{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T15:58:12Z","timestamp":1783353492697,"version":"3.54.6"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004721","name":"The University of Tokyo","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004721","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Super-resolution deep learning reconstruction (SR-DLR) is a promising tool for improving image quality by enhancing spatial resolution compared to conventional deep learning reconstruction (DLR). This study aimed to evaluate whether SR-DLR improves microbleed detection and visualization in brain magnetic resonance imaging (MRI) compared to DLR. This retrospective study included 69 patients (66.2\u2009\u00b1\u200913.8\u00a0years; 44 females) who underwent 3\u00a0T brain MRI with T2*-weighted 2D gradient echo and 3D flow-sensitive black blood imaging (reference standard) between June and August 2024. T2*-weighted images were reconstructed using SR-DLR and DLR. Three blinded readers detected microbleeds and assessed image quality, including microbleed and normal structure visibility, sharpness, noise, artifacts, and overall quality. Quantitative analysis involved measuring signal intensity along the septum pellucidum. Microbleed detection performance was analyzed using jackknife alternative free-response receiver operating characteristic analysis, while image quality was analyzed using the Wilcoxon signed-rank test and paired\n                    <jats:italic>t<\/jats:italic>\n                    -test. SR-DLR significantly outperformed DLR in microbleed detection (figure of merit: 0.690\n                    <jats:italic>vs.<\/jats:italic>\n                    0.645,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). SR-DLR also demonstrated higher sensitivity for microbleed detection. Qualitative analysis showed better microbleed visualization for two readers (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001) and improved image sharpness for all readers (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009\u2264\u20090.008). Quantitative analysis revealed enhanced sharpness, especially in full width at half maximum and edge rise slope (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). SR-DLR improved image sharpness and quality, leading to better microbleed detection and visualization in brain MRI compared to DLR.\n                  <\/jats:p>","DOI":"10.1007\/s10278-025-01522-6","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:58:25Z","timestamp":1745927905000},"page":"805-814","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Super-Resolution Deep Learning Reconstruction for T2*-Weighted Images: Improvement in Microbleed Lesion Detection and Image Quality"],"prefix":"10.1007","volume":"39","author":[{"given":"Yusuke","family":"Asari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0324-6562","authenticated-orcid":false,"given":"Koichiro","family":"Yasaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuki","family":"Endo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Kanzawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naomasa","family":"Okimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuke","family":"Watanabe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichi","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiori","family":"Amemiya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigeru","family":"Kiryu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osamu","family":"Abe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"1522_CR1","doi-asserted-by":"publisher","unstructured":"Hardy JA, Higgins GA: Alzheimer\u2019s Disease: The Amyloid Cascade Hypothesis. Science. https:\/\/doi.org\/10.1126\/science.1566067, April 10, 1992","DOI":"10.1126\/science.1566067"},{"key":"1522_CR2","doi-asserted-by":"publisher","unstructured":"Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W, Jessen F, Karlawish J, Liu E, Molinuevo JL, Montine T, Phelps C, Rankin KP, Rowe CC, Scheltens P, Siemers E, Snyder HM, Sperling R; Contributors: NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease. Alzheimers Dement. https:\/\/doi.org\/10.1016\/j.jalz.2018.02.018. April 14, 2018","DOI":"10.1016\/j.jalz.2018.02.018"},{"key":"1522_CR3","doi-asserted-by":"publisher","unstructured":"Penninkilampi R, Brothers HM, Eslick GD: Safety and Efficacy of Anti-Amyloid-\u03b2 Immunotherapy in Alzheimer\u2019s Disease: A Systematic Review and Meta-Analysis. J Neuroimmune Pharmacol. https:\/\/doi.org\/10.1007\/s11481-016-9722-5. March 12, 2017","DOI":"10.1007\/s11481-016-9722-5"},{"key":"1522_CR4","doi-asserted-by":"publisher","first-page":"211","DOI":"10.14283\/jpad.2022.21","volume":"9","author":"J Barakos","year":"2022","unstructured":"Barakos J, Purcell D, Suhy J, Chalkias S, Burkett P, Marsica Grassi C, Castrillo-Viguera C, Rubino I, Vijverberg E: Detection and Management of Amyloid-Related Imaging Abnormalities in Patients with Alzheimer\u2019s Disease Treated with Anti-Amyloid Beta Therapy. J Prev Alzheimers Dis. 9:211-220, 2022","journal-title":"J Prev Alzheimers Dis."},{"key":"1522_CR5","doi-asserted-by":"publisher","unstructured":"Salloway S, Sperling R, Gilman S, Fox NC, Blennow K, Raskind M, Sabbagh M, Honig LS, Doody R, van Dyck CH, Mulnard R, Barakos J, Gregg KM, Liu E, Lieberburg I, Schenk D, Black R, Grundman M; Bapineuzumab 201 Clinical Trial Investigators: A phase 2 multiple ascending dose trial of bapineuzumab in mild to moderate Alzheimer disease. Neurology. https:\/\/doi.org\/10.1212\/WNL.0b013e3181c67808. December 15, 2009","DOI":"10.1212\/WNL.0b013e3181c67808"},{"key":"1522_CR6","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1097\/WAD.0b013e3181c53b00","volume":"24","author":"RS Black","year":"2010","unstructured":"Black RS, Sperling RA, Safirstein B, Motter RN, Pallay A, Nichols A, Grundman M: A single ascending dose study of bapineuzumab in patients with Alzheimer disease. Alzheimer Dis Assoc Disord 24:198-203, 2010","journal-title":"Alzheimer Dis Assoc Disord"},{"key":"1522_CR7","doi-asserted-by":"publisher","unstructured":"Ferrero J, Williams L, Stella H, Leitermann K, Mikulskis A, O'Gorman J, Sevigny J: First-in-human, double-blind, placebo-controlled, single-dose escalation study of aducanumab (BIIB037) in mild-to-moderate Alzheimer\u2019s disease. Alzheimers Dement. https:\/\/doi.org\/10.1016\/j.trci.2016.06.002. June 20, 2016","DOI":"10.1016\/j.trci.2016.06.002"},{"key":"1522_CR8","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.jalz.2011.05.2351","volume":"7","author":"RA Sperling","year":"2011","unstructured":"Sperling RA, Jack CR Jr, Black SE, Frosch MP, Greenberg SM, Hyman BT, Scheltens P, Carrillo MC, Thies W, Bednar MM, Black RS, Brashear HR, Grundman M, Siemers ER, Feldman HH, Schindler RJ: Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer\u2019s Association Research Roundtable Workgroup. Alzheimers Dement. 7:367-85, 2011","journal-title":"Alzheimers Dement."},{"key":"1522_CR9","doi-asserted-by":"publisher","first-page":"E19","DOI":"10.3174\/ajnr.A7586","volume":"43","author":"PM Cogswell","year":"2022","unstructured":"Cogswell PM, Barakos JA, Barkhof F, Benzinger TS, Jack CR Jr, Poussaint TY, Raji CA, Ramanan VK, Whitlow CT: Amyloid-Related Imaging Abnormalities with Emerging Alzheimer Disease Therapeutics: Detection and Reporting Recommendations for Clinical Practice. AJNR Am J Neuroradiol. 43:E19-E35, 2022.","journal-title":"AJNR Am J Neuroradiol."},{"key":"1522_CR10","doi-asserted-by":"publisher","unstructured":"Yasaka K, Sato C, Hirakawa H, Fujita N, Kurokawa M, Watanabe Y, Kubo T, Abe O: Impact of deep learning on radiologists and radiology residents in detecting breast cancer on CT: a cross-vendor test study. Clin Radiol. https:\/\/doi.org\/10.1016\/j.crad.2023.09.022. October 13, 2023","DOI":"10.1016\/j.crad.2023.09.022"},{"key":"1522_CR11","doi-asserted-by":"publisher","unstructured":"Kiryu S, Akai H, Yasaka K, Tajima T, Kunimatsu A, Yoshioka N, Akahane M, Abe O, Ohtomo K: Clinical Impact of Deep Learning Reconstruction in MRI. Radiographics. https:\/\/doi.org\/10.1148\/rg.220133. May 18, 2023","DOI":"10.1148\/rg.220133"},{"issue":"9","key":"1522_CR12","doi-asserted-by":"publisher","first-page":"6118","DOI":"10.1007\/s00330-022-08729-z","volume":"32","author":"K Yasaka","year":"2022","unstructured":"Yasaka K, Tanishima T, Ohtake Y, et al. Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes. Eur Radiol. 2022;32(9):6118\u20136125. https:\/\/doi.org\/10.1007\/s00330-022-08729-z.","journal-title":"Eur Radiol."},{"key":"1522_CR13","doi-asserted-by":"publisher","unstructured":"Yasaka K, Tanishima T, Ohtake Y, Tajima T, Akai H, Ohtomo K, Abe O, Kiryu S: Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-022-08729-z. March 29 2022","DOI":"10.1007\/s00330-022-08729-z"},{"key":"1522_CR14","doi-asserted-by":"publisher","unstructured":"Tajima T, Akai H, Sugawara H, Furuta T, Yasaka K, Kunimatsu A, Yoshioka N, Akahane M, Abe O, Ohtomo K, Kiryu S: Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer. Magn Reson Imaging. https:\/\/doi.org\/10.1016\/j.mri.2022.06.014. June 27, 2022","DOI":"10.1016\/j.mri.2022.06.014"},{"key":"1522_CR15","doi-asserted-by":"publisher","unstructured":"Akai H, Yasaka K, Sugawara H, Tajima T, Akahane M, Yoshioka N, Ohtomo K, Abe O, Kiryu S: Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study. Magn Reson Med Sci. https:\/\/doi.org\/10.2463\/mrms.mp.2022-0020. July 9, 2022","DOI":"10.2463\/mrms.mp.2022-0020"},{"key":"1522_CR16","doi-asserted-by":"publisher","unstructured":"Tajima T, Akai H, Yasaka K, Kunimatsu A, Yamashita Y, Akahane M, Yoshioka N, Abe O, Ohtomo K, Kiryu S: Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images. Clin Radiol. https:\/\/doi.org\/10.1016\/j.crad.2022.08.127. September 15, 2022","DOI":"10.1016\/j.crad.2022.08.127"},{"key":"1522_CR17","doi-asserted-by":"publisher","unstructured":"Yasaka K, Akai H, Sugawara H, Tajima T, Akahane M, Yoshioka N, Kabasawa H, Miyo R, Ohtomo K, Abe O, Kiryu S: Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography. Jpn J Radiol. https:\/\/doi.org\/10.1007\/s11604-021-01225-2. December 1, 2021","DOI":"10.1007\/s11604-021-01225-2"},{"key":"1522_CR18","doi-asserted-by":"publisher","unstructured":"Tajima T, Akai H, Yasaka K, Kunimatsu A, Akahane M, Yoshioka N, Abe O, Ohtomo K, Kiryu S: Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach. Magn Reson Imaging. https:\/\/doi.org\/10.1016\/j.mri.2022.04.005. April 30, 2022","DOI":"10.1016\/j.mri.2022.04.005"},{"key":"1522_CR19","doi-asserted-by":"publisher","unstructured":"Matsuo K, Nakaura T, Morita K, Uetani H, Nagayama Y, Kidoh M, Hokamura M, Yamashita Y, Shinoda K, Ueda M, Mukasa A, Hirai T: Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images. Neuroradiology. https:\/\/doi.org\/10.1007\/s00234-023-03212-y. September 7, 2023","DOI":"10.1007\/s00234-023-03212-y"},{"key":"1522_CR20","doi-asserted-by":"publisher","unstructured":"Hokamura M, Uetani H, Nakaura T, Matsuo K, Morita K, Nagayama Y, Kidoh M, Yamashita Y, Ueda M, Mukasa A, Hirai T: Exploring the impact of super-resolution deep learning on MR angiography image quality. Neuroradiology. https:\/\/doi.org\/10.1007\/s00234-023-03271-1. December 27, 2023","DOI":"10.1007\/s00234-023-03271-1"},{"key":"1522_CR21","doi-asserted-by":"publisher","unstructured":"Yasaka K, Uehara S, Kato S, Watanabe Y, Tajima T, Akai H, Yoshioka N, Akahane M, Ohtomo K, Abe O, Kiryu S: Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction. J Imaging Inform Med. https:\/\/doi.org\/10.1007\/s10278-024-01112-y. April 26, 2024","DOI":"10.1007\/s10278-024-01112-y"},{"key":"1522_CR22","doi-asserted-by":"publisher","unstructured":"Yasaka K, Kanzawa J, Nakaya M, Kurokawa R, Tajima T, Akai H, Yoshioka N, Akahane M, Ohtomo K, Abe O, Kiryu S: Super-resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict. Acad Radiol. https:\/\/doi.org\/10.1016\/j.acra.2024.06.010. June 18, 2024","DOI":"10.1016\/j.acra.2024.06.010"},{"key":"1522_CR23","doi-asserted-by":"publisher","unstructured":"Wu X, Xu S, Zhang Y, Ye Y, Zhang D, Yu L, Zhang R, Sun J, Huang P: Validation of a Deep Learning-Based Method for Accelerating Susceptibility-Weighted Imaging in Clinical Settings. NMR Biomed. https:\/\/doi.org\/10.1002\/nbm.5320. January 8, 2025","DOI":"10.1002\/nbm.5320"},{"key":"1522_CR24","doi-asserted-by":"publisher","first-page":"5679","DOI":"10.1007\/s00330-022-08638-1","volume":"32","author":"C Duan","year":"2022","unstructured":"Duan C, Xiong Y, Cheng K, Xiao S, Lyu J, Wang C, Bian X, Zhang J, Zhang D, Chen L, Zhou X, Lou X: Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging. Eur Radiol. 32: 5679-5687, 2022","journal-title":"Eur Radiol."},{"key":"1522_CR25","doi-asserted-by":"publisher","unstructured":"Kidoh M, Shinoda K, Kitajima M, Isogawa K, Nambu M, Uetani H, Morita K, Nakaura T, Tateishi M, Yamashita Y, Yamashita Y: Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers. Magn Reson Med Sci. https:\/\/doi.org\/10.2463\/mrms.mp.2019-0018. September 4, 2019","DOI":"10.2463\/mrms.mp.2019-0018"},{"key":"1522_CR26","doi-asserted-by":"publisher","unstructured":"Higaki T, Tatsugami F, Fujioka C, Sakane H, Nakamura Y, Baba Y, Iida M, Awai K: Visualization of simulated small vessels on computed tomography using a model-based iterative reconstruction technique. Data Brief. https:\/\/doi.org\/10.1016\/j.dib.2017.06.024. June 16, 2017","DOI":"10.1016\/j.dib.2017.06.024"},{"key":"1522_CR27","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1148\/radiol.2021203071","volume":"299","author":"S Haller","year":"2021","unstructured":"Haller S, Haacke EM, Thurnher MM, Barkhof F: Susceptibility-weighted Imaging: Technical Essentials and Clinical Neurologic Applications. Radiology 299:3-26, 2021","journal-title":"Radiology"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01522-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01522-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01522-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:46:09Z","timestamp":1771526769000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01522-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,29]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["1522"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01522-6","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,29]]},"assertion":[{"value":"23 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval was obtained from the ethics committee of the University of Tokyo.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}