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CS reconstruction using 12 different sparsifying transforms were compared, including finite differences, temporal and spatial wavelets, learned transforms using principal component analysis (PCA) and K\u2010means singular value decomposition (K\u2010SVD), explicit exponential models, low rank and low rank plus sparse models. Spatial filtering prior to T<jats:sub>1\u03c1<\/jats:sub> parameter estimation was also tested. Synthetic phantom (<jats:italic>n<\/jats:italic>\u2009=\u20096) and in vivo human knee cartilage datasets (<jats:italic>n<\/jats:italic>\u2009=\u20097) were included.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative T<jats:sub>1\u03c1<\/jats:sub> error lower than 4.5%. Some sparsifying transforms, such as spatiotemporal finite difference (STFD), exponential dictionaries (EXP) and low rank combined with spatial finite difference (L+S SFD) significantly improved this performance, reaching average relative T<jats:sub>1\u03c1<\/jats:sub> error below 6.5% on T<jats:sub>1\u03c1<\/jats:sub> relaxation times with AF up to 10, when spatial filtering was used before T<jats:sub>1\u03c1<\/jats:sub> fitting, at the expense of smoothing the T<jats:sub>1\u03c1<\/jats:sub> maps. The STFD achieved 5.1% error at AF\u2009=\u200910 with spatial filtering prior to T<jats:sub>1\u03c1<\/jats:sub> fitting.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Accelerating 3D\u2010T<jats:sub>1\u03c1<\/jats:sub> mapping of cartilage with CS is feasible up to AF of 10 when using STFD, EXP or L+S SFD regularizers. These three best CS methods performed satisfactorily on synthetic phantom and in vivo knee cartilage for AFs up to 10, with T<jats:sub>1\u03c1<\/jats:sub> error of 6.5%.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.27138","type":"journal-article","created":{"date-parts":[[2018,2,26]],"date-time":"2018-02-26T06:01:26Z","timestamp":1519624886000},"page":"1475-1491","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Accelerating 3D\u2010T<sub>1\u03c1<\/sub> mapping of cartilage using compressed sensing with different sparse and low rank models"],"prefix":"10.1002","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2856-3625","authenticated-orcid":false,"given":"Marcelo V.W.","family":"Zibetti","sequence":"first","affiliation":[{"name":"Center for Biomedical Imaging, Department of Radiology New York University School of Medicine New York New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9572-4922","authenticated-orcid":false,"given":"Azadeh","family":"Sharafi","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging, Department of Radiology New York University School of Medicine New York New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo","family":"Otazo","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging, Department of Radiology New York University School of Medicine New York New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravinder R.","family":"Regatte","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging, Department of Radiology New York University School of Medicine New York New York"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,2,25]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0033-8389(03)00161-1"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.blo.0000144971.12731.a2"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rdc.2006.12.003"},{"key":"e_1_2_7_5_1","first-page":"S230","article-title":"The economic burden of osteoarthritis","volume":"15","author":"Bitton R","year":"2009","journal-title":"Am J Manag Care"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.1102"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1076-6332(03)80666-9"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1208"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1302\/2046-3758.57.BJR-2016-0057.R1"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1552-6569.2010.00467.x"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12410-013-9248-7"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.24677"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.20536"},{"key":"e_1_2_7_14_1","first-page":"565","volume-title":"MRI: Physics, Image Reconstruction, and Analysis","author":"Yuan J","year":"2015"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.10412"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21391"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914731"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22463"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24440"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24980"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.23128"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22483"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24577"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25130"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25773"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25702"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.3760"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1241"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24751"},{"key":"e_1_2_7_31_1","unstructured":"LustigM AlleyM VasanawalaS DonohoDL PaulyJM.L1 SPIR\u2010iT: autocalibrating parallel imaging compressed sensing. 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