{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T07:53:23Z","timestamp":1790927603199,"version":"4.1.0"},"reference-count":16,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100009553","name":"Agentura Pro Zdravotnick\u00fd V\u00fdzkum \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["NW26\u201008\u201000211"],"award-info":[{"award-number":["NW26\u201008\u201000211"]}],"id":[{"id":"10.13039\/501100009553","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001823","name":"Ministerstvo \u0160kolstv\u00ed, Ml\u00e1de\u017ee a T\u011blov\u00fdchovy","doi-asserted-by":"publisher","award":["CZ.02.01.01\/00\/23 015\/0008205"],"award-info":[{"award-number":["CZ.02.01.01\/00\/23 015\/0008205"]}],"id":[{"id":"10.13039\/501100001823","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001823","name":"Ministerstvo \u0160kolstv\u00ed, Ml\u00e1de\u017ee a T\u011blov\u00fdchovy","doi-asserted-by":"publisher","award":["LM2023050"],"award-info":[{"award-number":["LM2023050"]}],"id":[{"id":"10.13039\/501100001823","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To improve TCN\u2010based gradient\u2010waveform prediction with respect to acquisition efficiency and inference speed, and to evaluate its use for radial imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Gradient waveforms were measured using a thin\u2010slice method on a preclinical MRI system. Separate TCN models were trained for each physical gradient axis to predict both gradient and  terms. The framework was evaluated using phantom and in vivo radial acquisitions across a range of receiver bandwidths and compared with measured trajectories and gradient impulse response function (GIRF)\u2010based trajectory predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed workflow reduced model complexity and substantially improved prediction speed. In phantom and in vivo radial imaging, model\u2010based trajectories provided better image quality over a wider range of receiver bandwidths than GIRF\u2010based trajectories.  term estimation provided limited additional visual benefit after scanner preemphasis adjustment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>TCN\u2010based prediction of gradient\u2010waveform distortions can be made more efficient while remaining effective for radial MRI trajectory estimation on the studied preclinical system, particularly in demanding high\u2010bandwidth acquisitions.<\/jats:p>\n                  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