Accelerating 3D‐T 1ρ mapping of cartilage using compressed sensing with different sparse and low rank models

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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.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Menon

Zibetti

Regatte

2019

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“…Compressed sensing reconstruction is an inverse problem that aims to recover an image that is sparse in some transformed domain using undersampled data only. The reconstruction problem can be formulated as:

\hat{bold-italicx} = {argmin}_{bold-italicx} ‖ y - E x ‖_{bold2}^{bold2} + λ R (bold-italicx),

where x is a vector that represents the reconstructed set of relaxation‐weighted images, with its original size of

N_{y} \times N_{z} \times p

, y is a vector that represents the captured k ‐space data for all relaxation‐weighted images, its original size is k y × k z × c × p , where c is the number of receive coils, and the matrix E represents the encoding matrix mapping x to y , containing coil sensitivities (when parallel imaging and CS is jointly used, Fourier transforms, and sampling pattern . Many CS methods for knee cartilage use the joint parallel imaging and CS approach to achieve higher undersampling rates, as shown previously .…”

Section: Review Of Cs Mri For Compositional Mappingmentioning

confidence: 99%

“…The reconstruction problem can be formulated as:

\hat{bold-italicx} = {argmin}_{bold-italicx} ‖ y - E x ‖_{bold2}^{bold2} + λ R (bold-italicx),

where x is a vector that represents the reconstructed set of relaxation‐weighted images, with its original size of

N_{y} \times N_{z} \times p

{‖ bold-italice ‖}_{2}^{2} = \sum_{i = 1}^{N} {| e_{i} |}^{2}

, is quite common either because it is related to the usual assumption of Gaussian noise, or because it leads to a more tractable mathematical problem.…”

Section: Review Of Cs Mri For Compositional Mappingmentioning

confidence: 99%

“…In order to exploit sparsity, the undersampling process is designed in such a way that the introduced aliasing artifacts look like random noise, which can be effectively removed with a nonlinear operation that enforces, for example, sparsity on a vector, low rankness on a matrix, and combinations of sparse and low‐rank models . The AF using CS for knee cartilage varies in the literature; however, it can reach between an 8‐ to 10‐fold when combined with parallel imaging, taking around 3 minutes for data acquisition.…”

mentioning

confidence: 99%

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Rapid compositional mapping of knee cartilage with compressed sensing MRI

Zibetti

Baboli

Chang

et al. 2018

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More than one decade after the introduction of compressed sensing (CS) in MRI, researchers are still working on ways to translate it into different research and clinical applications. The greatest advantage of CS in MRI is the reduced amount of k-space data needed to reconstruct images, which can be exploited to reduce scan time or to improve spatial resolution and volumetric coverage. Efficient data acquisition using CS is extremely important for compositional mapping of the musculoskeletal system in general and knee cartilage mapping techniques in particular. High-resolution quantitative information about tissue biochemical composition could be obtained in just a few minutes using CS MRI. However, in order to make this goal a reality, some issues still need to be addressed. In this paper, we review the current state of the art of CS methods for rapid compositional mapping of knee cartilage. Specifically, data acquisition strategies, image reconstruction algorithms, and data fitting models are discussed. Different CS studies for T2 and T1ρ mapping of knee cartilage are reviewed, with illustrative results. Future directions, opportunities, and challenges of rapid compositional mapping techniques are also discussed.

T_1ρ mapping for assessment of renal allograft fibrosis

Hectors

Bane

Kennedy

et al. 2019

Background There is an unmet need for noninvasive methods to diagnose and stage renal allograft fibrosis. Purpose To investigate the utility of T1ρ measured with MRI for the assessment of fibrosis in renal allografts. Study Type Institutional Review Board (IRB)‐approved prospective. Subjects Fifteen patients with stable functional allograft (M/F 9/6, mean age 56 years) and 12 patients with allograft dysfunction and established fibrosis (M/F 6/6, mean age 51 years). Field Strength/Sequence T1ρ imaging at 1.5T using a custom‐developed sequence. Assessment Average T1ρ in the cortex and medulla was quantified and T1ρ repeatability (expressed by the coefficient of variation [CV]) was measured in four patients. Statistical Tests Differences in T1ρ values between the 2 groups were assessed using Mann–Whitney U‐tests. Diagnostic performance of T1ρ for differentiation between functional and fibrotic allografts was evaluated using receiver operating characteristic (ROC) analysis. Spearman correlations of T1ρ with Masson's trichrome‐stained fractions and serum estimated glomerular filtration rate (eGFR) were assessed. Results Higher T1ρ repeatability was found for cortex compared with medulla (mean CV T1ρ cortex 7.4%, medulla 13.3%). T1ρ values were significantly higher in the cortex of fibrotic vs. functional allografts (111.8 ± 17.2 msec vs. 99.0 ± 11.0 msec, P = 0.027), while there was no difference in medullary T1ρ values (122.6 ± 20.8 msec vs. 124.3 ± 20.8 msec, P = 0.789). Cortical T1ρ significantly correlated with Masson's trichrome‐stained fractions (r = 0.515, P = 0.044) and eGFR (r = –0.546, P = 0.004), and demonstrated an area under the curve (AUC) of 0.77 for differentiating between functional and fibrotic allografts (sensitivity and specificity of 75.0% and 86.7%, using threshold of 106.9 msec). Data Conclusion Our preliminary results suggest that T1ρ is a potential imaging biomarker of renal allograft fibrosis. These results should be verified in a larger study. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1085–1091.