Characterization of Age‐Related and Sex‐Related Differences of Relaxation Parameters in the Intervertebral Disc Using <scp>MR</scp>‐Fingerprinting

Menon, Rajiv G.; Monga, Anmol; Kijowski, Richard; Regatte, Ravinder R.

doi:10.1002/jmri.28925

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…One limitation of the study was that the analysis of the possible influence of sex was limited because of the small sample size with only a single male specimen (Table 1). However, a simple comparison of prediction errors and optimized stiffnesses did not suggest sex influenced the results, current literature is inconclusive on the relation between sex and the intervertebral disc properties (Kurutz, 2006;Mohan and Huynh, 2019;Menon et al, 2024). Furthermore, the small sample size limits the possible insights into the distribution of the IVJ stiffness properties within the population.…”

Section: Limitationsmentioning

confidence: 86%

Variability of intervertebral joint stiffness between specimens and spine levels

Gould,

Davico,

Liebsch

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Musculoskeletal multibody models of the spine can be used to investigate the biomechanical behaviour of the spine. In this context, a correct characterisation of the passive mechanical properties of the intervertebral joint is crucial. The intervertebral joint stiffness, in particular, is typically derived from the literature, and the differences between individuals and spine levels are often disregarded.Methods: This study tested if an optimisation method of personalising the intervertebral joint stiffnesses was able to capture expected stiffness variation between specimens and between spine levels and if the variation between spine levels could be accurately captured using a generic scaling ratio. Multibody models of six T12 to sacrum spine specimens were created from computed tomography data. For each specimen, two models were created: one with uniform stiffnesses across spine levels, and one accounting for level dependency. Three loading conditions were simulated. The initial stiffness values were optimised to minimize the kinematic error.Results: There was a range of optimised stiffnesses across the specimens and the models with level dependent stiffnesses were less accurate than the models without. Using an optimised stiffness substantially reduced prediction errors.Discussion: The optimisation captured the expected variation between specimens, and the prediction errors demonstrated the importance of accounting for level dependency. The inaccuracy of the predicted kinematics for the level-dependent models indicated that a generic scaling ratio is not a suitable method to account for the level dependency. The variation in the optimised stiffnesses for the different loading conditions indicates personalised stiffnesses should also be considered load-specific.

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Section: Limitationsmentioning

confidence: 86%

Variability of intervertebral joint stiffness between specimens and spine levels

Gould,

Davico,

Liebsch

et al. 2024

Front. Bioeng. Biotechnol.

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“…In [ 90 ], an MRF acquisition method was proposed that measures FF maps, water

, and fat

. MRF acquisition framework similar to [ 86 ] was used to estimate

, and T 1ρ for lower legs [ 88 ], and hips [ 91 , 92 ] and inter-vertebral discs [ 108 ].…”

Section: Emerging Trends In Mrf Applicationsmentioning

confidence: 99%

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Monga,

Singh,

de Moura

et al. 2024

Bioengineering

Self Cite

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Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

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Editorial for “Characterization of Age‐Related and Sex‐Related Differences of Relaxation Parameters in the Intervertebral Disc Using MR‐Fingerprinting”

Wang

2023

Magnetic Resonance Imaging

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C urrently, quantitative magnetic resonance imaging (MRI) has been widely used to provide functional information about the human body by assessing tissue composition, microstructure, and metabolites. Magnetic resonance fingerprinting (MRF) is a novel framework that can simultaneously generate multiple quantitative property maps, including T1, T2, and T1ρ. The MRF acquisition process involves three steps: signal acquisition, dictionary generation, and pattern matching. During signal acquisition, scan parameters are varied to generate a unique signal evolution over time, forming a signal "fingerprint" that is matched to a known dictionary to generate tissue property maps. 1 There has been a growing interest in evaluating the application of MRF in musculoskeletal system. Cloos et al developed an MRF approach called Plug-and-Play MRF, which eliminates artifacts induced by inhomogeneous B1+ field and enables accurate quantitative PD, T1, and T2 maps near metal implants. 2 Sharafi et al found that MRF sequences can simultaneously measure the T1, T2, T1ρ, and B1+ maps of human articular cartilage and detect cartilage degradation in patients with mild osteoarthritis. 3 In this study, a 3D-MRF sequence was shown to be capable of simultaneously acquiring proton density, T1, T2, and T1ρ maps of intervertebral disc (IVD) in healthy subjects. 4 The application of MRF has also been validated in other areas such as the brain, heart, breast, prostate, and abdomen. However, there are still limitations to its widespread application, including pulse sequence optimization, computational power, and metrics for error analysis. 1 Aging is a significant risk factor for IVD degeneration. This study demonstrated a significant negative correlation between age and T1, T2, and T1ρ values of lumbar IVDs. 4 The results are similar to previous studies. Wang et al enrolled both asymptomatic subjects and patients with low back pain and found T2 and T1ρ values of lumbar IVDs were significantly negatively correlated with age. 5 Menezes-

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Characterization of Age‐Related and Sex‐Related Differences of Relaxation Parameters in the Intervertebral Disc Using MR‐Fingerprinting

Cited by 4 publications

References 29 publications

Variability of intervertebral joint stiffness between specimens and spine levels

Variability of intervertebral joint stiffness between specimens and spine levels

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Editorial for “Characterization of Age‐Related and Sex‐Related Differences of Relaxation Parameters in the Intervertebral Disc Using MR‐Fingerprinting”

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