{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:50:36Z","timestamp":1791521436141,"version":"4.3.4"},"reference-count":47,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a vessel wall imaging (VWI)\u2010dedicated image restoration model enabling high\u2010resolution whole\u2010brain VWI in under 6\u2009min.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A transformer\u2010based 2.5D model was trained in a supervised manner to enhance the image quality of CAIPIRINHA 2\u2009\u00d7\u20092\u2010accelerated VWI acquisitions to a level comparable to that of GRAPPA twofold\u2010accelerated scans. The majority of\n                      <jats:italic>k<\/jats:italic>\n                      \u2010space data used for training and testing were acquired on a Siemens 3\u2009T system equipped with a standard 20\u2010channel head\u2013neck coil. Region\u2010of\u2010interest fine\u2010tuning and slice\u2010to\u2010volume reconstruction were devised to balance global structural fidelity with local vascular precision and 3D continuity. Model performance was evaluated both quantitatively and qualitatively through single\u2010center retrospective testing and prospective testing in multi\u2010center, generalized settings. Integration of the model into the vendor's inline image reconstruction pipeline was also investigated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with the input images, model outputs improved peak SNR by more than 7\u2009dB and structural similarity index measure by 0.19 in the retrospective test set. When applied to CAIPIRINHA fourfold\u2010accelerated data that otherwise underwent offline or inline GRAPPA reconstruction, the model significantly enhanced vessel wall SNR, wall\u2010to\u2010lumen CNR, wall\u2010to\u2010cerebrospinal fluid CNR, and image quality scores. These improvements were sustained in an independent cohort from different institutions and reproduced in a dataset acquired with smaller field of view and a 64\u2010channel coil, supporting the model's robustness and generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed inline\u2010integrable image restoration model enables clinically viable whole\u2010brain VWI in under 6\u2009min using standard 20\u2010channel coils, offering a practical approach to improve patient throughput without compromising diagnostic quality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70594","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:27:56Z","timestamp":1791520076000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Inline\u2010Integrable Image Restoration Model With\n                    <scp>ROI<\/scp>\n                    Fine\u2010Tuning and Slice\u2010to\u2010Volume Reconstruction for Accelerated Whole\u2010Brain Vessel Wall Imaging"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2572-8220","authenticated-orcid":false,"given":"Pengcheng","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Radiology University of Southern California  Los Angeles California USA"},{"name":"Department of Biomedical Engineering University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Southern California  Los Angeles California USA"},{"name":"Department of Biomedical Engineering University of Southern California  Los Angeles California 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Chaoyang Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7111-9066","authenticated-orcid":false,"given":"Qingle","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven Y.","family":"Cen","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5133-4903","authenticated-orcid":false,"given":"Sophia X.","family":"Cui","sequence":"additional","affiliation":[{"name":"Siemens Medical Solutions  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Zhang","sequence":"additional","affiliation":[{"name":"GE HealthCare  Beijing 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