Global sensitivity analysis of skeletal muscle dMRI metrics: Effects of microstructural and pulse parameters

Naughton, Noel; Georgiadis, John G.

doi:10.1002/mrm.28014

Cited by 7 publications

(6 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…120 Other modeling approaches analogously vary diffusion time and other protocol parameters to extract microstructural features. [121][122][123][124][125] However, the time-dependence of the microvascular component in skeletal muscle has received far less attention to date. Since the advent of IVIM in MRI applications, 12 the importance of diffusion time and the relevant dynamical regimes have been well recognized.…”

Section: Diffusion Time Variationmentioning

confidence: 99%

Intravoxel Incoherent Motion Magnetic Resonance Imaging in Skeletal Muscle: Review and Future Directions

Englund

Reiter

Shahidi

et al. 2021

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Throughout the body, muscle structure and function can be interrogated using a variety of noninvasive magnetic resonance imaging (MRI) methods. Recently, intravoxel incoherent motion (IVIM) MRI has gained momentum as a method to evaluate components of blood flow and tissue diffusion simultaneously. Much of the prior research has focused on highly vascularized organs, including the brain, kidney, and liver. Unique aspects of skeletal muscle, including the relatively low perfusion at rest and large dynamic range of perfusion between resting and maximal hyperemic states, may influence the acquisition, postprocessing, and interpretation of IVIM data. Here, we introduce several of those unique features of skeletal muscle; review existing studies of IVIM in skeletal muscle at rest, in response to exercise, and in disease states; and consider possible confounds that should be addressed for musclespecific evaluations. Most studies used segmented nonlinear least squares fitting with a b-value threshold of 200 sec/mm 2 to obtain IVIM parameters of perfusion fraction (f), pseudo-diffusion coefficient (D*), and diffusion coefficient (D). In healthy individuals, across all muscles, the average AE standard deviation of D was 1.46 AE 0.30 Â 10 À3 mm 2 /sec, D* was 29.7 AE 38.1 Â 10 À3 mm 2 /sec, and f was 11.1 AE 6.7%. Comparisons of reported IVIM parameters in muscles of the back, thigh, and leg of healthy individuals showed no significant difference between anatomic locations. Throughout the body, exercise elicited a positive change of all IVIM parameters. Future directions including advanced postprocessing models and potential sequence modifications are discussed. Level of Evidence: 2 Technical Efficacy: Stage 2

show abstract

Section: Diffusion Time Variationmentioning

confidence: 99%

Intravoxel Incoherent Motion Magnetic Resonance Imaging in Skeletal Muscle: Review and Future Directions

Englund

Reiter

Shahidi

et al. 2021

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…Second, DTI metrics obtained with finite diffusion time in WM (Lee et al 2018) and skeletal muscle (Naughton and Georgiadis 2019a) depend on the fiber diameter. Both these effects can be addressed by extending the present model to 3D and then simulating the DTI signal, as in (Naughton and Georgiadis 2019b).…”

Section: Importance Of Model Parameters and Their Combinationmentioning

confidence: 99%

Sensitivity analysis of effective transverse shear viscoelastic and diffusional properties of myelinated white matter

Sullivan¹,

Wu²,

Gallo³

et al. 2021

Phys. Med. Biol.

Self Cite

View full text Add to dashboard Cite

Motivated by the need to interpret the results from a combined use of in vivo brain Magnetic Resonance Elastography (MRE) and Diffusion Tensor Imaging (DTI), we developed a computational framework to study the sensitivity of single-frequency MRE and DTI metrics to white matter microstructure and cell-level mechanical and diffusional properties. White matter was modeled as a triphasic unidirectional composite, consisting of parallel cylindrical inclusions (axons) surrounded by sheaths (myelin), and embedded in a matrix (glial cells plus extracellular matrix). Only 2D mechanics and diffusion in the transverse plane (perpendicular to the axon direction) was considered, and homogenized (effective) properties were derived for a periodic domain containing a single axon. The numerical solutions of the MRE problem were performed with ABAQUS and by employing a sophisticated boundary-conforming grid generation scheme. Based on the linear viscoelastic response to harmonic shear excitation and steady-state diffusion in the transverse plane, a systematic sensitivity analysis of MRE metrics (effective transverse shear storage and loss moduli) and DTI metric (effective radial diffusivity) was performed for a wide range of microstructural and intrinsic (phase-based) physical properties. The microstructural properties considered were fiber volume fraction, and the myelin sheath/axon diameter ratio. The MRE and DTI metrics are very sensitive to the fiber volume fraction, and the intrinsic viscoelastic moduli of the glial phase. The MRE metrics are nonlinear functions of the fiber volume fraction, but the effective diffusion coefficient varies linearly with it. Finally, the transverse metrics of both MRE and DTI are insensitive to the axon diameter in steady state. Our results are consistent with the limited anisotropic MRE and co-registered DTI measurements, mainly in the corpus callosum, available in the literature. We conclude that isotropic MRE and DTI constitutive models are good approximations for myelinated white matter in the transverse plane. The unidirectional composite model presented here is used for the first time to model harmonic shear stress under MRE-relevant frequency on the cell level. This model can be extended to 3D in order to inform the solution of the inverse problem in MRE, establish the biological basis of MRE metrics, and integrate MRE/DTI with other modalities towards increasing the specificity of neuroimaging.

show abstract

“…Several groups have used in silico simulation to study the effect of imaging parameters on the DT such as SNR, diffusion directions, diffusion time, and diffusion weighting, in order to inform guidelines for developing DTI protocols. [12][13][14][15][16][17][18][19] Other studies have investigated the influence and sensitivity of microstructural features of muscle such as fiber geometry, diffusivity, and permeability on the DT. [17][18][19][20][21] No studies have systematically evaluated the relationship between muscle microstructure, Δ, and the DT.…”

Section: Introductionmentioning

confidence: 99%

“…In silico modeling allows for the precise control over simulated fiber size, fiber geometry, and Δ in order to calculate the resulting DT. Several groups have used in silico simulation to study the effect of imaging parameters on the DT such as SNR, diffusion directions, diffusion time, and diffusion weighting, in order to inform guidelines for developing DTI protocols 12‐19 . Other studies have investigated the influence and sensitivity of microstructural features of muscle such as fiber geometry, diffusivity, and permeability on the DT 17‐21 .…”

Section: Introductionmentioning

confidence: 99%

“…[12][13][14][15][16][17][18][19] Other studies have investigated the influence and sensitivity of microstructural features of muscle such as fiber geometry, diffusivity, and permeability on the DT. [17][18][19][20][21] No studies have systematically evaluated the relationship between muscle microstructure, Δ, and the DT. Furthermore, most modeling studies do not experimentally validate their results with acquired data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Varying diffusion time to discriminate between simulated skeletal muscle injury models using stimulated echo diffusion tensor imaging

Berry

Englund

Galinsky

et al. 2020

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose Evaluate the relationship between muscle microstructure, diffusion time (Δ), and the diffusion tensor (DT) to identify the optimal Δ where changes in muscle fiber size may be detected. Methods The DT was simulated in models with histology informed geometry over a range of Δ with a stimulated echo DT imaging (DTI) sequence using the numerical simulation application DifSim. The difference in the DT at each Δ between healthy and injured skeletal muscle models was calculated, to identify the optimal Δ at which changes in muscle fiber size may be detected. The random permeable barrier model (RPBM) was used to estimate muscle microstructure from the simulated DT measurements, which were compared to the ground truth. Results Across all models, fractional anisotropy provided greater contrast between injured and control models than diffusivity measurements. Compared to control models, in atrophic injury models, the greatest difference in the DT was found between 90 ms and 250 ms. In models with acute edema, the contrast between injured and control muscle increased with increasing diffusion time, although these models had smaller mean fiber areas. RPBM systematically underestimated fiber size but accurately estimated surface area‐to‐volume ratio of simulated models. Conclusion These findings may better inform pulse sequence parameter selection when performing DTI experiments in vivo. If only a single diffusion experiment can be performed, the selected Δ should be ~170 ms to maximize the ability to discriminate between different injury models. Ideally several diffusion times between 90 ms and 500 ms should be sampled in order to maximize diffusion contrast, particularly when the disease process is unknown.

show abstract

Global sensitivity analysis of skeletal muscle dMRI metrics: Effects of microstructural and pulse parameters

Cited by 7 publications

References 67 publications

Intravoxel Incoherent Motion Magnetic Resonance Imaging in Skeletal Muscle: Review and Future Directions

Intravoxel Incoherent Motion Magnetic Resonance Imaging in Skeletal Muscle: Review and Future Directions

Sensitivity analysis of effective transverse shear viscoelastic and diffusional properties of myelinated white matter

Varying diffusion time to discriminate between simulated skeletal muscle injury models using stimulated echo diffusion tensor imaging

Contact Info

Product

Resources

About