{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T19:50:13Z","timestamp":1789761013790,"version":"4.0.1"},"reference-count":27,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:p>The purpose of the current study was to develop a deep learning technique called Golden\u2010angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast\u2010enhanced 4D MRI acquired with golden\u2010angle radial k\u2010space trajectories. GRASPnet operates in the image\u2010time space and does not use explicit data consistency to minimize the reconstruction time. Three different network architectures were developed: (1) GRASPnet\u20102D: 2D convolutional kernels (x,y) and coil and contrast dimensions collapsed into a single combined dimension; (2) GRASPnet\u20103D: 3D kernels (x,y,t); and (3) GRASPnet\u20102D\u2009+\u2009time: two 3D kernels to first exploit spatial correlations (x,y,1) followed by temporal correlations (1,1,t). The networks were trained using iterative GRASP reconstruction as the reference. Free\u2010breathing 3D abdominal imaging with contrast injection was performed on 33 patients with liver lesions using a T1\u2010weighted golden\u2010angle stack\u2010of\u2010stars pulse sequence. Ten datasets were used for testing. The three GRASPnet architectures were compared with iterative GRASP results using quantitative and qualitative analysis, including impressions from two body radiologists. The three GRASPnet techniques reduced the reconstruction time to about 13\u2009s with similar results with respect to iterative GRASP. Among the GRASPnet techniques, GRASPnet\u20102D\u2009+\u2009time compared favorably in the quantitative analysis. Spatiotemporal deep learning enables reconstruction of dynamic 4D contrast\u2010enhanced images in a few seconds, which would facilitate translation to clinical practice of compressed sensing methods that are currently limited by long reconstruction times.<\/jats:p>","DOI":"10.1002\/nbm.4861","type":"journal-article","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T08:03:21Z","timestamp":1666944201000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["GRASPNET: Fast spatiotemporal deep learning reconstruction of golden\u2010angle radial data for free\u2010breathing dynamic contrast\u2010enhanced magnetic resonance imaging"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1853-6677","authenticated-orcid":false,"given":"Ramin","family":"Jafari","sequence":"first","affiliation":[{"name":"Department of Medical Physics Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard Kinh Gian","family":"Do","sequence":"additional","affiliation":[{"name":"Department of Radiology Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Divina","family":"LaGratta","sequence":"additional","affiliation":[{"name":"Department of Radiology Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maggie","family":"Fung","sequence":"additional","affiliation":[{"name":"GE Healthcare  Waukesha Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ersin","family":"Bayram","sequence":"additional","affiliation":[{"name":"GE Healthcare  Waukesha Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ty","family":"Cashen","sequence":"additional","affiliation":[{"name":"GE Healthcare  Waukesha Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3782-4930","authenticated-orcid":false,"given":"Ricardo","family":"Otazo","sequence":"additional","affiliation":[{"name":"Department of Medical Physics Memorial Sloan Kettering Cancer Center  New York New York USA"},{"name":"Department of Radiology Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000586"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24980"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0b013e318271869c"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000299"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0253078"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26714"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27903"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26977"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2760978"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature25988"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28917"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28420"},{"key":"e_1_2_8_14_1","volume-title":"Deep MRI reconstruction with radial subsampling. Vol. 12031","author":"Yiasemis G","year":"2022"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.03.034"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ab9358"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105321"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmr.2007.06.012"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3144619"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010020\u201070551\u20108"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24643"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21481"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24267"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28546"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/53\/9\/010"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010020\u201007263\u20100"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3084288"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15217"}],"container-title":["NMR in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/nbm.4861","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/nbm.4861","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/nbm.4861","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T14:31:22Z","timestamp":1760970682000},"score":1,"resource":{"primary":{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/10.1002\/nbm.4861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,25]]},"references-count":27,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["10.1002\/nbm.4861"],"URL":"https:\/\/doi.org\/10.1002\/nbm.4861","archive":["Portico"],"relation":{},"ISSN":["0952-3480","1099-1492"],"issn-type":[{"value":"0952-3480","type":"print"},{"value":"1099-1492","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,25]]},"assertion":[{"value":"2022-03-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-10-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-11-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e4861"}}