{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:53:11Z","timestamp":1786978391070,"version":"build-2736575974"},"reference-count":21,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2018,10,17]],"date-time":"2018-10-17T00:00:00Z","timestamp":1539734400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["K25 CA176219"],"award-info":[{"award-number":["K25 CA176219"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R21 DK096282"],"award-info":[{"award-number":["R21 DK096282"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1 TR000445"],"award-info":[{"award-number":["UL1 TR000445"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The non\u2010uniform fast Fourier transform (NUFFT) involves interpolation of non\u2010uniformly sampled Fourier data onto a Cartesian grid, an interpolation that is slowed by complex, non\u2010local data access patterns. A faster NUFFT would increase the clinical relevance of the plethora of advanced non\u2010Cartesian acquisition methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Here we customize the NUFFT procedure for a radial trajectory and GPU architecture to eliminate the bottlenecks encountered when allowing for arbitrary trajectories and hardware. We call the result TRON, for\n                      <jats:bold>TR<\/jats:bold>\n                      ajectory\n                      <jats:bold>O<\/jats:bold>\n                      ptimized\n                      <jats:bold>N<\/jats:bold>\n                      UFFT. We benchmark the speed and accuracy TRON on a Shepp\u2010Logan phantom and on whole\u2010body continuous golden\u2010angle radial MRI.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>TRON was 6\u201330\u00d7 faster than the closest competitor, depending on test data set, and was the most accurate code tested.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Specialization of the NUFFT algorithm for a particular trajectory yielded significant speed gains. TRON can be easily extended to other trajectories, such as spiral and PROPELLER. TRON can be downloaded at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/github.com\/davidssmith\/TRON\">http:\/\/github.com\/davidssmith\/TRON<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.27497","type":"journal-article","created":{"date-parts":[[2018,10,17]],"date-time":"2018-10-17T11:54:28Z","timestamp":1539777268000},"page":"2064-2071","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Trajectory optimized NUFFT: Faster non\u2010Cartesian MRI reconstruction through prior knowledge and parallel architectures"],"prefix":"10.1002","volume":"81","author":[{"given":"David S.","family":"Smith","sequence":"first","affiliation":[{"name":"Institute of Imaging Science Vanderbilt University Medical Center Nashville Tennessee"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saikat","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Institute of Imaging Science Vanderbilt University Medical Center Nashville Tennessee"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seth A.","family":"Smith","sequence":"additional","affiliation":[{"name":"Institute of Imaging Science Vanderbilt University Medical Center Nashville Tennessee"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Brian Welch","sequence":"additional","affiliation":[{"name":"Institute of Imaging Science Vanderbilt University Medical Center Nashville Tennessee"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,10,17]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2002.807005"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.97598"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2007.909834"},{"key":"e_1_2_6_5_1","unstructured":"GregersonA.Implementing Fast MRI gridding on GPUs via CUDA. 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Technical Report #UCB\/EECS\u20102017\u201090; May 12 2017 UC Berkley;2017."},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25665"},{"key":"e_1_2_6_14_1","first-page":"17","volume-title":"Self\u2010gated Radial MRI for Respiratory Motion Compensation on Hybrid PET\/MR Systems","author":"Grimm R","year":"2013"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910370623"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25531"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1522-2594(200005)43:5<682::AID-MRM10>3.0.CO;2-G"},{"key":"e_1_2_6_18_1","unstructured":"SmithD.Raw array format. Git Repository 2018; SHA: 80ce83f."},{"key":"e_1_2_6_19_1","doi-asserted-by":"crossref","unstructured":"SmithD.Trajectory Optimized NUFFT. 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