Rapid compositional mapping of knee cartilage with compressed sensing MRI

Zibetti, Marcelo V. W.; Baboli, Rahman; Chang, Gregory; Otazo, Ricardo; Regatte, Ravinder R.

doi:10.1002/jmri.26274

Cited by 29 publications

(35 citation statements)

References 111 publications

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“…Several studies have demonstrated that compressed sensing (CS)‐based methods can accelerate T 1 ρ mapping . CS is particularly suitable for T 1 ρ mapping because of the high image compressibility along the parametric direction.…”

Section: Introductionmentioning

confidence: 99%

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Zhu

Liu

Ying

et al. 2019

Magnetic Resonance in Med

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Purpose To develop and evaluate a fast imaging method based on signal‐compensated low‐rank plus sparse matrix decomposition to accelerate data acquisition for biexponential brain T1ρ mapping (Bio‐SCOPE). Methods Two novel strategies were proposed to improve reconstruction performance. A variable‐rate undersampling scheme was used with a varied acceleration factor for each k‐space along the spin‐lock time direction, and a modified nonlinear thresholding scheme combined with a feature descriptor was used for Bio‐SCOPE reconstruction. In vivo brain T1ρ mappings were acquired from 4 volunteers. The fully sampled k‐space data acquired from 3 volunteers were retrospectively undersampled by net acceleration rates (R) of 4.6 and 6.1. Reference values were obtained from the fully sampled data. The agreement between the accelerated T1ρ measurements and reference values was assessed with Bland‐Altman analyses. Prospectively undersampled data with R = 4.6 and R = 6.1 were acquired from 1 volunteer. Results T1ρ‐weighted images were successfully reconstructed using Bio‐SCOPE for R = 4.6 and 6.1 with signal‐to‐noise ratio variations <1 dB and normalized root mean square errors <4%. Accelerated and reference T1ρ measurements were in good agreement for R = 4.6 (T1ρs: 18.6651 ± 1.7786 ms; T1ρl: 88.9603 ± 1.7331 ms) and R = 6.1 (T1ρs: 17.8403 ± 3.3302 ms; T1ρl: 88.0275 ± 4.9606 ms) in the Bland‐Altman analyses. T1ρ parameter maps from prospectively undersampled data also show reasonable image quality using the Bio‐SCOPE method. Conclusion Bio‐SCOPE achieves a high net acceleration rate for biexponential T1ρ mapping and improves reconstruction quality by using a variable‐rate undersampling data acquisition scheme and a modified soft‐thresholding algorithm in image reconstruction.

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Section: Introductionmentioning

confidence: 99%

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Zhu

Liu

Ying

et al. 2019

Magnetic Resonance in Med

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“…Unlike standard NUFFT‐based magnitude noise profiles which follow a well‐known Rayleigh distribution, the statistical characteristics of noise from iterative reconstruction are still not fully understood. The effects on statistical and spatial noise distribution may however bias the estimation of other parameters such as

T_{2}

…”

Section: Discussionmentioning

confidence: 99%

Compressed sensing effects on quantitative analysis of undersampled human brain sodium MRI

Blunck

Kolbe

Moffat

et al. 2019

Magnetic Resonance in Med

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Purpose The clinical application of sodium MRI is hampered due to relatively low image quality and associated long acquisition times. Compressed sensing (CS) aims at a reduction of measurement time, but has been found to encompass quantitative estimation bias when used in low SNR x‐Nuclei imaging. This work analyses CS in quantitative human brain sodium MRI from undersampled acquisitions and provides recommendations for tissue sodium concentration (TSC) estimation. Methods CS reconstructions from 3D radial acquisitions of 5 healthy volunteers were investigated over varying undersampling factors (USFs) and CS penalty weights on different sparsity domains, Wavelet, Discrete Cosine Transform (DCT), and Identity. Resulting images were compared with highly sampled and undersampled NUFFT‐based images and evaluated for image quality (i.e. structural similarity), image intensity bias, and its effect on TSC estimates in gray and white matter. Results Wavelet‐based CS reconstructions show highest image quality with stable TSC estimates for most USFs. Up to an USF of 4, images showed good structural detail. DCT and Identity‐based CS enable good image quality, however show a bias in TSC with a reduction in estimates across USFs. Conclusions The image intensity bias is lowest in Wavelet‐based reconstructions and enables an up to fourfold acquisition speed up while maintaining good structural detail. The associated acquisition time reduction can facilitate a translation of sodium MRI into clinical routine.

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“… λ is the regularization parameter, and T is the sparsifying transform. Here the sparsifying transform used was spatial temporal finite differences (STFD) with the temporal order set to 1 and the spatial order set to 1 . The value of λ was determined by running a series of test values on a log scale for one dataset and using that value for subsequent reconstructions .…”

Section: Methodsmentioning

confidence: 99%

“…The value of λ was determined by running a series of test values on a log scale for one dataset and using that value for subsequent reconstructions . In this version of GRASP, fast iterative shrinkage thresholding algorithm with fast gradient projection (FISTA‐FGP) was used for minimization of the cost function …”

Section: Methodsmentioning

confidence: 99%

In vivo tibiofemoral cartilage strain mapping under static mechanical loading using continuous GRASP‐MRI

Menon

Zibetti

Regatte

2019

Magnetic Resonance Imaging

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Background Quantification of dynamic biomechanical strain in articular cartilage in vivo; in situ using noninvasive MRI techniques is desirable and may potentially be used to assess joint pathology. Purpose To demonstrate the use of static mechanical loading and continuous 3D‐MRI acquisition of the human knee joint in vivo to measure the strain in the tibiofemoral articular cartilage. Study Type Prospective. Subjects Five healthy human volunteers (four women, one man; age 25.6 ± 1.7) underwent MRI at rest, under static mechanical loading condition, and during recovery. Field Strength/Sequence A field strength of 3T was used. The sequence used was 3D‐continuous golden angle radial sparse parallel (GRASP) MRI and compressed sensing (CS) reconstruction. Assessment Tibiofemoral cartilage deformation maps under loading and during recovery were calculated using an optical flow algorithm. The corresponding Lagrangian strain was calculated in the articular cartilage. Statistical Tests Range of displacement and strain in each subject, and the resulting mean and standard deviation, were calculated. Results During the loading condition, the cartilage displacement in the direction of loading ranged from a minimum of –673.6 ± 121.9 μm to a maximum of 726.5 ± 169.5 μm. Corresponding strain ranged from a minimum of –7.0 ± 4.2% to a maximum of 5.4 ± 1.6%. During the recovery condition, the cartilage displacement in the same direction reduced to a minimum of –613.0 ± 129.5 μm and a maximum of 555.7 ± 311.4 μm. The corresponding strain range reduced to a minimum of –1.6 ± 7.5% to a maximum of 4.2 ± 2.6%. Data Conclusion This study shows the feasibility of using static mechanical loading with continuous GRASP‐MRI acquisition to measure the strain in the articular cartilage. By measuring strain during the loading and recovery phases, dynamic strain information in the articular cartilage might be able to be investigated. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:426–434.

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Rapid compositional mapping of knee cartilage with compressed sensing MRI

Cited by 29 publications

References 111 publications

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Compressed sensing effects on quantitative analysis of undersampled human brain sodium MRI

In vivo tibiofemoral cartilage strain mapping under static mechanical loading using continuous GRASP‐MRI

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Rapid compositional mapping of knee cartilage with compressed sensing MRI

Cited by 29 publications

References 111 publications

Bio‐SCOPE: fast biexponential T1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Bio‐SCOPE: fast biexponential T1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Compressed sensing effects on quantitative analysis of undersampled human brain sodium MRI

In vivo tibiofemoral cartilage strain mapping under static mechanical loading using continuous GRASP‐MRI

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Product

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Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition