Accelerated mono‐ and biexponential 3D‐T1ρ relaxation mapping of knee cartilage using golden angle radial acquisitions and compressed sensing. Issue 4 (18th October 2019)

• Record Type: Journal Article
• Authors: Zibetti, Marcelo V. W.; Sharafi, Azadeh; Otazo, Ricardo; Regatte, Ravinder R.
• Is Part Of: Magnetic resonance in medicine. Volume 83:Issue 4(2020)
• Page Start: 1291
• Page End: 1309
• Publication Date: 2019-10-18
• Subjects: compressed sensing -- low rank -- radial -- sparse reconstruction -- T1ρ relaxation; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28019
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To use golden‐angle radial sampling and compressed sensing (CS) for accelerating mono‐ and biexponential 3D spin‐lattice relaxation time in the rotating frame (T1ρ ) mapping of knee cartilage. Methods: Golden‐angle radial stack‐of‐stars and Cartesian 3D‐T1ρ ‐weighted knee cartilage datasets ( n = 12) were retrospectively undersampled by acceleration factors (AFs) 2‐10. CS‐based reconstruction using 8 different sparsifying transforms were compared for mono‐ and biexponential T1ρ ‐mapping of knee cartilage, including spatio‐temporal 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‐valued fitting was used and Marchenko‐Pastur principal component analysis filtering also tested. Results: 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 T1ρ mapping, with median normalized absolute deviation below 10% up to AF of 6. Conclusion: Golden‐angle radial acquisitions combined with CS outperformed Cartesian acquisitions for 3D‐T1ρ 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‐temporal finite differences, and low rank obtained the best results for radial sampling patterns.