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Commercially available DL-based MR image enhancement software was utilized for image post-processing. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of enhancing lesions were measured. For qualitative assessment and diagnostic performance evaluation, two radiologists graded the overall image quality, noise, and artifacts of each image and the conspicuity of visible lesions. The Wilcoxon signed-rank test and regression analyses with generalized estimating equations (GEEs) were used for statistical analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      For MR images that were not previously processed using other DL-based methods, SNR and CNR were higher in the DL-enhanced images than in the standard images (438.3 vs. 661.1,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01; 173.9 vs. 223.5,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). Overall image quality and noise were improved in the DL images (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01, average score-5 proportion 38% vs. 65%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01, 43% vs. 74%), whereas artifacts did not significantly differ (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2265\u20090.07). Sensitivity was increased after post-processing from 79 to 86% (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.02), especially for lesions smaller than 5\u2009mm (69\u00a0to\u00a078%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.03), and changes in specificity (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.24) and average false-positive (FP) count (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.18) were not significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DL image enhancement improves the image quality and diagnostic performance of 3D contrast-enhanced T1-weighted BB MR imaging for the detection of small brain metastases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Can deep learning (DL)-based image enhancement improve the image quality and diagnostic performance of 3D contrast-enhanced T1-weighted black blood (BB) MR imaging for brain metastases?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>DL-based image enhancement improved image quality of thin slice BB MR images and sensitivity for brain metastasis, particularly for lesions smaller than 5<\/jats:italic>\n                      \u2009\n                      <jats:italic>mm<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>DL-based image enhancement on BB images may assist in the accurate diagnosis of brain metastasis by achieving better sensitivity while maintaining comparable specificity<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11920-7","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T18:16:55Z","timestamp":1754677015000},"page":"928-938","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning-based image enhancement for improved black blood imaging in brain metastasis"],"prefix":"10.1007","volume":"36","author":[{"given":"Gaeun","family":"Oh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungyoon","family":"Paik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Won","family":"Jo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hye Jeong","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5625-5921","authenticated-orcid":false,"given":"Roh-Eul","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung Hong","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"11920_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s41572-018-0055-y","volume":"5","author":"AS Achrol","year":"2019","unstructured":"Achrol AS, Rennert RC, Anders C et al (2019) Brain metastases. 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Dr. Seung Hong Choi is an external director of AIRS Medical Inc. Roh-Eul Yoo is a Deputy Editor of\n                      European Radiology\n                      . She has not taken part in the review or selection processes of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was not required for this study because of its retrospective nature.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This study was approved by the institutional review board of Seoul National University Hospital (IRB No. 2311-047-1482).","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}