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CS\u2010based reconstruction using 8 different sparsifying transforms were compared for mono\u2010 and biexponential T<jats:sub>1\u03c1<\/jats:sub>\u2010mapping of knee cartilage, including spatio\u2010temporal finite differences, wavelets, dictionary from principal component analysis, and exponential decay models, and also low rank and low rank plus sparse models (L+S). Complex\u2010valued fitting was used and Marchenko\u2010Pastur principal component analysis filtering also tested.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Most CS methods performed well for an AF of 2, with relative median normalized absolute deviation below 10% for monoexponential and biexponential mapping. For monoexponential mapping, radial sampling obtained a median normalized absolute deviation below 10% up to AF of 10, while Cartesian obtained this level of error only up to AF of 4. Radial sampling was also better with biexponential T<jats:sub>1\u03c1<\/jats:sub>mapping, with median normalized absolute deviation below 10% up to AF of 6.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Golden\u2010angle radial acquisitions combined with CS outperformed Cartesian acquisitions for 3D\u2010T<jats:sub>1\u03c1<\/jats:sub>mapping of knee cartilage, being it is a good alternative to Cartesian sampling for reducing scan time and\/or improving image and mapping quality. The methods exponential decay models, spatio\u2010temporal finite differences, and low rank obtained the best results for radial sampling patterns.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.28019","type":"journal-article","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T16:01:08Z","timestamp":1571414468000},"page":"1291-1309","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Accelerated mono\u2010 and biexponential 3D\u2010T1\u03c1 relaxation mapping of knee cartilage using golden angle radial acquisitions and compressed sensing"],"prefix":"10.1002","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2856-3625","authenticated-orcid":false,"given":"Marcelo V. 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