David A Reiter1,2, Fatemeh Adelnia3, Donnie Cameron4,5, Richard G Spencer6, Luigi Ferrucci6. 1. Department of Radiology & Imaging Sciences, Emory University, Atlanta, Georgia, USA. 2. Department of Orthopedics, Emory University, Atlanta, Georgia, USA. 3. Vanderbilt University Institute of Imaging Sciences, Vanderbilt University, Medical center, Nashville, Tennessee, USA. 4. Norwich Medical School, University of East Anglia, Norwich, United Kingdom. 5. C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden Medical Center, Leiden, the Netherlands. 6. National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA.
Abstract
PURPOSE: To develop an anomalous (non-Gaussian) diffusion model for characterizing skeletal muscle perfusion using multi-b-value DWI. THEORY AND METHODS: Fick's first law was extended for describing tissue perfusion as anomalous superdiffusion, which is non-Gaussian diffusion exhibiting greater particle spread than that of the Gaussian case. This was accomplished using a space-fractional derivative that gives rise to a power-law relationship between mean squared displacement and time, and produces a stretched exponential signal decay as a function of b-value. Numerical simulations were used to estimate parameter errors under in vivo conditions, and examine the effect of limited SNR and residual fat signal. Stretched exponential DWI parameters, α and D , were measured in thigh muscles of 4 healthy volunteers at rest and following in-magnet exercise. These parameters were related to a stable distribution of jump-length probabilities and used to estimate microvascular volume fractions. RESULTS: Numerical simulations showed low dispersion in parameter estimates within 1.5% and 1%, and bias errors within 3% and 10%, for α and D , respectively. Superdiffusion was observed in resting muscle, and to a greater degree following exercise. Resting microvascular volume fraction was between 0.0067 and 0.0139 and increased between 2.2-fold and 4.7-fold following exercise. CONCLUSIONS: This model captures superdiffusive molecular motions consistent with perfusion, using a parsimonious representation of the DWI signal, providing approximations of microvascular volume fraction comparable with histological estimates. This signal model demonstrates low parameter-estimation errors, and therefore holds potential for a wide range of applications in skeletal muscle and elsewhere in the body.
PURPOSE: To develop an anomalous (non-Gaussian) diffusion model for characterizing skeletal muscle perfusion using multi-b-value DWI. THEORY AND METHODS: Fick's first law was extended for describing tissue perfusion as anomalous superdiffusion, which is non-Gaussian diffusion exhibiting greater particle spread than that of the Gaussian case. This was accomplished using a space-fractional derivative that gives rise to a power-law relationship between mean squared displacement and time, and produces a stretched exponential signal decay as a function of b-value. Numerical simulations were used to estimate parameter errors under in vivo conditions, and examine the effect of limited SNR and residual fat signal. Stretched exponential DWI parameters, α and D , were measured in thigh muscles of 4 healthy volunteers at rest and following in-magnet exercise. These parameters were related to a stable distribution of jump-length probabilities and used to estimate microvascular volume fractions. RESULTS: Numerical simulations showed low dispersion in parameter estimates within 1.5% and 1%, and bias errors within 3% and 10%, for α and D , respectively. Superdiffusion was observed in resting muscle, and to a greater degree following exercise. Resting microvascular volume fraction was between 0.0067 and 0.0139 and increased between 2.2-fold and 4.7-fold following exercise. CONCLUSIONS: This model captures superdiffusive molecular motions consistent with perfusion, using a parsimonious representation of the DWI signal, providing approximations of microvascular volume fraction comparable with histological estimates. This signal model demonstrates low parameter-estimation errors, and therefore holds potential for a wide range of applications in skeletal muscle and elsewhere in the body.
Authors: Lukas Filli; Andreas Boss; Moritz C Wurnig; David Kenkel; Gustav Andreisek; Roman Guggenberger Journal: NMR Biomed Date: 2014-12-17 Impact factor: 4.044
Authors: Fatemeh Adelnia; Michelle Shardell; Christopher M Bergeron; Kenneth W Fishbein; Richard G Spencer; Luigi Ferrucci; David A Reiter Journal: NMR Biomed Date: 2019-03-12 Impact factor: 4.044