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Tortuosity

Tortuosity is a dimensionless parameter that quantifies the deviation of actual pathways from a straight line in complex structures, particularly in porous media, where it represents the ratio of the effective path length through the material to the Euclidean (straight-line) distance between endpoints.^[1] This measure, typically denoted by τ and always greater than or equal to 1, captures the sinuosity and interconnectedness of pore spaces, influencing the hindrance to transport processes such as diffusion, fluid flow, and conduction.^[2] Originating from geometric considerations, tortuosity extends beyond simple length ratios to include hydraulic, electrical, and diffusive variants, each tailored to specific physical phenomena in the medium. In porous media like soils, rocks, and engineered materials, tortuosity plays a pivotal role in determining effective transport properties, as it accounts for the increased resistance caused by tortuous paths around solid obstacles, thereby reducing diffusivity and permeability relative to free-space values.^[2] For instance, the effective diffusion coefficient D* relates to the free diffusion coefficient D by D* = D / τ in simple models, though more advanced formulations incorporate porosity (φ) as D* = φ D / τ.^[1] This parameter is essential for modeling phenomena such as groundwater flow in geosciences, where higher tortuosity correlates with lower hydraulic conductivity, and in petrophysics, where it helps predict reservoir performance. Beyond geosciences, tortuosity is critical in electrochemical devices like batteries and fuel cells, where it governs mass and charge transport through porous electrodes, directly impacting device efficiency and polarization losses.^[1] In biological contexts, such as the extracellular space (ECS) of the brain, tortuosity (often denoted λ) quantifies diffusion hindrance around cellular structures, with typical values around 1.6 that increase during pathological conditions like ischemia, affecting nutrient delivery and waste clearance.^[3] Applications also span thermal conductivity in insulators and sound absorption in acoustic materials, underscoring tortuosity's interdisciplinary relevance in optimizing material design for energy, environmental, and biomedical technologies.^[2] Tortuosity is calculated through diverse methods, including geometric approaches that trace shortest paths via imaging techniques like micro-computed tomography (μCT), flux-based simulations solving equations like Laplace's for transport fields, and empirical models relating it to porosity (e.g., τ = 1 / √φ for some media).^[1] Experimental measurements often employ real-time iontophoresis or integrative optical imaging in biological systems, while numerical tools like the Lattice Boltzmann Method enable predictions in complex microstructures.^[3] These approaches reveal that tortuosity values typically range from 1 to over 4, depending on the medium's heterogeneity and the definition used, highlighting ongoing challenges in standardization across fields.^[2]

Definition and Fundamentals

Geometric Interpretation

Tortuosity, in its geometric sense, quantifies the deviation of a pathway from a straight line within a medium, defined as the ratio of the actual tortuous path length L_e to the straight-line Euclidean distance L between endpoints, expressed as \tau = \frac{L_e}{L}.^[2] This formulation captures the sinuosity inherent in structures like porous materials, where particles or fluids must navigate around obstacles, elongating their travel compared to direct traversal.^[4] As a dimensionless parameter, tortuosity satisfies \tau \geq 1, with equality holding only for perfectly straight paths; values exceeding 1 reflect increasing curvature or meandering, which impede efficient transport by extending effective distances.^[2] In conceptual terms, a value of \tau = 1 corresponds to unobstructed linear motion, while higher \tau indicates progressively more convoluted routes, such as those in fractal-like or irregularly shaped voids.^[5] To illustrate, consider a simple two-dimensional curved line connecting two points: if the line arcs gently, \tau slightly exceeds 1, but a highly sinuous zigzag elevates it further, emphasizing how geometric complexity amplifies path length relative to displacement.^[2] Similarly, in three dimensions, a coiled tube exemplifies tortuosity, where the helical winding multiplies the centerline length against the axial straight-line span, mimicking pathways in fibrous or packed media without requiring intricate pore networks. Geometric tortuosity represents an intrinsic property of the medium's microstructure, determined solely by its spatial configuration, whereas apparent tortuosity incorporates observer-dependent or process-specific effects, such as interactions that alter perceived path efficiency.^[6]^[5] This distinction underscores tortuosity's role as a pure morphological descriptor, independent of external influences like fluid dynamics or measurement techniques.^[7]

Historical Development

The concept of tortuosity originated in the early 20th century within petrophysics and hydrology, where it was introduced to quantify the deviation of fluid flow paths from straight lines in porous media. In 1927, Josef Kozeny proposed the term in the context of modeling permeability, drawing on a capillary tube analogy to describe water conduction in soil and emphasizing the geometric lengthening of flow paths due to pore structure.^[7] This geometric interpretation laid the groundwork for understanding tortuosity as a factor influencing hydraulic conductivity in hydrological applications. By the late 1930s, Philip C. Carman advanced the Kozeny model, extending it to predict permeability in granular beds such as saturated sands, soils, and clays.^[7] A pivotal development occurred in 1942 when G.E. Archie introduced Archie's law in petrophysics, relating the formation factor to porosity (F = a/φ^m), which in subsequent interpretations accounts for the tortuous nature of conduction paths in porous rocks alongside porosity effects.^[7] This empirical relation marked tortuosity's shift toward quantitative application in reservoir characterization, influencing subsequent studies in fluid and electrical transport. Following World War II, in the post-1950s era, tortuosity gained broader adoption in materials science for analyzing diffusion and permeation in engineered porous materials, such as catalysts and filters, where it became essential for scaling microscopic pore geometry to macroscopic properties.^[8] The 1980s saw further refinements in porous media theory, with Jacob Bear and collaborators integrating tortuosity into comprehensive models of multiphase flow and transport, emphasizing its anisotropy and dependence on saturation in macroscopic averaging techniques.^[9] A key milestone in the 1990s involved the advent of X-ray computed tomography, which enabled three-dimensional visualization and direct computation of tortuosity in real porous samples like sandstones, revolutionizing the shift from idealized models to image-based analysis.^[10] In the post-2010 period, machine learning approaches have emerged for predicting tortuosity, particularly in battery electrode design, where convolutional neural networks analyze microstructural images to forecast transport properties and optimize performance.^[11]

Mathematical Formulations

Two-Dimensional Cases

In two-dimensional cases, tortuosity quantifies the deviation of transport paths from straight lines in planar structures, such as slices of porous media or curved channels. The basic geometric formulation expresses tortuosity \tau as the ratio of the effective path length to the straight-line distance, given by

\tau = \frac{\int ds}{L},

where ds is the differential arc length element along the curved path and L is the Euclidean distance between endpoints. This integral form captures the elongation due to path curvature in 2D geometries, such as those encountered in thin-layer porous materials or vascular networks viewed in cross-section.^[12] For simple 2D paths, tortuosity can be computed analytically to illustrate its value. Consider a semicircular path of radius r, where the arc length is \pi r and the straight-line distance is $2r, resulting in \tau = \pi/2 \approx 1.57.^[13] Similar calculations apply to sinusoidal curves, which model undulating flow paths in layered media; for a sine wave with amplitude a and wavelength \lambda, the path length approximates \int_0^\lambda \sqrt{1 + (2\pi a / \lambda)^2 \sin^2(2\pi x / \lambda)} \, dx, yielding \tau \approx 1.216 for moderate amplitudes where a / \lambda = 0.2. In fractal-like 2D patterns, such as self-similar pore networks, tortuosity follows power-law relations with porosity \phi, often \tau \sim \phi^{-\epsilon} where \epsilon \approx 0.5 to 1, reflecting increased path complexity at lower porosities.^[14] A vector-based approach in 2D leverages flow velocities to estimate tortuosity, particularly for hydraulic contexts, as \tau = \langle \mathbf{u} \rangle / \langle u_x \rangle, where \mathbf{u} is the velocity vector, \langle \mathbf{u} \rangle its magnitude average, and \langle u_x \rangle the component along the principal direction. This formulation, derived from lattice Boltzmann simulations, approximates the path elongation via discretized sums over grid points, \tau \approx \sum |\mathbf{u}(\mathbf{r})| / \sum u_x(\mathbf{r}), and is useful for irregular 2D porous structures like overlapping circles with porosity \phi = 0.85, yielding \tau \approx 1.13. Despite their simplicity, 2D models for tortuosity have limitations, as they often underrepresent the interconnectivity and branching observed in real three-dimensional media, leading to underestimation of effective path resistances in complex networks.^[15] For instance, at low porosities below 0.5, 2D approximations struggle to capture viable paths, resulting in geometric tortuosity values lower than hydraulic ones due to idealized connectivity.^[12]

Three-Dimensional Cases

In three-dimensional structures, geometric tortuosity quantifies the elongation of pathways within the volumetric pore network relative to a straight-line distance. It is defined as \tau = \frac{L_e}{L}, where L_e is the effective path length through the pores and L is the straight-line distance across the medium. This scalar measure generalizes to 3D by averaging over multiple paths in the pore space, often computed using volume integrals that integrate the local path lengths weighted by the pore volume fraction, such as \tau = \frac{1}{V_p} \int_{V_p} \frac{ds}{dx} \, dV, where V_p is the pore volume, ds is the differential arc length along the path, and dx is the projection along the principal direction.^[16] For anisotropic media, tortuosity is represented as a second-order tensor \boldsymbol{\tau}, capturing direction-dependent path complexities. Derived from volume-averaged Fick's law, \mathbf{J} = -\mathbf{D}_{\text{eff}} \nabla c, where \mathbf{J} is the diffusive flux and c is concentration, the effective diffusivity tensor relates to the bulk diffusivity \mathbf{D}_0 = D_0 \mathbf{I} (with \mathbf{I} the identity tensor) via \mathbf{D}_{\text{eff}} = \varepsilon D_0 \boldsymbol{\tau}^{-1}, or equivalently, the components satisfy \tau_{ij} = \varepsilon \frac{D_0 \delta_{ij}}{(D_{\text{eff}})_{ij}} in principal coordinates, where \varepsilon is porosity, \delta_{ij} is the Kronecker delta, and inversion accounts for the hindrance in each direction. This formulation arises from averaging microscopic fluxes over a representative elementary volume, incorporating the tortuosity tensor to adjust for non-uniform path orientations and lengths.^[17] In isotropic 3D random media, such as overlapping spheres, tortuosity approximates \tau \approx 1 / \sqrt{\varepsilon}, reflecting reduced diffusivity due to path meandering in disordered structures; this follows from the Bruggeman relation D_{\text{eff}} / D_0 = \varepsilon^{3/2}, implying \tau = \varepsilon / (D_{\text{eff}} / D_0) = \varepsilon^{-1/2}. Computation in voxel-based representations often employs shortest-path algorithms like A-star on 3D grids to estimate \tau by averaging geodesic distances between boundary nodes, enabling quantification in digitized porous volumes.^[4] Challenges in 3D tortuosity arise from distinguishing connected transport pathways from dead-end pores, as the former dominate effective transport while the latter inflate geometric estimates without contributing to flux; this requires separating percolating networks via graph theory or diffusion simulations to isolate relevant volumes.^[16]

Measurement and Computation

Experimental Methods

One common experimental approach to measure electrical tortuosity involves saturating a porous sample with an electrolyte solution and determining the effective electrical conductivity (σ_eff) relative to the bulk fluid conductivity (σ_bulk) using impedance spectroscopy over a frequency range, typically from 1 Hz to 1 MHz, to minimize electrode polarization effects. The sample, often a cylindrical core of 1-2 cm diameter and length, is placed between non-polarizing electrodes in a conductivity cell, and resistivity is recorded under controlled temperature (e.g., 25°C) to ensure ionic mobility consistency. Porosity (φ) is measured independently, e.g., via gravimetric methods. Tortuosity (τ) is then calculated as τ = √(φ σ_bulk / σ_eff), assuming a simplified geometric model where the formation factor approximates the square root relation for many granular media. This method has been applied to sandstones, yielding τ values around 1.5-3 for Berea samples with porosities of 15-20%.^[18] Diffusion-based experiments quantify diffusional tortuosity by tracking the self-diffusion of tracer molecules, such as water or noble gases like xenon, within a fluid-saturated porous sample using pulsed field gradient nuclear magnetic resonance (PFG-NMR). The setup typically employs a benchtop NMR spectrometer with gradient strengths up to 50 G/cm and diffusion times (Δ) of 10-100 ms to probe length scales of 100-2000 μm, allowing observation of the long-time diffusion limit where the effective diffusion coefficient (D_eff) stabilizes. Porosity (ε) is independently measured via NMR relaxation or gravimetric methods, and τ is derived from D_eff = D_0 ε / τ, where D_0 is the free-fluid diffusion coefficient calibrated against bulk samples at the same temperature and viscosity. For bead packs or catalyst pellets, this yields τ ≈ 1.2-1.6, with higher values in heterogeneous rocks due to restricted pathways. Recent applications as of 2025 include refined PFG-NMR for assessing tortuosity in fast-charging supercapacitors.^[19] Hydraulic permeability tests infer hydraulic tortuosity from steady-state fluid flow through a saturated porous plug under controlled pressure gradients, adhering to Darcy's law: Q = -(k A / μ) (ΔP / L), where Q is the volumetric flow rate, A the cross-sectional area, μ the fluid viscosity, ΔP the pressure drop, L the sample length, and k the intrinsic permeability. The setup involves a core holder with upstream and downstream reservoirs maintaining constant ΔP (e.g., 0.1-1 bar) using a syringe pump for low-viscosity fluids like water or brine, with flow rates measured via effluent collection or flow meters over 10-30 minutes to ensure steady state. Porosity and characteristic pore size are determined separately (e.g., via mercury intrusion), allowing τ to be estimated by relating k to models like Kozeny-Carman, where higher τ reduces k for given ε; typical results for sand packs show τ increasing from 1.1 at ε=0.8 to 2.0 at ε=0.4.^[20] Accuracy in these methods relies on calibration against samples with known geometries, such as uniform glass bead packs (τ ≈ 1.4), to validate instrument response and fluid properties, with errors typically below 5% for homogeneous media but rising to 20-30% in heterogeneous samples due to local variations in pore connectivity and partial saturation effects. Post-2000 advancements, including higher-resolution impedance analyzers (sub-mHz frequencies) and multi-gradient NMR sequences, have improved microscale resolution to 10-50 μm, reducing artifacts from sample heterogeneity by enabling shorter diffusion times and better separation of bulk versus restricted diffusion signals. Common error sources include incomplete saturation, temperature fluctuations (±0.1°C control recommended), and surface interactions altering effective pathways, necessitating replicate measurements on multiple core orientations.

Numerical and Image-Based Techniques

Numerical and image-based techniques provide computational frameworks for estimating tortuosity in complex porous structures using digital representations derived from advanced imaging, such as micro-computed tomography (μCT) or magnetic resonance imaging (MRI). These methods are essential for analyzing irregular geometries where direct measurement is challenging, enabling the quantification of tortuosity through algorithmic processing of voxelized data. By segmenting the pore space from solid phases in the images, tortuosity can be computed as a structural descriptor that informs transport behavior without relying on physical experiments.^[5] A prominent approach involves voxel-based shortest path algorithms applied to segmented 3D images, where tortuosity τ is calculated as the ratio of the average geodesic distance (actual path length along connected pores) to the Euclidean distance across the domain. Dijkstra's algorithm, an efficient graph-search method, is commonly used to identify these shortest paths between boundary points or random pairs within the pore network, with τ obtained by averaging over numerous paths to account for heterogeneity. This technique has been implemented in open-source tools like the TORT3D MATLAB code, which processes binary 3D images of unconsolidated porous media and demonstrates accuracy comparable to analytical benchmarks for simple structures. For instance, in μCT scans of sandstone samples, this method yields τ values around 1.2–1.5, reflecting the winding nature of pore channels.^[4]^[21] Finite element modeling (FEM) offers an alternative by simulating transport processes, such as steady-state diffusion, to derive tortuosity indirectly through effective medium theory. In this method, the governing diffusion equation ∇·(D ∇c) = 0 is solved on a meshed representation of the imaged microstructure, where D is the local diffusivity (set to D_0 in pores and 0 in solids), yielding the effective diffusivity D_eff from the flux across the domain; tortuosity is then extracted as τ = ε (D_0 / D_eff), with ε as the porosity. Commercial software like COMSOL Multiphysics supports these simulations by importing segmented image data and handling anisotropic meshes for high-fidelity results, particularly in fibrous or granular media. Representative applications in polymer electrolytes show τ values of 2–4, aligning with enhanced path complexity in 3D.^[22]^[1] Since 2015, machine learning approaches, especially convolutional neural networks (CNNs), have gained traction for rapid tortuosity prediction from microstructure images, bypassing iterative simulations. These models are trained on large datasets of synthetic porous media generated via random obstacle placement, learning to map 2D or 3D image features directly to τ values, often alongside porosity and permeability. A seminal CNN implementation achieves prediction errors below 5% for tortuosity in granular packs, enabling inference in seconds on GPU hardware compared to hours for traditional methods. Such techniques are particularly effective for high-throughput analysis of μCT-derived datasets in heterogeneous rocks. Recent extensions as of 2025 include neural networks for thermal tortuosity prediction using macroscopic geometric parameters.^[23]^[24] Validation of these techniques typically involves cross-comparison with experimental data, such as mercury intrusion porosimetry or NMR diffusion measurements, confirming that numerical τ estimates match within 10% for diverse porous materials like soils and composites. Computational costs, however, escalate with resolution; for 3D grids exceeding 256³ voxels, Dijkstra-based methods may require 1–10 hours on multi-core CPUs due to path enumeration, while FEM simulations on equivalent meshes can demand 24–48 hours or more, necessitating optimizations like parallel processing or reduced-order modeling for practical use.^[5]^[21]

Applications

Transport in Porous Media

In porous media such as rocks and soils, tortuosity plays a crucial role in governing fluid flow and mass transport by accounting for the elongated and convoluted nature of pore pathways, which increases flow resistance compared to straight-line distances. The Kozeny-Carman equation integrates tortuosity with porosity to predict permeability, expressed as k = \frac{\varepsilon^3}{(1-\varepsilon)^2} \cdot \frac{d^2}{180 \tau}, where k is the permeability, \varepsilon is the porosity, d is the particle diameter, and \tau is the tortuosity. This formulation highlights how higher tortuosity reduces permeability by lengthening effective flow paths, making it essential for hydraulic modeling in geological formations.^[25] For diffusive transport, tortuosity modifies Fick's law through the effective diffusion coefficient D_{\text{eff}} = D_0 \cdot \frac{\varepsilon}{\tau}, where D_0 is the bulk diffusion coefficient, thereby explaining the slowdown of molecular diffusion in tortuous pores due to increased path lengths and reduced cross-sectional area availability.^[26] This adjustment is particularly important in low-permeability media where diffusion dominates over advection, influencing solute migration rates. Tortuosity can be estimated from electrical conductivity measurements, as outlined in experimental methods.^[26] In geosciences, tortuosity informs predictions of groundwater flow and oil recovery in sandstone reservoirs, where typical values range from approximately 2 to 4, reflecting moderate pore complexity that balances connectivity and resistance.^[27] For instance, in sandstone formations, higher tortuosity correlates with reduced recovery efficiency during enhanced oil recovery operations, guiding reservoir simulation to optimize injection strategies. Recent models from the 2020s incorporate tortuosity into multiphase flow simulations for CO2 sequestration, using techniques like the Lattice Boltzmann Method to evaluate its effects on injection dynamics and plume migration in heterogeneous aquifers.^[28] These simulations demonstrate that tortuosity influences optimal pulsatile injection frequencies, enhancing CO2 displacement by up to 16% in water-saturated media.^[28]

Electrochemistry and Energy Storage

In electrochemical systems such as lithium-ion batteries, tortuosity significantly influences ion and charge transport within porous electrodes by extending the effective path length for species diffusion. In Newman's concentrated solution theory, the effective ionic diffusivity in the electrolyte phase of porous electrodes is expressed as D_{\text{eff}} = \frac{\epsilon}{\tau} D, where \epsilon is the electrode porosity, \tau is the tortuosity, and D is the bulk diffusivity; this adjustment accounts for the tortuous pore structure, leading to higher concentration gradients and overpotentials that limit overall cell performance.^[29] This framework, foundational to porous electrode modeling, underscores how elevated tortuosity impedes lithium-ion migration, particularly in high-rate operations where rapid charging exacerbates transport limitations. Quantification of tortuosity in porous electrodes reveals typical values of 3 to 5 for graphite anodes, depending on microstructure and porosity levels around 30-40%; these values arise from the random orientation of active particles and binders, which increase ionic resistance and reduce the effective conductivity by factors of 6 to 20.^[30]^[31] Higher tortuosity directly impairs rate capability by amplifying concentration polarization, resulting in diminished capacity utilization at currents above 1C, and accelerates capacity fade through mechanisms like uneven solid electrolyte interphase (SEI) formation and lithium plating during cycling.^[32]^[33]^[34] Optimization strategies focus on reducing tortuosity through engineered microstructures, particularly in solid-state batteries where ionic transport is more constrained. Post-2020 research has demonstrated that vertically aligned architectures, such as carbon nanotube scaffolds in polymer-based cathodes, can lower the tortuosity factor to 1.5 from typical values of 4-5, enhancing Li⁺ conductivity to 4.25 × 10⁻⁴ S/cm at 40°C and enabling stable cycling at low stack pressures with minimal fade over 100 cycles.^[35]^[36] These alignments, achieved via 3D printing or shear-induced orientation, create straight ion pathways that mitigate diffusion bottlenecks and improve energy density in all-solid-state systems.^[37] In fuel cell applications, tortuosity in gas diffusion layers (GDLs) critically governs oxygen transport to the catalyst layer, where values typically ranging from 2 to 4 in carbon fiber-based GDLs elongate gas diffusion paths and elevate mass transport resistance.^[38] The effective oxygen diffusivity follows D_{\text{eff, dry}} = \frac{\epsilon}{\tau} D_{\text{bulk}}, with dry GDLs showing D_{\text{eff}} \approx 0.038 cm²/s at 30°C; under humid operation, water saturation further increases effective tortuosity, reducing diffusivity to 30% of dry values and causing performance losses up to 100 mV at 1 A/cm² due to oxygen starvation.^[39]^[40] This highlights tortuosity as a key factor in mitigating flooding-related inefficiencies in polymer electrolyte fuel cells.^[41]

Biological and Medical Contexts

In biological systems, tortuosity quantifies the deviation from straight-line paths in natural structures such as vascular and neural networks, influencing transport efficiency and physiological function. In vasculature, it measures vessel winding, which elevates flow resistance and impairs perfusion; for instance, coronary artery tortuosity can increase hemodynamic resistance by up to 92% during exercise, contributing to myocardial ischemia.^[42] This effect is pronounced in pathological conditions like tumor angiogenesis, where elevated vessel tortuosity disrupts uniform blood distribution and hinders therapeutic drug delivery by increasing interstitial pressure and reducing convective transport. Quantitative vessel tortuosity metrics, such as those derived from imaging, serve as biomarkers for assessing vascular normalization in response to anti-angiogenic therapies.^[43] In neural tissues, tortuosity describes the meandering of axonal paths within white matter tracts, which can be quantified using diffusion MRI to evaluate microstructural integrity. These MRI-derived measures reveal axonal tortuosity variations linked to neurodegeneration, where increased undulation correlates with axonal loss and demyelination in conditions like multiple sclerosis. Such assessments help track disease progression by highlighting deviations in fiber orientation and dispersion.^[44] Physiologically, higher tortuosity in structures like retinal vessels correlates with reduced oxygen saturation and tissue hypoxia, elevating metabolic demands for oxygen delivery. In sickle cell retinopathy, for example, increased vessel tortuosity is associated with lower retinal vascular oxygen content, potentially exacerbating energy costs for maintaining perfusion in oxygen-sensitive tissues.^[45] Advancements in the 2020s have integrated computational anatomy for tortuosity quantification in medical diagnostics, particularly for stroke risk prediction. Retinal vascular tortuosity features, analyzed via AI-driven imaging, enhance predictive models for incident stroke beyond traditional risk factors, with metrics like arterial inflection count showing significant associations. Similarly, intracranial vessel tortuosity assessments via CTA predict procedural challenges in thrombectomy and correlate with ischemic outcomes.^[46]^[47]

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Jul 26, 2013 · The resistance of the coronary arteries increased up to 92% due to the CT during exercise. A maximum increase of 96% was observed in the mean ...
[43]
A tumor vasculature–based imaging biomarker for predicting ...
Nov 25, 2022 · We present a new imaging biomarker, quantitative vessel tortuosity (QVT), and evaluate its association with response and survival in patients ...Results · Materials And Methods · Vascular Feature Extraction<|separator|>
[44]
[PDF] Diffusion distinguishes between axonal loss and demyelination in ...
In this work, we establish for the first time that the AWF is most sensitive to axonal loss, while the EAS tortuosity is most sensitive to demyelination. For ...Missing: path τ 1.1-1.5
[45]
Relationship between retinal vessel tortuosity and oxygenation in ...
Nov 18, 2019 · Reduced retinal vascular oxygen (O2) content causes tissue hypoxia and may lead to development of vision-threatening pathologies.Image Acquisition And... · Vascular Oxygen Content · Vessel Tortuosity IndexMissing: costs | Show results with:costs
[46]
Retinal vascular fingerprints predict incident stroke - Heart
Tortuosity. Among 30 parameters extracted using 10 extraction methods, only arterial inflection count tortuosity was found to be associated with stroke risk. ...
[47]
Impact of intracranial vessel tortuosity on mechanical thrombectomy ...
Aug 13, 2025 · Conclusions: Intracranial vessel tortuosity reduces recanalization success and increases procedural challenges. Its impact on complications and ...