Dataset Information

Accelerating 3D-T1? mapping of cartilage using compressed sensing with different sparse and low rank models.

ABSTRACT: PURPOSE:To evaluate the feasibility of using compressed sensing (CS) to accelerate 3D-T1? mapping of cartilage and to reduce total scan times without degrading the estimation of T1? relaxation times. METHODS:Fully sampled 3D-T1? datasets were retrospectively undersampled by factors 2-10. 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-means singular value decomposition (K-SVD), explicit exponential models, low rank and low rank plus sparse models. Spatial filtering prior to T1? parameter estimation was also tested. Synthetic phantom (n?=?6) and in vivo human knee cartilage datasets (n?=?7) were included. RESULTS:Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative T1? 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 T1? error below 6.5% on T1? relaxation times with AF up to 10, when spatial filtering was used before T1? fitting, at the expense of smoothing the T1? maps. The STFD achieved 5.1% error at AF?=?10 with spatial filtering prior to T1? fitting. CONCLUSION:Accelerating 3D-T1? 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 T1? error of 6.5%.

SUBMITTER: Zibetti MVW

PROVIDER: S-EPMC6097944 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Accelerating 3D-T<sub>1ρ</sub> mapping of cartilage using compressed sensing with different sparse and low rank models.

Zibetti Marcelo V W MVW Sharafi Azadeh A Otazo Ricardo R Regatte Ravinder R RR

Magnetic resonance in medicine 20180225 4

<h4>Purpose</h4>To evaluate the feasibility of using compressed sensing (CS) to accelerate 3D-T<sub>1ρ</sub> mapping of cartilage and to reduce total scan times without degrading the estimation of T<sub>1ρ</sub> relaxation times.<h4>Methods</h4>Fully sampled 3D-T<sub>1ρ</sub> datasets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared, including finite differences, temporal and spatial wavelets, learned transforms using pr ...[more]

PMID: 29479738

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Project description:Background3D-T1ρ mapping is useful to quantify various neurologic disorders, but data are currently time-consuming to acquire.PurposeTo compare the performance of five compressed sensing (CS) algorithms-spatiotemporal finite differences (STFD), exponential dictionary (EXP), 3D-wavelet transform (WAV), low-rank (LOW) and low-rank plus sparse model with spatial finite differences (L + S SFD)-for 3D-T1ρ mapping of the human brain with acceleration factors (AFs) of 2, 5, and 10.Study typeRetrospective.SubjectsEight healthy volunteers underwent T1ρ imaging of the whole brain.Field strength/sequenceThe sequence was fully sampled 3D Cartesian ultrafast gradient echo sequence with a customized T1ρ preparation module on a clinical 3T scanner.AssessmentThe fully sampled data was undersampled by factors of 2, 5, and 10 and reconstructed with the five CS algorithms. Image reconstruction quality was evaluated and compared to the SENSE reconstruction of the fully sampled data (reference) and T1ρ estimation errors were assessed as a function of AF.Statistical testsNormalized root mean squared errors (nRMSE) and median normalized absolute deviation (MNAD) errors were calculated to compare image reconstruction errors and T1ρ estimation errors, respectively. Linear regression plots, Bland-Altman plots, and Pearson correlation coefficients (CC) are shown.ResultsFor image reconstruction quality, at AF = 2, EXP transforms had the lowest mRMSE (1.56%). At higher AF values, STFD performed better, with the smallest errors (3.16% at AF = 5, 4.32% at AF = 10). For whole-brain quantitative T1ρ mapping, at AF = 2, EXP performed best (MNAD error = 1.62%). At higher AF values (AF = 5, 10), the STFD technique had the least errors (2.96% at AF = 5, 4.24% at AF = 10) and the smallest variance from the reference T1ρ estimates.Data conclusionThis study demonstrates the use of different CS algorithms that may be useful in reducing the scan time required to perform volumetric T1ρ mapping of the brain.Level of evidence2.Technical efficacy stage1.

Dataset Information

Accelerating 3D-T1? mapping of cartilage using compressed sensing with different sparse and low rank models.

Publications

Accelerating 3D-T<sub>1ρ</sub> mapping of cartilage using compressed sensing with different sparse and low rank models.

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