{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T20:32:05Z","timestamp":1786393925570,"version":"build-2736575974"},"reference-count":23,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB3811404"],"award-info":[{"award-number":["2023YFB3811404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20753"],"award-info":[{"award-number":["U24A20753"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L252089"],"award-info":[{"award-number":["L252089"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>As a key modality for cerebrovascular disease diagnosis, time\u2010of\u2010flight magnetic resonance angiography (TOF\u2010MRA) necessitates high spatial resolution and sensitivity. Although acceleration of MRA is critical for reducing scan time and increasing patient throughput, achieving diagnostic\u2010quality images at high undersampling rates remains a significant challenge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop a deep learning\u2010based post\u2010processing method that generates high\u2010quality MRA images from highly undersampled data, improving detection sensitivity for vascular abnormalities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective study employed a standard MRA scan as a baseline, followed by accelerated scans using compressed sensing (CS) with factors of 4, 6, 8, and 10, as well as sensitivity encoding (SENSE) with a factor of 4. A Dual\u2010Domain Projection Generative Adversarial Network (DDPGAN) was proposed, taking stacked 2D patches from accelerated MRA reconstructions as input and employing a dual\u2010headed discriminator to enhance image quality. The quantitative indicators included Peak Signal\u2010to\u2010Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Mean Squared Error (NMSE), and Signal\u2010to\u2010Noise Ratio (SNR). Qualitative assessment was conducted using a 5\u2010point reader scale focusing on image clarity and diagnostic confidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Sixty\u2010four patients (53\u00a0\u00b1\u00a016 years; 26 men) were included. DDPGAN outperformed other deep learning models across all qualitative and quantitative metrics, achieving PSNR\u00a0=\u00a031.98\u00a0dB, SSIM\u00a0=\u00a00.88, NMSE\u00a0=\u00a00.052, and SNR\u00a0=\u00a056.82 for CS\u00d710. Qualitatively, CS\u00d74 images improved from poor (reader1: 3.50\u00a0\u00b1\u00a00.50; reader2: 3.64\u00a0\u00b1\u00a00.48) to good\/excellent (reader1: 4.64\u00a0\u00b1\u00a00.48; reader2: 4.93\u00a0\u00b1\u00a00.26), surpassing even standard scans (reader 1: 3.93\u00a0\u00b1\u00a00.707, reader 2: 4.71\u00a0\u00b1\u00a00.808). Comparative studies confirmed the robustness of DDPGAN across diverse acceleration scenarios, with significant improvements in the visualization of major cerebral vascular structures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The DDPGAN framework significantly enhances accelerated MRA image quality, enabling high detail preservation and diagnostic confidence across various acceleration scenarios. Its dual\u2010domain design supports efficient, high\u2010quality MRA examinations, indicating strong clinical potential particularly in the evaluation of cerebrovascular disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70567","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T07:54:29Z","timestamp":1783670069000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing compressed sensing and parallel imaging accelerated magnetic resonance angiography using a dual\u2010domain projection generative adversarial network"],"prefix":"10.1002","volume":"53","author":[{"given":"Libo","family":"Xu","sequence":"first","affiliation":[{"name":"Shanghai Key Laboratory of Magnetic Resonance, School of Physics East China Normal University  Shanghai China"},{"name":"Institute of Magnetic Resonance and Molecular Imaging in Medicine East China Normal University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueyan","family":"Bian","sequence":"additional","affiliation":[{"name":"Department of Radiology Beijing Chaoyang Hospital Capital Medical University  Beijing China"},{"name":"Key Lab of Medical Engineering for Cardiovascular Disease Ministry of Education  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Gan","sequence":"additional","affiliation":[{"name":"RadioDynamic Medical  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyang","family":"Pan","sequence":"additional","affiliation":[{"name":"RadioDynamic Medical  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristen W","family":"Yeom","sequence":"additional","affiliation":[{"name":"Department of Radiology Lucile Packard Children's Hospital  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Moseley","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology Beijing Chaoyang Hospital Capital Medical University  Beijing China"},{"name":"Key Lab of Medical Engineering for Cardiovascular Disease Ministry of Education  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan\u2010Jie","family":"Gong","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Magnetic Resonance, School of Physics East China Normal University  Shanghai China"},{"name":"Institute of Magnetic Resonance and Molecular Imaging in Medicine East China Normal University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/792834"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurad.2013.03.004"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurad.2014.08.002"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13244\u2010013\u20100264\u20106"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2010.2085084"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1364\/ao.54.000c23"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10171"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22161"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21391"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1522\u20102594(199911)42:53.0.CO;2\u2010S"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3528402"},{"key":"e_1_2_10_13_1","doi-asserted-by":"crossref","unstructured":"LiH ChiewM DragonuI et\u00a0al.Few\u2010shot learning for highly accelerated 3D time\u2010of\u2010flight MRA reconstruction.bioRxiv. 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