In vivo diffusion tensor imaging of the human calf muscle

Sinha, Shantanu; Sinha, Usha; Edgerton, V. Reggie

doi:10.1002/jmri.20593

Cited by 161 publications

(154 citation statements)

References 32 publications

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“…Also, several studies have reported an ordered distribution of v 2 and v 3 within individual muscles [86-88]. However, a definitive structural basis for λ 2 / v 2 and λ 3 / v 3 has yet to be concluded.…”

Section: Diffusion-tensor Mri Of Skeletal Musclementioning

confidence: 99%

“…When imaging the lower leg, we use an MRI-compatible foot restraint/force measurement system to provide consistent, motion-limited positioning and control plantarflexion/dorsiflexion angle [97]. To achieve a similar result, Sinha et al [86] and Froeling et al [98] have described scaffold systems for the leg and forearm, respectively.…”

Section: Diffusion-tensor Mri Of Skeletal Musclementioning

confidence: 99%

“…In their study of the mouse hindlimb, Heemskerk et al also calculated the PCSA as the dot product of a measured anatomical cross-sectional area and the fiber direction [106] (it should be noted that, like other in vivo imaging-based assessments, this calculation did not normalize fiber lengths based on sarcomere length). DT-MRI-based fiber tracking also has been performed in a variety of human muscles, including the plantarflexor [86, 101, 110, 113], tibialis anterior [97, 107, 109], forearm [100], thigh [114-116], and female pelvic floor [117] muscles. Fiber tracking has been used to study structural alterations to the genioglossus muscle due to an oral appliance [118] and to investigate the effects of chronic lateral patella dislocation, the latter study demonstrating larger lateral force vectors in patients than in controls [115].…”

Section: Diffusion-tensor Mri Of Skeletal Musclementioning

confidence: 99%

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Diffusion-tensor MRI-based skeletal muscle fiber tracking

Damon¹,

Buck²,

Ding³

2011

Imaging in Medicine

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A skeletal muscle's function is strongly influenced by the internal organization and geometric properties of its fibers, a property known as muscle architecture. Diffusion-tensor magnetic resonance imaging-based fiber tracking provides a powerful tool for non-invasive muscle architecture studies, has three-dimensional sensitivity, and uses a fixed frame of reference. Significant advances have been made in muscle fiber tracking technology, including defining seed points for fiber tracking, quantitatively characterizing muscle architecture, implementing denoising procedures, and testing validity and repeatability. Some examples exist of how these data can be integrated with those from other advanced MRI and computational methods to provide novel insights into muscle function. Perspectives are offered regarding future directions in muscle diffusion-tensor imaging, including needs to develop an improved understanding for the microstructural basis for reduced and anisotropic diffusion, establish the best practices for data acquisition and analysis, and integrate fiber tracking with other physiological data.

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Section: Diffusion-tensor Mri Of Skeletal Musclementioning

confidence: 99%

Section: Diffusion-tensor Mri Of Skeletal Musclementioning

confidence: 99%

Section: Diffusion-tensor Mri Of Skeletal Musclementioning

confidence: 99%

See 1 more Smart Citation

Diffusion-tensor MRI-based skeletal muscle fiber tracking

Damon¹,

Buck²,

Ding³

2011

Imaging in Medicine

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“…Studies of the spinal cord [15][16][17] and calf muscles [18] have shown promising results for DTI. Ries et al [15] applied DTI to subjects suffering from a narrowing of the cervical canal and observed substantial differences in diffusion characteristics to detect lesions in the spine.…”

Section: Motivationmentioning

confidence: 99%

“…Ducreux et al [17] reconstructed 3D fiber tracts to visualize the deformation of the posterior spinal cord lemniscal and corticospinal tracts. Finally, Sinha et al [18] performed calf muscle fiber tracking in vivo using DTI in human subjects.…”

Section: Motivationmentioning

confidence: 99%

Robust optimization of diffusion-weighted MRI protocols used for fiber reconstruction

Majumdar

Удпа

Raguin

2008

J. Phys.: Conf. Ser.

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Abstract. Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI)technique that employs diffusion-encoding gradients to sensitize the signal to the diffusion of water molecules. DWI allows the noninvasive and quantitative probing of opaque structures such as fibrous soft tissues. Model-based DWI post-processing algorithms, such as diffusion tensor imaging (DTI), solve an inverse problem to estimate from a series of DWI data a set of model parameters representing the diffusion process and the environment of the water molecules. DWI models connect the model parameters (e.g., fiber orientations for fibrous soft tissues) with the experimental parameters (e.g., strengths and directions of the 3-D diffusion-encoding gradients). For spinal cord injuries and skeletal muscle characterization, the fiber orientations within the imaged region can be approximately known a priori using localizer images. Then, we propose and implement a model-based robust optimization framework for two axisymmetric diffusion models, producing robust DWI protocols with respect to the approximate knowledge of the fiber orientations within the images, thereby reducing the uncertainty in the parameter estimates caused by experimental noise. Our goal is to improve the yield of quantitative DWI diagnostics used in clinical and preclinical trials by minimizing the experimental uncertainty.1. Introduction 1.1. Background Model-based diffusion-weighted imaging (DWI) methods, such as DTI [1,2] and QUAQ [3,4], are a subset of quantitative magnetic resonance imaging (MRI) techniques, which are used to infer important structural information within biological tissues. DWI is based on its sensitivity to the diffusive movement of water molecules [5], which in turn is representative of the molecular environment. The sensitization to diffusion is achieved by applying a pair of pulsed diffusionencoding gradients [5]. While for standard MRI the imaging is done by collecting points in k-space, the Fourier reciprocal space of spin locations, DWI protocols involve acquiring a series of DWI images that sample q-space, the Fourier reciprocal space of spin displacements, by using one different set of diffusion-encoding gradient pulses per image [2,3]. The 3-D vector q is related to the diffusion-encoding gradient vector (g) via q := γδg/2π, where γ is the gyromagnetic ratio (γ/2π = 42.576 MHz T −1 ) and δ is the gradient pulse duration. The b-factor is often used

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Non‐Destructive Determination of Muscle Architectural Variables Through the Use of DiceCT

Dickinson

Stark

Kupczik

2018

The Anatomical Record

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The fascicular architecture of skeletal muscle dictates functional parameters such as force production and contractile velocity. Muscle microarchitecture is typically determined by means of manual dissection, a technique that is inherently destructive to specimens. Furthermore, fascicle lengths and pennation angles are commonly assessed at only a limited number of sampling sites per muscle. We present the results of a digital technique to non-destructively assess muscle architectural variables for three jaw-adductor muscles within a specimen of the cercopithecine primate Macaca fascicularis (crab-eating macaque). The specimen is first subjected to a contrast-enhanced staining protocol to increase the density of internal soft tissues. High-resolution µCT scans are then collected and segmented to isolate individual muscles. A textural orientation algorithm is then applied to each muscle volume to reconstruct constituent muscle fascicles in three dimensions. Using this technique, we report muscle volume, fascicle length, angle of pennation, and physiological cross-sectional area (PCSA) for each muscle. These data are compared to results collected using traditional dissection of the contralateral muscles. Reconstructions of muscle volume and pennation angle closely correspond to the dissection results. The degree of similarity between measurements of fascicle length and PCSA varies between muscles, with temporalis demonstrating the greatest disparity between techniques; likely reflecting the complex geometry and fascicular arrangement of this muscle. The described technique samples a much larger number of fascicles than had previously been possible and non-destructively investigates the internal architecture of preserved specimens. We conclude that this approach demonstrates great potential for quantifying muscle internal architecture. Anat Rec, 301:363-377, 2018. © 2018 Wiley Periodicals, Inc.

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In vivo diffusion tensor imaging of the human calf muscle

Cited by 161 publications

References 32 publications

Diffusion-tensor MRI-based skeletal muscle fiber tracking

Diffusion-tensor MRI-based skeletal muscle fiber tracking

Robust optimization of diffusion-weighted MRI protocols used for fiber reconstruction

Non‐Destructive Determination of Muscle Architectural Variables Through the Use of DiceCT

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