{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:41:41Z","timestamp":1791650501824,"version":"4.3.4"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>To address head motion in brain MRI with a novel motion\u2010resolved imaging framework, with application to motion\u2010robust quantitative multiparametric mapping.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low\u2010rank tensor. By incorporating a motion\u2010state dimension into the parameter dimensions and introducing unsupervised motion\u2010state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion\u2010robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion\u2010resolved imaging for simultaneous T<jats:sub>1<\/jats:sub>\/T<jats:sub>2<\/jats:sub>\/T<jats:sub>1<\/jats:sub><jats:italic><jats:sub>\u03c1<\/jats:sub><\/jats:italic> mapping in the brain was investigated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion\u2010resolved approach produced T<jats:sub>1<\/jats:sub>\/T<jats:sub>2<\/jats:sub>\/T<jats:sub>1<\/jats:sub><jats:italic><jats:sub>\u03c1<\/jats:sub><\/jats:italic> maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T<jats:sub>1<\/jats:sub>\/T<jats:sub>2<\/jats:sub>\/T1<jats:italic><jats:sub>\u03c1<\/jats:sub><\/jats:italic> measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion\u2010resolved approach produced T<jats:sub>1<\/jats:sub>\/T<jats:sub>2<\/jats:sub>\/T<jats:sub>1<\/jats:sub><jats:italic><jats:sub>\u03c1<\/jats:sub><\/jats:italic> maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T<jats:sub>1<\/jats:sub>\/T<jats:sub>2<\/jats:sub>\/T<jats:sub>1<\/jats:sub><jats:italic><jats:sub>\u03c1<\/jats:sub><\/jats:italic> measurement distributions closest to the motion\u2010free reference, with minimal measurement bias and variance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Motion\u2010resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration\u2010based motion correction.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.28959","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T17:54:41Z","timestamp":1629136481000},"page":"102-119","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Motion\u2010robust quantitative multiparametric brain MRI with motion\u2010resolved MR 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