50, 84.0% versus 28\/50, 56.0%,\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    \u2009=\u20090.002), superior signal-to-noise ratio (SNR) and anatomical clarity, lower artifact grades (all\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    &lt; 0.05), a lower incidence of adverse reaction (6.00% versus 20.00%,\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    \u2009=\u20090.074), and a shorter examination duration (28.45\u2009\u00b1\u20093.16\u2009min versus 33.72\u2009\u00b1\u20094.23\u2009min,\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    &lt; 0.05).\n                    <jats:bold>Conclusion<\/jats:bold>\n                    : Pre-MRI gradient noise desensitization training effectively alleviates anxiety, shortens examination duration, and improves MRI image quality in patients with brain tumors, supporting its clinical promotion value.\n                  <\/jats:p>","DOI":"10.4103\/nah.nah_39_26","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:00:38Z","timestamp":1783569638000},"page":"633-639","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Retrospective Study on the Effect of Gradient Noise Exposure Desensitization Training prior to Magnetic Resonance Imaging on Anxiety Levels and Image Quality in Patients with Brain Tumors"],"prefix":"10.4103","volume":"28","author":[{"given":"XueTong","family":"Cai","sequence":"first","affiliation":[{"name":"Faculty of Science, University of Queensland, St. Lucia, Brisbane, Queensland, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZiYi","family":"Gu","sequence":"additional","affiliation":[{"name":"Radiological department, Beijing Fengtai Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"R1-20260724","doi-asserted-by":"crossref","first-page":"106405","DOI":"10.1016\/j.compbiomed.2022.106405","article-title":"Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools","volume":"152","author":"Ranjbarzadeh","year":"2023","journal-title":"Comput Biol 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