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The individual applications of artificial intelligence-assisted compressed sensing (ACS) and deep learning-based reconstruction (DLR) technologies have demonstrated effectiveness in accelerated scanning. However, the synergistic potential of ACS combined with DLR at 5.0T remains unexplored. This study systematically evaluates the diagnostic efficacy of the integrated ACS-DLR technique for T2WI at 5.0T, comparing it to conventional parallel imaging (PI) protocols.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>The prospective analysis was performed on 98 participants who underwent brain T2WI scans using ACS, DLR, and PI techniques. Two observers evaluated the overall image quality, truncation artifacts, motion artifacts, cerebrospinal fluid flow artifacts, vascular pulsation artifacts, and the significance of lesions. Subjective rating differences among the three sequences were compared. Objective assessment involved the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in gray matter, white matter, and cerebrospinal fluid for each sequence. The SNR, CNR, and acquisition time of each sequence were compared.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The acquisition time for ACS and DLR was reduced by 78%. The overall image quality of DLR is higher than that of ACS (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) and equivalent to PI (<jats:italic>P<\/jats:italic>\u2009&gt;\u20090.05). The SNR of the DLR sequence is the highest, and the CNR of DLR is higher than that of the ACS sequence (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) and equivalent to PI (<jats:italic>P<\/jats:italic>\u2009&gt;\u20090.05).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The integration of ACS and DLR enables the ultrafast acquisition of brain T2WI while maintaining superior SNR and comparable CNR compared to PI sequences.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Clinical trial number<\/jats:title>\n            <jats:p>Not applicable.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12880-025-01763-5","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T09:48:49Z","timestamp":1751363329000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Accelerating brain T2-weighted imaging using artificial intelligence\u2013assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI"],"prefix":"10.1186","volume":"25","author":[{"given":"Yun","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoxin","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichao","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanjiang","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"1763_CR1","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41586-022-04492-9","volume":"603","author":"S Marek","year":"2022","unstructured":"Marek S, Tervo-Clemmens B, Calabro FJ, et al. 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