Diffusion tensor imaging of native and degenerated human articular cartilage

Deng, Xiang; Farley, Michelle; Nieminen, Miika T.; Gray, Martha L.; Burstein, Deborah

doi:10.1016/j.mri.2006.10.015

Cited by 84 publications

(90 citation statements)

References 15 publications

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“…Nevertheless, we have discarded any possible directional information inherent to the three DW scans with orthogonal dephasing moments (see Fig. 7), since low fractional anisotropy is expected for cartilage (27) and characterization of fractional anisotropy effects is beyond the scope of this work. In contrast, we have averaged the scans prior to diffusion calculation not only to increase SNR but also to reduce residual motion-related image artifacts (5).…”

Section: Discussionmentioning

confidence: 99%

Fast diffusion‐weighted steady state free precession imaging of in vivo knee cartilage

Bieri

Ganter

Welsch

et al. 2011

Magnetic Resonance in Med

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Quantification of molecular diffusion with steady state free precession (SSFP) is complicated by the fact that diffusion effects accumulate over several repetition times (TR) leading to complex signal dependencies on transverse and longitudinal magnetization paths. This issue is commonly addressed by setting TR > T 2 , yielding strong attenuation of all higher modes, except of the shortest ones. As a result, signal attenuation from diffusion becomes T 2 independent but signal-to-noise ratio (SNR) and sequence efficiency are remarkably poor. In this work, we present a new approach for fast in vivo steady state free precession diffusion-weighted imaging of cartilage with TR << T 2 offering a considerable increase in signal-to-noise ratio and sequence efficiency. At a first glance, prominent coupling between magnetization paths seems to complicate quantification issues in this limit, however, it is observed that diffusion effects become rather T 2 (DD 1/10 DT 2 ) but not T 1 independent (DD~1/2 DT 1 ) for low flip angles a~10 2 15°. As a result, fast high-resolution (0. The dynamic displacement of water on cellular dimensions provides a unique insight into tissue structure, microstructure, and organization. As a result, diffusionweighted imaging (DWI) has become a highly sensitive and specific tool for the detection of pathological changes in tissue. While echo planar imaging (1) is the current gold standard for DWI of the brain (2), its rather poor achievable spatial resolution and coverage for tissues with short transverse relaxation times (T 2 ) has pushed the development of several other DWI techniques, some of them using the diffusion sensitivity of steady state free precession (SSFP) to a single, unbalanced gradient (see Fig. 1a; Refs. 3-7). Due to short repetition times (TR < T 2 ), as commonly used with SSFP, the steady state signal is composed of many different transverse and longitudinal paths or modes including stimulated echoes. As a result, especially the ''echo'' in nonbalanced SSFP (i.e., the refocused signal immediately preceding the radiofrequency pulse) is very sensitive to diffusion and represents a unique alternative to traditional echo planar imaging-based DWI. The FID (i.e., the signal immediately following the radiofrequency pulse) is generally not used, since its sensitivity to diffusion in tissues is quite low.Several models have been developed for the description of diffusion effects in SSFP (4,8-10), most of them being based on the seminal work of Kaiser, Bartholdi, and Ernst (KBE) (8). In MRI, besides semiempirical approaches, such as the one presented by Le Bihan (4), the extension of the KBE ansatz to pulsed gradient SSFP by Wu and Buxton (9,10) is generally well accepted and several research groups have reported DWI with SSFPEcho (for complete review, see Ref. 7). Quantification of diffusion, however, is complicated by contributions of many different echoes leading to complicated dependencies on relaxation times (T 1,2 ) and sequence parameters (TR, flip angle). Nevertheless, quantifi...

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Section: Discussionmentioning

confidence: 99%

Fast diffusion‐weighted steady state free precession imaging of in vivo knee cartilage

Bieri

Ganter

Welsch

et al. 2011

Magnetic Resonance in Med

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“…Determining their relaxation constants from experimental data collected with spin-echo sequences is common in diffusion tensor imaging (DTI) and spectroscopy (DTS) [1][2][3], measurements of fractional anisotropy (FA) [4], transverse relaxation rates (R 2 ) [5][6][7], etc. An exponentially decaying signal can be described by two parameters: its amplitude ρ and decay rate λ, (1) where t is the user-controlled encoding parameter (diffusion weighting or echo time).…”

Section: Introductionmentioning

confidence: 99%

The optimal MR acquisition strategy for exponential decay constants estimation

Fleysher

Gonen

2008

Magnetic Resonance Imaging

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Estimating the relaxation constant of an exponentially decaying signal from experimental MR data is fundamental in diffusion tensor imaging, fractional anisotropy mapping, measurements of transverse relaxation rates and contrast agent uptake. The precision of such measurements depends on the choice of acquisition parameters made at the design stage of the experiments. In this report, χ 2 fitting of multi-point data is used to demonstrate that the most efficient acquisition strategy is a two-point scheme. We also conjecture that the smallest cofficient of variation of the decay constant achievable in any N-point experiment is 3.6 times larger than that in the image intensity obtained by averaging N acquisitions with minimal exponential weighting.

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“…The tension-compression response of collagen fibres is crucial to the overall mechanical response of articular cartilage. MRI alone can not yet be used to determine the functional state of articular cartilage [8,65,67,73,74]. There is hope that a multi-disciplinary combination of DT-MR Imaging and numerical simulation will bring this goal to fruition, as discussed in, e. g. [74].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

A Computational Framework for Patient‐Specific Analysis of Articular Cartilage Incorporating Structural Information from DT‐MRI

2009

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MSC (2000) 74L15, 74E10, 92C55, 92C10Accurate techniques for simulating soft biological tissue deformation are an increasingly valuable tool in many areas of biomechanical analysis and medical image computing. To model the morphology and the material response of human articular cartilage a phenomenological and patient-specific simulation approach incorporating the collagen fibre fabric is proposed. We then demonstrate a unique combination of ultra-high field Diffusion Tensor Magnetic Resonance Imaging (17.6T DT-MRI) and a novel numerical approach incorporating the empirical data to predict the collagen fibre fabric deformation for an indentation experiment.

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Diffusion tensor imaging of native and degenerated human articular cartilage

Cited by 84 publications

References 15 publications

Fast diffusion‐weighted steady state free precession imaging of in vivo knee cartilage

Fast diffusion‐weighted steady state free precession imaging of in vivo knee cartilage

The optimal MR acquisition strategy for exponential decay constants estimation

A Computational Framework for Patient‐Specific Analysis of Articular Cartilage Incorporating Structural Information from DT‐MRI

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