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Elastography

Elastography is a non-invasive medical imaging modality that quantifies the biomechanical properties of soft tissues, particularly stiffness and elasticity, by applying mechanical stress and measuring the resulting deformation or wave propagation.^[1] This technique leverages the principle that pathological tissues, such as tumors or fibrotic areas, often exhibit altered mechanical properties compared to healthy tissue, enabling differentiation based on elasticity rather than just anatomy.^[2] Developed in the late 1980s and early 1990s as an extension of ultrasound and magnetic resonance imaging (MRI), elastography has evolved into a quantitative tool grounded in Hooke's law, which relates applied stress to strain through the elastic modulus.^[1] The core principles of elastography involve perturbing the tissue—either through quasi-static compression, harmonic vibrations, or transient acoustic pulses—and imaging the mechanical response, such as displacement, strain, or shear wave speed, to infer properties like Young's modulus (E) or shear modulus (G).^[1] In shear wave elastography, a subtype of ultrasound elastography, techniques typically use acoustic radiation force or external probes to generate shear waves, whose propagation velocity (c_s = √(G/ρ), where ρ is tissue density) indicates stiffness, with stiffer tissues showing higher speeds. Magnetic resonance elastography (MRE), on the other hand, employs low-frequency mechanical drivers (e.g., 50–100 Hz) synchronized with MRI phase-contrast sequences to map three-dimensional stiffness distributions.^[3] Both modalities assume tissue isotropy and linearity for simplification, though real tissues may exhibit viscoelasticity and anisotropy, influencing accuracy.^[2] Elastography encompasses several subtypes, broadly categorized by imaging platform and perturbation method. Ultrasound elastography includes strain elastography (quasi-static, qualitative or semi-quantitative via strain ratio) and shear wave elastography (dynamic, quantitative subtypes like transient elastography (TE), point shear wave elastography (pSWE), and two-dimensional shear wave elastography (2D-SWE)), which measure wave speeds in meters per second or moduli in kilopascals.^[2] MRE provides volumetric assessment, particularly useful for deep organs, by inverting wave equations to produce elastograms.^[3] Optical elastography, employing light-based techniques such as optical coherence elastography, targets high-resolution imaging of superficial tissues.^[4] Emerging variants, such as surface acoustic wave elastography, target superficial tissues but remain less widespread.^[5] Clinically, elastography is widely applied for non-invasive disease assessment, with liver fibrosis staging being the most established use; for instance, TE achieves an area under the receiver operating characteristic curve (AUROC) of 0.89 for significant fibrosis (F≥2).^[2] In breast imaging, it differentiates benign from malignant lesions with sensitivities of 83–93% and specificities of 81–84%.^[2] Other applications include thyroid nodule malignancy detection (sensitivity 80–92%, specificity 85–90%), prostate cancer biopsy guidance (sensitivity up to 96%), and kidney allograft fibrosis evaluation (accuracy ~95%).^[2] Despite limitations like operator dependency, obesity-related failures, and artifacts from calcifications, elastography enhances diagnostic precision when integrated with conventional imaging.^[3]^[2]

Introduction

Definition and Basic Principles

Elastography is a non-invasive medical imaging technique that quantifies the mechanical properties of soft tissues, particularly stiffness and viscosity, by applying controlled deformations and imaging the resulting tissue responses to map spatial variations in elasticity.^[6] This approach visualizes biomechanical contrasts between healthy and pathological tissues, where alterations in elastic properties can signal disease.^[1] Unlike conventional imaging modalities that primarily depict anatomical structures or physiological functions, elastography focuses on tissue biomechanics to provide complementary diagnostic information.^[6] At its core, elastography relies on fundamental mechanical principles. Elasticity describes a material's ability to undergo reversible deformation and return to its original shape, governed by Hooke's law: \sigma = E \epsilon, where \sigma is stress (force per unit area, in pascals), \epsilon is strain (relative deformation, \Delta L / L), and E is Young's modulus, quantifying longitudinal stiffness.^[7] Viscosity, in contrast, characterizes time-dependent resistance to deformation, often modeled alongside elasticity in viscoelastic tissues using frameworks like the Kelvin-Voigt model.^[7] Young's modulus is typically measured in kilopascals (kPa) or megapascals (MPa), with elastography techniques producing either qualitative color-coded maps of relative stiffness or quantitative absolute values for precise assessment.^[7] Tissue stiffness serves as a key biomarker for pathology because diseased states, such as tumors, often exhibit increased rigidity compared to surrounding healthy tissue, attributable to factors like elevated cell density, fibrosis, and extracellular matrix remodeling.^[8] For instance, invasive breast carcinomas can display Young's moduli of approximately 490 kPa under compression, far exceeding the 20 kPa typical of adipose tissue.^[7] Elastography is adaptable to platforms including ultrasound, magnetic resonance imaging, and optical coherence tomography, enabling broad clinical integration.^[1]

Historical Background

The concept of elastography emerged from early investigations into tissue mechanics using ultrasound, with foundational work in the 1950s at Wright-Patterson Air Force Base demonstrating that the speed of sound in biological tissues varies under compression, laying groundwork for later elasticity imaging techniques.^[9] Building on these principles, researchers in the 1980s began exploring strain imaging, culminating in the seminal 1991 paper by Ophir et al., which introduced "elastography" as a quantitative method to image tissue elasticity by measuring axial strain from ultrasound echoes before and after compression, marking the birth of quasistatic ultrasound strain elastography.^[10] In the 1990s, quasistatic elastography advanced through refinements in strain estimation and display, enabling visualization of relative tissue stiffness differences in real-time.^[11] A parallel milestone occurred in magnetic resonance elastography (MRE), with Muthupillai et al. demonstrating in 1995 a technique to directly visualize propagating acoustic strain waves in tissues using phase-contrast MRI synchronized to mechanical vibrations, allowing quantitative mapping of shear stiffness.^[12] The 2000s saw rapid expansion and commercialization of elastography modalities. Transient elastography was introduced in 2003 via the FibroScan device by Sandrin et al., which uses a single ultrasound transducer to generate and detect low-frequency shear waves for liver stiffness assessment, facilitating non-invasive fibrosis evaluation.^[13] That same year, Nightingale et al. developed acoustic radiation force impulse (ARFI) imaging, employing focused ultrasound pulses to induce localized tissue displacement and quantify stiffness through shear wave propagation analysis.^[14] In 2004, Bercoff et al. pioneered supersonic shear imaging (SSI), a method generating plane shear waves via sequential acoustic pushes to map 2D elasticity in real time, enhancing spatial resolution for clinical applications.^[15] During the 2010s, elastography gained regulatory and clinical traction, with the FDA approving FibroScan for liver stiffness measurement in 2013, followed by clearances for ARFI and 2D shear wave systems on various ultrasound platforms. Integration into guidelines accelerated adoption; for instance, the 2015 EASL-ALEH Clinical Practice Guidelines recommended non-invasive elastography as a first-line tool for staging liver fibrosis in chronic liver diseases, reducing reliance on biopsy.^[16] Post-2020 developments have incorporated artificial intelligence to improve elastography processing, with AI algorithms enhancing strain quantification, reducing operator variability, and boosting diagnostic accuracy for conditions like breast tumors and liver fibrosis, as evidenced in meta-analyses showing superior sensitivity over traditional methods.^[17] Concurrently, prototypes for wearable elastography have emerged, such as the 2025 mechano-acoustic sensing patch by Liu et al., which enables wireless, ambulatory monitoring of tissue stiffness through conformal skin contact and automated shear wave analysis.^[18]

Fundamental Principles

Inducing Tissue Deformation

In elastography, inducing tissue deformation is the initial step that applies mechanical stress to tissues, enabling the subsequent assessment of their elastic properties, such as stiffness related to Young's modulus. This deformation mimics physiological loading conditions and must be controlled to ensure reproducibility and safety across various imaging modalities.^[19]^[20] Deformation types primarily include compressive (axial strain), shear (lateral waves), and dynamic (vibrations). Compressive deformation involves applying normal stress along the axis of loading, resulting in axial strain that is particularly useful for superficial tissues. Shear deformation generates transverse waves through lateral forces, allowing evaluation of shear modulus in viscoelastic materials. Dynamic deformation employs harmonic vibrations to propagate waves throughout the tissue volume, facilitating three-dimensional stiffness mapping.^[19]^[20]^[21] External methods rely on forces applied from outside the body. Manual compression, often freehand, uses transducer pressure to induce quasi-static strain, suitable for operator-controlled assessments. Physiological motion harnesses natural body movements, such as breathing or heartbeat, to generate passive deformation without additional equipment. Mechanical actuators, including electromechanical drivers or piezoelectric devices, deliver precise vibrations (typically 50–500 Hz) for dynamic excitation, as seen in systems like FibroScan for liver evaluation.^[19]^[20]^[21] Internal methods generate deformation directly within the tissue using imaging hardware. Acoustic radiation force applies focused ultrasound pulses to create localized displacement, producing shear waves with minimal skin contact. In magnetic resonance elastography, external actuators induce vibrations, while magnetic field gradients primarily encode the resulting motion rather than directly causing deformation; optical forces from lasers remain experimental and limited to superficial or in vitro applications.^[19]^[20]^[21] Key factors influencing deformation induction include frequency, amplitude, and tissue depth penetration. Low frequencies (static or near-static) suit compressive methods for uniform strain, while higher frequencies (e.g., 50–500 Hz in vibrations or ultrasound pulses) propagate shear waves but increase attenuation. Amplitude determines displacement magnitude, typically limited to micrometers (10–20 μm) to maintain linearity. Penetration depth varies inversely with frequency and tissue attenuation, restricting deep imaging in obese patients or dense organs.^[19]^[20]^[21] Safety considerations prioritize non-invasive application to prevent discomfort or damage, with strain typically below 1% in ultrasound-based methods and acoustic pulse intensities adhering to FDA guidelines (e.g., derated spatial-peak pulse-average intensity < 190 W/cm² for non-fetal tissues). These constraints ensure patient tolerability while achieving measurable deformation.^[19]^[20]^[21]^[22]

Detecting Displacement and Strain

Detecting tissue displacement is a fundamental step in elastography, where induced deformations are quantified to infer mechanical properties. In ultrasound elastography, displacement tracking commonly employs speckle tracking, which correlates pre- and post-deformation B-mode echo patterns to estimate local tissue motion with sub-wavelength accuracy. This method leverages the random interference patterns formed by backscattered ultrasound waves from tissue microstructures. In magnetic resonance elastography (MRE), phase-contrast MRI sequences capture cyclic displacements by encoding motion into the phase of the MR signal, enabling visualization of propagating shear waves with micrometer sensitivity.^[23] For optical elastography, techniques such as Doppler optical coherence tomography (OCT) detect phase shifts in the interference fringes caused by tissue motion, akin to Doppler ultrasound but at optical wavelengths, providing high-fidelity tracking of nanoscale displacements.^[24] Once displacements are tracked, strain is calculated to represent local tissue deformation. Axial strain, defined as \varepsilon = \Delta L / L where \Delta L is the change in length and L is the original length, is derived by differentiating axial displacements along the compression direction, often using least-squares estimation on small windows of data.^[25] Shear strain, arising from wave propagation, is computed from the gradient of transverse displacements, capturing off-axis deformations in viscoelastic media. For more complex geometries, finite element methods solve the inverse problem by minimizing the difference between measured and modeled displacements, yielding strain fields that account for boundary conditions and tissue heterogeneity.^[26] Quantitative metrics from these measurements include strain maps, which visualize relative stiffness as color-coded overlays, and reconstructions of the elasticity modulus. For incompressible soft tissues, the Young's modulus E approximates $3\mu, where \mu is the shear modulus estimated from strain distributions under known stress.^[7] These maps provide qualitative contrast for lesion detection, while modulus values offer absolute quantification, such as elevated E in fibrotic liver tissue exceeding 10 kPa.^[27] Challenges in detection include noise and artifacts that degrade accuracy. Signal-to-noise ratio (SNR) in displacement estimates is critical, with low SNR leading to decorrelation errors in speckle tracking or phase wrapping in MRI; typical elastographic SNR values range from 10-30 in clinical scans, improved by multi-frame averaging.^[28] Motion artifacts from physiological sources, like cardiac or respiratory movement, are mitigated using correction algorithms such as rigid-body registration or Kalman filtering to align pre- and post-deformation images.^[29] Resolution limits constrain the detail of displacement and strain maps. Spatial resolution in ultrasound elastography is typically 1-2 mm, limited by beam width and speckle size, while optical methods achieve 1-10 \mum via high numerical aperture focusing.^[30] Temporal resolution varies from 30-60 frames per second in real-time ultrasound systems to sub-millisecond rates in high-speed OCT, enabling dynamic wave imaging, whereas MRE is constrained to 10-20 Hz due to acquisition times.^[31]

Data Processing and Image Formation

Data processing in elastography begins with filtering the raw displacement data to mitigate noise, such as speckle artifacts in ultrasound or phase noise in magnetic resonance imaging, often employing directional filters to isolate relevant wave propagation signals.^[9] Subsequent steps involve inverting these filtered displacements to estimate mechanical properties like the shear modulus, typically using least-squares inversion of wave equations that solve the inverse elasticity problem under assumptions of local homogeneity.^[32] This inversion reconstructs 2D or 3D elasticity maps by iteratively fitting measured displacements to biomechanical models, enabling spatial mapping of tissue stiffness across the imaging volume.^[33] Image formation integrates these processed maps into interpretable visualizations, commonly overlaying color-coded elastograms on grayscale B-mode ultrasound images, where hues like red indicate softer regions and blue denote stiffer areas for intuitive qualitative assessment.^[2] Quantitative outputs include region-of-interest (ROI) measurements of absolute stiffness in kilopascals (kPa), derived from calibrated color scales that map strain or wave speed to modulus values, alongside 3D volume renders for volumetric analysis in complex anatomies. These approaches distinguish qualitative relative stiffness patterns from quantitative absolute metrics, with reproducibility often exceeding an intraclass correlation coefficient (ICC) of 0.8 in controlled phantom studies.^[34] Advanced algorithms enhance accuracy in heterogeneous tissues through finite element modeling, which simulates wave propagation in discretized domains to account for spatial variations in modulus and boundary conditions during inversion.^[35] Post-2020 developments incorporate machine learning, particularly convolutional neural networks, to reduce artifacts by learning noise patterns from training data and refining displacement estimates without explicit physical modeling.^[36] Common output formats include 2D elastograms displaying strain distributions and wave speed maps, where shear wave velocity c relates to shear modulus \mu and tissue density \rho via

c = \sqrt{\frac{\mu}{\rho}}

facilitating direct stiffness quantification.^[37]

Ultrasound Elastography

Strain Elastography

Strain elastography is a quasistatic ultrasound-based imaging technique that assesses tissue stiffness by measuring relative axial strain induced by external compressive forces applied to the skin surface. The method relies on gentle, cyclic compression using either a freehand ultrasound transducer or a specialized mechanical device, which deforms the underlying tissue. Strain is derived from tracking the displacement of ultrasound echoes between uncompressed and compressed states, primarily through correlation of radiofrequency signals to detect local tissue motion and subsequent strain patterns. This approach visualizes softer regions as areas of higher strain (color-coded in red or green on elastograms) and stiffer areas as lower strain (blue), providing a qualitative map of tissue elasticity.^[10] Originally developed by Ophir and colleagues in 1991, strain elastography represented a pioneering effort to quantify tissue elasticity non-invasively by imaging strain distributions in soft tissues under external loading. The technique has since evolved, with early implementations focusing on manual freehand compression prone to variability, while contemporary systems integrate automated probes that deliver consistent, controlled pressure cycles to enhance image stability and reduce artifacts. These advancements include real-time feedback mechanisms and quality control metrics, such as strain uniformity scores, to guide operators toward optimal acquisition conditions.^[10] Key advantages of strain elastography include its simplicity, as it requires no additional hardware beyond standard ultrasound equipment, absence of ionizing radiation, and capability for real-time visualization during examination, facilitating immediate clinical decision-making. However, it is limited by high operator dependence, as inconsistent compression force and angle can lead to unreliable strain estimates, and it offers only semi-quantitative results without direct measurement of absolute mechanical properties like Young's modulus. Artifacts from out-of-plane motion or heterogeneous tissue further challenge reproducibility.^[2]^[38] In clinical practice, strain elastography employs metrics such as the strain ratio, calculated as the ratio of strain in a reference healthy region to that in the target lesion (values greater than 1 indicate relative stiffness), and the strain index, which averages strain within a selected area to quantify local elasticity variations. These semi-quantitative tools aid in differentiating benign from malignant lesions, for instance in breast or thyroid imaging, where stiffer tissues often correlate with pathology, though thresholds vary by organ and require site-specific validation.^[39]^[40]

Shear Wave Elastography

Shear wave elastography (SWE) is a dynamic ultrasound technique that quantifies tissue stiffness by generating and tracking shear waves within the tissue. Acoustic radiation force, produced by focused ultrasound push pulses, induces localized shear waves that propagate laterally from the excitation point. These waves are then detected and their propagation speed is measured using ultrafast ultrasound imaging, which acquires frames at rates exceeding 5000 Hz to capture the rapid motion. This method allows for real-time visualization of shear wave propagation and derivation of local stiffness properties.^[41] The shear modulus (μ), a measure of tissue elasticity, is calculated from the shear wave speed (c) using the equation μ = ρ c², where ρ is the tissue density, typically approximated as 1000 kg/m³ for soft tissues. This relationship assumes isotropic, incompressible materials and provides absolute quantification of stiffness in units of kilopascals (kPa), distinguishing SWE from relative strain-based methods. For example, in healthy liver tissue, shear wave speeds typically range from 1.0 to 1.3 m/s, corresponding to shear moduli of approximately 1 to 1.7 kPa.^[42]^[41] A prominent variant is supersonic shear imaging (SSI), which employs multiple sequential push pulses from a linear array to create shear waves that propagate faster than the imaging beam, enabling the construction of two-dimensional stiffness maps over extended regions. In SSI, the push foci are positioned along a line, generating converging shear wavefronts whose interference patterns are analyzed to map elasticity with high spatial resolution (on the order of 1 mm). This approach facilitates comprehensive imaging of tissue heterogeneity, such as in abdominal organs.^[41]^[27] SWE offers key advantages, including absolute stiffness quantification without external compression and penetration depths up to 8 cm, making it suitable for evaluating both superficial and deeper structures like the liver or muscles. Limitations include sensitivity to confounders such as obesity or inflammation, which can attenuate wave propagation. Recent post-2020 advances incorporate artificial intelligence for enhanced dispersion analysis, enabling better characterization of tissue viscoelasticity by modeling frequency-dependent shear wave speeds and viscosity. Machine learning algorithms process dispersion curves to differentiate elastic and viscous components, improving diagnostic accuracy in conditions like fibrosis.^[43]^[44]^[45]^[46]

Acoustic Radiation Force Impulse Imaging

Acoustic Radiation Force Impulse (ARFI) imaging is an ultrasound-based elastography technique that employs short-duration acoustic radiation force pulses, typically lasting less than 1 millisecond, to generate localized tissue displacements and quantify mechanical properties at specific points.^[47] This method uses a single diagnostic ultrasound transducer to both apply the force via a focused "pushing" beam and track the resulting tissue motion with high-frequency tracking beams, enabling integration with conventional B-mode imaging.^[47] The acoustic radiation force induces shear waves within a region of excitation (ROE), and the tissue response—such as peak displacement or recovery time—is monitored using correlation-based speckle-tracking algorithms to assess stiffness.^[47] In ARFI, stiffer tissues exhibit smaller displacements and faster recovery due to the inverse relationship between displacement magnitude and shear modulus, allowing for point-wise elasticity estimation.^[47] Quantitative metrics include Young's modulus derived from displacement profiles, with examples showing values around 4 kPa in soft background tissue and up to 58 kPa in stiffer lesions, providing contrast for focal abnormalities.^[47] Variants may measure shear wave speed outside the ROE using time-of-flight methods, further characterizing local viscoelastic properties.^[47] Commercially, this is implemented as Virtual Touch tissue quantification on systems like the Siemens ACUSON S2000 scanner, which automates ARFI-based stiffness measurements.^[47] ARFI offers advantages such as high spatial resolution comparable to B-mode ultrasound, targeted probing of regions of interest without external compression, and reduced operator dependency compared to strain-based methods.^[47] It excels in scenarios requiring precise, localized assessment, such as evaluating focal lesions in organs like the liver or breast.^[47] However, limitations include its point-sampling nature, which does not produce full 2D elasticity maps, potential depth restrictions due to acoustic attenuation, and challenges with quantitative accuracy in heterogeneous tissues.^[47]

Transient Elastography

Transient elastography is a non-invasive ultrasound-based technique primarily used for assessing liver stiffness through the propagation of low-frequency shear waves generated by a specialized probe. The FibroScan device, developed by Echosens, employs a combined ultrasound transducer and vibrator mounted on the probe's axis, which is placed on the skin over the right liver lobe. The vibrator induces a mechanical pulse at a frequency of 50 Hz, propagating a shear wave through the liver tissue, while the ultrasound transducer tracks the wave's propagation velocity along a line 25–65 mm below the skin surface, covering a cylindrical volume approximately 1 cm wide and 4 cm long.^[48]^[13] The measured shear wave velocity is converted to liver stiffness using the [formula E](/page/Formula_E) = 3 [\rho](/page/Density) v^2, where E is the Young's modulus in kilopascals (kPa), \rho is the tissue density (assumed 1.0 g/cm³), and v is the shear wave velocity in meters per second; results are reported as a median value from at least 10 valid acquisitions, typically ranging from 2.5 to 75 kPa. Normal liver stiffness is below 7 kPa, while values exceeding 12.5 kPa are indicative of cirrhosis, with diagnostic cutoffs varying slightly by etiology (e.g., 12.5–14.6 kPa for advanced fibrosis in chronic hepatitis C). This method provides a one-dimensional profile of stiffness, focusing on a fixed depth range suitable for liver evaluation.^[48]^[49] Transient elastography offers several advantages, including rapid performance in under 5 minutes, painlessness, high reproducibility with intra- and interobserver agreement exceeding 95%, and standardization that enables widespread clinical use without sedation. It is particularly valuable for serial monitoring in chronic liver disease patients. However, limitations include reduced reliability in obese individuals (BMI >30 kg/m²), where failure rates can reach 20%, as well as in cases of ascites or severe inflammation, which may confound results; an XL probe variant addresses some obesity issues by extending measurement depth.^[48]^[49] Validation studies demonstrate strong correlation with liver biopsy for fibrosis staging, with area under the receiver operating characteristic curve (AUROC) values of 0.95–0.97 for detecting cirrhosis (F4) and over 0.90 for advanced fibrosis (F≥3), enabling accurate exclusion of significant disease in low-risk cases and reducing the need for invasive procedures. Like other shear wave techniques, it derives stiffness from wave speed but is limited to one-dimensional assessment without real-time imaging.^[48]^[49]

Magnetic Resonance Elastography

Principles of Wave Propagation in MRE

In magnetic resonance elastography (MRE), mechanical waves are generated using external drivers, such as pneumatic actuators or piezoelectric devices, which apply harmonic vibrations to the tissue at frequencies typically ranging from 50 to 200 Hz.^[50] These drivers are positioned outside the MRI bore to avoid interference, and the induced motion is precisely synchronized with the MRI pulse sequence using a signal generator to ensure accurate capture of wave propagation.^[51] This setup allows for the creation of shear waves that propagate through the tissue, providing a basis for mapping mechanical properties noninvasively.^[52] The propagation of these waves in biological tissues is modeled using the viscoelastic wave equation, which accounts for both elastic and viscous behaviors inherent to soft tissues.^[51] In the frequency domain, this is often represented by the Helmholtz equation for harmonic waves, assuming local homogeneity: \nabla^2 \mathbf{u} + k^2 \mathbf{u} = 0, where \mathbf{u} is the displacement vector and k is the complex wave number incorporating tissue stiffness and damping.^[50] Tissues exhibit attenuation, where wave amplitude decreases due to energy dissipation from viscosity, and dispersion, where wave speed varies with frequency, leading to broader wavelengths at lower frequencies for better penetration.^[51] These effects are critical for interpreting wave patterns, as stiffer tissues propagate waves faster with less attenuation.^[50] Detection of wave propagation relies on motion-sensitive gradients integrated into the MRI sequence, which encode tissue displacements into the phase of the MR signal.^[52] Bipolar gradient pulses, oscillating at the driver frequency, induce phase shifts proportional to the local displacement magnitude, typically on the order of micrometers (\sim 1-10 \, \mum).^[51] Multiple phase images are acquired across the wave cycle (e.g., 8 offsets over one period) and processed via Fourier transformation to extract displacement fields in three orthogonal directions.^[50] This phase-contrast approach enables high-resolution visualization of propagating waves without external reference frames.^[52] A key advantage of MRE's wave propagation principles is the ability to achieve whole-organ 3D imaging, facilitated by volumetric MRI acquisitions that capture wave dynamics throughout the tissue volume.^[51] Unlike surface-limited techniques, MRE offers deep penetration (up to tens of centimeters) due to the non-ionizing nature of MRI and the diffusive propagation of low-frequency shear waves, making it suitable for internal organs like the liver or brain.^[50] Tissue models in MRE distinguish between isotropic assumptions, where mechanical properties are uniform in all directions (common for homogeneous organs), and anisotropic models that account for directional variations, such as in fibrous or muscular tissues.^[51] Inversion processes derive the storage modulus, the real part of the complex shear modulus G^* = G' + iG'' (reported in kPa), representing the elastic energy storage component from observed wave speeds and attenuations.^[50] These models enable quantitative elasticity mapping while considering viscoelastic dispersion for more accurate property estimation.^[51]

Acquisition and Inversion Methods

In magnetic resonance elastography (MRE), acquisition methods rely on specialized MRI sequences designed to capture the propagation of shear waves within tissues. The most widely adopted approach uses phase-contrast gradient-echo (GRE) sequences that incorporate motion-encoding gradients (MEGs) synchronized with the mechanical driver to encode cyclic tissue displacements into the magnetic resonance phase.^[53] These MEGs are applied in multiple directions and offsets to sample the wave field comprehensively, typically acquiring data in 3D volumes for volumetric stiffness mapping.^[54] For assessing viscoelastic properties, multi-frequency acquisitions are employed, exciting tissues at frequencies ranging from 30 to 100 Hz; in liver MRE, a standard frequency of 60 Hz is used to balance wave penetration and resolution while minimizing attenuation.^[55] Inversion methods reconstruct mechanical properties from the acquired displacement data by solving the wave equation, often assuming local tissue homogeneity for initial estimates. Direct inversion techniques, such as local frequency estimation (LFE), estimate the local wave number k from the spatial frequency content of the displacement field using bandpass filtering, yielding the shear modulus via the approximation \mu \approx \rho \left( \frac{2\pi f}{k} \right)^2, where \rho is tissue density and f is the excitation frequency.^[56] This method provides rapid computation suitable for clinical workflows and has been validated in homogeneous phantoms and tissues, producing stiffness maps with values expressed in kilopascals (kPa).^[57] Advanced inversion strategies address tissue heterogeneity through 3D nonlinear inversion (3D MRE or NLI-MRE), which iteratively solves the full viscoelastic wave equation using finite element models to account for wave reflections, mode conversions, and spatial variations in stiffness.^[58] These methods improve accuracy in complex anatomies like the brain or fibrotic liver, where local assumptions fail. Recent post-2020 developments include open datasets of MRE wave images from phantoms, livers, and brains, acquired at multiple frequencies and used to train AI-based inversion models for enhanced robustness and speed.^[59] A 2025 dataset, for instance, provides 3D multifrequency data at 60 Hz for phantom and in vivo brain validation, facilitating machine learning approaches to inversion.^[59] Motion artifacts, arising from cardiac or respiratory influences, are mitigated during acquisition using navigator echoes, which monitor bulk tissue motion and enable retrospective or prospective corrections to preserve wave phase integrity.^[60] Final outputs include quantitative stiffness maps in kPa, often overlaid on anatomical images, along with confidence maps derived from inversion residuals to highlight reliable regions and exclude noisy areas.^[61]

Optical Elastography

Optical Coherence Elastography

Optical coherence elastography (OCE) is an advanced imaging technique that leverages optical coherence tomography (OCT) to quantify the biomechanical properties of soft tissues at the microscale by detecting minute displacements induced by mechanical loading. It employs phase-sensitive detection within OCT systems to measure nanometer-scale tissue deformations with high precision, enabling the mapping of elasticity in three dimensions. This method extends the capabilities of OCT beyond structural imaging to include functional assessment of tissue stiffness, which is crucial for understanding pathological changes in diseases where mechanical properties alter, such as fibrosis or tumors. The core technique of OCE involves exciting the tissue with external or internal stimuli to generate controlled deformations, followed by OCT tracking of the resulting displacements. Common excitation methods include non-contact air-puff loading, which induces surface acoustic waves for corneal assessment, and magnetic actuation using nanoparticles to create localized forces in deeper layers. Phase-resolved OCT, particularly spectral-domain variants, detects these displacements with sub-nanometer sensitivity by analyzing phase shifts in the interference signal, achieving axial resolutions of approximately 1-3 μm and lateral resolutions of 10-15 μm. This high resolution makes OCE particularly suitable for superficial tissues like the ocular structures (e.g., cornea and crystalline lens) and skin, where it has been applied to evaluate stiffness variations in conditions such as keratoconus or dermal disorders. For instance, in vivo corneal elastography using air-puff excitation has quantified Young's modulus values ranging from 577 to 1129 kPa, providing insights into biomechanical integrity post-treatment.^[62] Mechanical properties are quantified by deriving strain fields from displacement maps and applying models such as the Kelvin-Voigt framework for viscoelastic characterization. Static OCE computes Young's modulus from stress-strain relations, yielding values like 23-300 kPa in hydrated skin layers, while dynamic OCE assesses frequency-dependent responses to capture viscoelasticity, such as shear wave speeds differing by 8-14% in tissue phantoms mimicking healthy versus diseased states. Post-2020 advancements have integrated OCE with light-sheet microscopy for volumetric 3D microscale imaging of extracellular matrix remodeling, enhancing throughput and resolution for cellular-level studies in skin fibroblasts. These developments underscore OCE's advantages, including non-invasive, real-time operation (with processing times reduced to ~1 second per frame via optimized algorithms) and compatibility with clinical settings for superficial tissues.^[63] Despite these strengths, OCE is limited by the shallow optical penetration depth of near-infrared light, typically 1-2 mm in scattering tissues like skin, which restricts its use to superficial applications and necessitates complementary techniques for deeper imaging. Additionally, challenges in computational efficiency and excitation uniformity can introduce variability, though ongoing refinements in phase-stability and excitation methods continue to mitigate these issues.^[62]

Brillouin Elastography and Other Optical Techniques

Brillouin elastography, also known as Brillouin microscopy, is a label-free optical technique that probes the mechanical properties of materials through inelastic light scattering from thermally excited acoustic phonons in the gigahertz frequency range.^[64] This scattering, termed Brillouin light scattering (BLS), arises from the interaction between incident laser light and high-frequency acoustic waves, resulting in a frequency shift that encodes information about the material's hypersonic speed.^[65] The Brillouin frequency shift \Delta \nu is related to the speed of sound v by the equation \Delta \nu = \frac{2 n v}{\lambda} \sin(\theta/2), where n is the refractive index, \lambda is the wavelength of the incident light, and \theta is the scattering angle; from this, the longitudinal modulus M can be derived as M = \rho v^2, with \rho being the material density.^[64] This approach enables non-contact, non-destructive mapping of viscoelastic properties at the micron scale, distinguishing it from excitation-based methods by relying on intrinsic thermal fluctuations.^[65] Key advantages of Brillouin elastography include its label-free nature, which avoids artifacts from exogenous agents, and its high temporal resolution, capable of acquiring mechanical images in seconds to minutes for cellular samples.^[66] Recent advances from 2022 to 2025 have focused on improving acquisition speed and signal-to-noise ratios through stimulated Brillouin scattering and optimized Fabry-Pérot interferometers, enabling sub-millisecond spectral acquisition for dynamic cellular mechanics studies.^[66] For instance, innovations in high-peak-power lasers and adaptive optics have reduced light exposure while mapping elasticity in live cells, facilitating investigations into biomechanical responses during processes like fibroblast activation.^[67] However, limitations persist, including the complexity of setups requiring high-resolution spectrometers and stable laser sources, as well as high sensitivity to hydration levels, where water content can dominate the measured modulus over intrinsic stiffness.^[68] This hydration dependence arises because the longitudinal modulus is acutely responsive to fluid fractions, potentially confounding interpretations in biological tissues.^[69] Beyond Brillouin methods, other optical techniques in elastography include photoacoustic elastography, which leverages hybrid opto-acoustic wave generation to assess tissue stiffness.^[70] In this approach, pulsed laser illumination induces thermoelastic expansion, producing ultrasonic waves whose propagation and attenuation reveal viscoelastic parameters, often combined with multispectral imaging for depth-resolved mapping up to several millimeters.^[71] Optical microrheology complements these by employing particle tracking to quantify local rheology; inert tracer particles embedded in the sample undergo Brownian motion, analyzed via video microscopy or optical tweezers to compute the complex shear modulus G^*(\omega) from mean-squared displacements.^[72] Passive variants rely on thermal fluctuations, while active modes apply controlled forces, offering nanoscale sensitivity for intracellular environments without external mechanical excitation.^[73] These techniques find promising applications in biomedical research, particularly for elucidating mechanics in the tumor microenvironment, where Brillouin microscopy has revealed stiffness gradients in cancer spheroids that correlate with metastatic potential.^[74] For example, longitudinal tracking of 3D tumor models has shown evolving elasticity during growth, highlighting heterogeneous regions influenced by extracellular matrix remodeling.^[75]

Clinical Applications

Hepatic Disease Evaluation

Elastography has emerged as a cornerstone non-invasive tool for evaluating hepatic diseases, including fibrosis, cirrhosis, and steatosis, by quantifying liver stiffness and fat content to stage disease severity and guide management. In chronic liver conditions such as non-alcoholic fatty liver disease (NAFLD) and viral hepatitis, it provides quantitative metrics that correlate with histopathological findings, enabling serial monitoring without the risks of biopsy. Transient elastography (TE) serves as the primary technique due to its accessibility and widespread validation, while shear wave elastography (SWE) and magnetic resonance elastography (MRE) offer advanced options for deeper tissue assessment in complex cases.^[76]^[77] For fibrosis staging using the METAVIR system, TE measures liver stiffness in kilopascals (kPa), with values below 7 kPa typically indicating no or mild fibrosis (F0-F1), while greater than 7 kPa suggests significant fibrosis (F2 or higher); cirrhosis (F4) is often indicated by readings exceeding 12 kPa. MRE, which excels in whole-liver coverage, reports normal stiffness values of 2.0-2.5 kPa, with thresholds of 3.0-4.0 kPa signaling advanced fibrosis and over 5.0 kPa indicating cirrhosis. In steatosis assessment, TE incorporates the controlled attenuation parameter (CAP), measured in decibels per meter (dB/m), where values ≥248 dB/m indicate steatosis (S≥1), escalating to ≥290 dB/m for severe steatosis (S3).^[77] These metrics align with guidelines from the Society of Radiologists in Ultrasound (SRU), American Association for the Study of Liver Diseases (AASLD), and European Association for the Study of the Liver (EASL), which endorse elastography for initial screening and longitudinal follow-up in at-risk populations.^[78]^[79]^[80]^[76]^[81]^[77] Meta-analyses confirm elastography's diagnostic performance against liver biopsy, with TE achieving an area under the receiver operating characteristic curve (AUROC) of 0.84 for significant fibrosis, 0.89 for severe fibrosis, and 0.94 for cirrhosis, alongside sensitivities and specificities of 85-90% at optimized cutoffs. MRE demonstrates superior accuracy for advanced fibrosis (summary AUROC 0.92) and cirrhosis (0.90), particularly in obese patients where ultrasound-based methods may falter. Post-2020 advancements include integration with contrast-enhanced ultrasound (CEUS) to assess microvascular vascularity in fibrotic livers, enhancing prognostic evaluation in cirrhosis. As a non-invasive alternative to biopsy, elastography reduces procedural complications and costs, supporting risk stratification in guidelines for NAFLD and chronic hepatitis.^[79]^[82]^[83]^[76] Despite these strengths, limitations persist, as acute inflammation, elevated transaminases (>5 times upper limit of normal), or hepatic congestion can falsely elevate stiffness readings, confounding fibrosis assessment independent of steatosis or necroinflammation. Optimal results require standardized protocols, such as fasting and quality metrics (e.g., interquartile range/median ≤30% for TE), to mitigate variability across etiologies.^[76]^[79]

Oncologic Imaging

Elastography plays a significant role in oncologic imaging by assessing tissue stiffness to aid in tumor detection and characterization across various organs. In breast cancer evaluation, strain elastography and shear wave elastography (SWE) are integrated with the Breast Imaging Reporting and Data System (BI-RADS) to differentiate benign from malignant lesions. Lesions exhibiting stiffness greater than 50 kPa on SWE are considered suspicious for malignancy, with studies reporting a sensitivity of approximately 80% for this threshold when combined with conventional ultrasound.^[84]^[85] This approach enhances specificity, particularly for BI-RADS 4 lesions, by identifying softer benign masses that may avoid biopsy. Post-2020 advancements, including refined SWE protocols, have positioned ultrasound elastography as a valuable tool for early breast cancer screening, as highlighted in a 2025 review emphasizing its role in improving detection rates in dense breasts.^[86] For thyroid nodules, acoustic radiation force impulse (ARFI) elastography quantifies stiffness to stratify malignancy risk, with values exceeding 30 kPa indicating higher suspicion for malignant lesions. This cutoff helps in triaging indeterminate nodules, where stiffer tissues correlate with papillary thyroid carcinoma, achieving diagnostic accuracies that support fine-needle aspiration decisions. In prostate cancer, SWE and strain elastography detect stiff regions indicative of tumors, often combined with multiparametric MRI; for instance, SWE identifies lesions with elevated shear wave velocities greater than 3.5 m/s as potentially malignant, improving targeted biopsy yields. Other applications include cervical and ovarian cancers, where elastography assesses stromal stiffness to characterize tumors, though evidence remains emerging compared to breast and thyroid uses. Clinical evidence demonstrates that incorporating elastography reduces unnecessary biopsies by 20-30% in breast imaging without compromising cancer detection, particularly when BI-RADS categories are reclassified based on stiffness metrics. Combining elastography with contrast-enhanced ultrasound further boosts diagnostic performance, with sensitivity and specificity exceeding 85% for differentiating benign and malignant breast masses by evaluating both mechanical and vascular properties. Despite these benefits, challenges persist, such as false positives from benign stiff lesions like fibroadenomas or sclerosing adenosis in the breast, which can mimic malignancy and necessitate multimodal confirmation. Operator dependency and variability in tissue heterogeneity also limit reproducibility in some cases.^[87]^[88]^[89]

Musculoskeletal and Cardiovascular Assessment

In musculoskeletal applications, shear wave elastography (SWE) provides quantitative evaluation of tendon elasticity, enabling differentiation between healthy and pathological states. For the Achilles tendon, normal stiffness values in asymptomatic non-athletic individuals average 103.6 ± 30.5 kPa, while athletes exhibit higher means of 183.8 ± 98 kPa due to adaptive changes from loading.^[90] In tendinopathy or post-injury scenarios, SWE detects reduced elasticity, with ruptured tendons showing values as low as 72 kPa compared to healthy ranges of 261–300 kPa.^[91] This technique supports post-injury monitoring by tracking stiffness recovery during rehabilitation; for instance, surgically repaired Achilles tendons demonstrate progressive increases in elastic modulus, reaching 187.7 ± 23.8 kPa by 12 weeks postoperatively, correlating with improved mechanical integrity.^[92] Cardiovascular assessment leverages magnetic resonance elastography (MRE) to measure myocardial stiffness, a key indicator of fibrosis in non-ischemic conditions. In healthy subjects, diastolic myocardial stiffness typically ranges from 4.5 to 5.1 kPa, whereas fibrotic myocardium in hypertrophic cardiomyopathy exceeds 12 kPa, with values up to 21.8 kPa in advanced cases.^[93]^[94] Shear wave elastography complements this by estimating arterial pulse wave velocity (PWV) to gauge vascular stiffness; in the carotid artery, PWV correlates directly with shear wave speed, where elevated values above 5 m/s signal increased cardiovascular risk in normotensive populations.^[95]^[96] Emerging evidence from 2025 highlights wearable mechano-acoustic sensors for real-time elastography, offering ambulatory monitoring of tissue stiffness through surface wave dispersion analysis without external actuators.^[97] These devices enable continuous, depth-resolved stiffness mapping during daily activities, enhancing functional evaluation in degenerative conditions. A primary advantage of elastography in musculoskeletal and cardiovascular contexts is dynamic assessment during motion, allowing real-time quantification of tendon and muscle elasticity under contraction or joint movement, which improves diagnostic accuracy for functional impairments.^[91] Despite this, motion artifacts from respiration or cardiac pulsation pose limitations, often requiring breath-holding or gating techniques to minimize signal degradation in both SWE and MRE applications.^[98]

Limitations and Future Directions

Technical and Clinical Challenges

One major technical challenge in ultrasound-based elastography modalities, such as shear wave elastography (SWE), is the limited depth penetration, typically restricted to less than 10 cm due to ultrasound signal attenuation in tissue, which hinders reliable assessment of deeper structures like the liver in patients with obesity or ascites.^[2] Anisotropy in tissue structure further complicates measurements by violating the isotropic assumption underlying many elastography algorithms, leading to variations in shear wave speed estimates at tissue interfaces and potentially inaccurate stiffness quantification.^[2] Operator variability introduces additional inconsistency, particularly in strain elastography where manual compression is subjective; while coefficient of variation (CV) targets below 10% are desirable for reproducibility, actual intra-operator CV can exceed this in heterogeneous tissues due to differences in probe pressure and positioning.^[2] In magnetic resonance elastography (MRE), technical hurdles include trade-offs between spatial resolution and wave attenuation, as higher-frequency shear waves enhance resolution but decay rapidly, limiting applicability in stiff tissues like bone.^[50] Clinically, elastography measurements are confounded by physiological factors such as edema and inflammation, which elevate apparent tissue stiffness independently of fibrosis, thereby reducing specificity in acute conditions.^[99] Validation against gold standards like liver biopsy remains essential, yet discrepancies arise because elastography primarily captures composite stiffness rather than isolated pathological changes, complicating direct correlations.^[100] Reproducibility across systems poses a significant barrier, with inter-vendor differences in SWE platforms—such as those from GE and Siemens—resulting in up to 20-30% variability in liver stiffness measurements due to proprietary algorithms and hardware differences.^[101] Efforts toward standardization, including the WFUMB guidelines, emphasize consistent protocols for acquisition and interpretation to mitigate these discrepancies, recommending multiple measurements and quality criteria like CV <10% for reliable results.^[102] Safety profiles are generally favorable, with low risk from non-ionizing ultrasound, but cumulative exposure from repeated scans raises concerns for thermal effects, particularly in vulnerable populations, prompting adherence to ALARA principles.^[103]

Emerging Technologies and Advances

Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced elastography by enabling automated segmentation of tissue regions and improved dispersion imaging to assess viscosity alongside stiffness. For instance, AI-assisted ultrasound elastography models have demonstrated high efficiency in classifying breast lesions, achieving a pooled sensitivity of 90% and specificity of 88% in a 2025 meta-analysis of clinical studies using large datasets. In magnetic resonance elastography (MRE), deep learning frameworks like UNet-ResNet50 have automated liver fibrosis segmentation from 2024-2025 imaging datasets, reducing processing time while maintaining diagnostic accuracy comparable to expert radiologists. These AI integrations address previous limitations in quantitative analysis by providing real-time viscosity mapping through wave dispersion analysis, as evidenced in recent ultrasound studies. Regulatory bodies like the FDA have issued guidance on AI/ML-enabled medical devices, emphasizing validation for elastography applications to ensure safety and efficacy.^[17]^[104]^[105]^[106]^[107] Hybrid imaging techniques combining elastography with other modalities have emerged to provide multimodal insights into tissue perfusion and mechanics. Contrast-enhanced ultrasound (CEUS) integrated with elastography allows simultaneous evaluation of vascular perfusion and stiffness, improving characterization of focal liver lesions by correlating dynamic contrast patterns with mechanical properties in post-2020 studies. Photoacoustic-optical hybrids further advance this by merging optical absorption contrast with elastographic stiffness mapping, enabling high-resolution visualization of microvascular and biomechanical changes in tumors, as demonstrated in interleaved photoacoustic-ultrasound systems achieving sub-second frame rates. These hybrids, such as spherical array optoacoustic-ultrasound setups, offer enhanced specificity for oncology applications without invasive procedures.^[108]^[109]^[110] Wearable devices represent a major leap toward continuous, non-invasive monitoring, particularly with mechano-acoustic sensors introduced in 2025 for ambulatory tissue stiffness assessment. These wireless sensors generate and detect surface acoustic waves to perform elastography on skin-applied patches, enabling real-time cardiac monitoring of myocardial stiffness changes during daily activities, with demonstrated accuracy in detecting fibrosis variations. Bioadhesive ultrasound shear wave elastography wearables, developed in 2024, use acoustic radiation force to induce shear waves for portable liver and muscle evaluations, overcoming traditional scanner limitations for longitudinal studies. Such technologies facilitate home-based tracking of disease progression in cardiovascular conditions.^[97]^[111]^[112] Progress in three-dimensional (3D) and four-dimensional (4D) elastography has enabled volumetric and dynamic assessments critical for complex tissues. Volumetric MRE techniques now produce full 3D stiffness maps of organs like the liver, incorporating AI for rapid inversion of wave propagation data to quantify heterogeneity in fibrosis staging. Real-time optical coherence elastography (OCE) has advanced to 4D phase velocity mapping, capturing spatiotemporal elasticity variations in heterogeneous biological tissues at high resolution, as shown in 2020-2025 developments using asynchronous acquisition methods. These capabilities, including deep learning-enhanced 4D OCE at 831 Hz volumes, support intraoperative guidance and personalized biomechanics research.^[105]^[113]^[114] Looking ahead, portable elastography devices are poised for widespread adoption, driven by ongoing regulatory approvals and market expansion. Compact ultrasound and MRE systems, cleared by bodies like the FDA for point-of-care use, promise broader accessibility in remote settings. The global elastography imaging market is projected to reach approximately $7.5 billion by 2032, fueled by these innovations and increasing demand for non-invasive diagnostics in chronic diseases.^[115]^[116]

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WFUMB Guideline/Guidance on Liver Multiparametric Ultrasound
The guidelines are clinically oriented, and the role of shear wave elastography in both fibrosis staging and prognostication in different etiologies of liver ...Missing: integration | Show results with:integration
[100]
Overview of Diagnostic Ultrasound Safety - Wiley Online Library
Oct 8, 2025 · This article provides a comprehensive, organ-, and modality-specific review of current guidelines for ultrasound exposure parameters, including ...
[101]
Bias in artificial intelligence for medical imaging - PubMed Central
AI in medical imaging is at risk of being compromised by several types of biases, which could adversely affect patient outcomes. • Understanding that medical ...Missing: elastography | Show results with:elastography
[102]
Diagnostic accuracy of artificial intelligence models for imaging ...
Oct 2, 2025 · AI-based diagnostic models showed excellent performance, with pooled sensitivity of 91% (95% CI: 84–95%), specificity of 92% (95% CI: 86–96%), ...
[103]
Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d ...
Oct 20, 2025 · The aim of this study is to use image segmentation technology for the computational interpretation of MRE, enabling AI to automatically segment ...
[104]
Deep learning in ultrasound elastography imaging: A review
Aug 9, 2025 · Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi‐ ...
[105]
[PDF] Artificial Intelligence in Ultrasound Imaging: A Novel Perspective
... 3D tissue strain and strain distribution maps. Hybrids of elastography and shear wave elastography have been developed by AI and offer exquisite lesion ...
[106]
Hybrid photoacoustic and fast super-resolution ultrasound imaging
Here we developed an interleaved PA/fast ULM imaging technique that enables super-resolution vascular and physiological imaging in less than 2 seconds per frame ...
[107]
Hybrid spherical array for combined volumetric optoacoustic and B ...
Mar 15, 2024 · We present a new, to our knowledge, hybrid optoacoustic ultrasound (OPUS) imaging approach employing a spherical array with dedicated segments for each ...
[108]
Wireless, wearable elastography via mechano-acoustic wave ... - NIH
Sep 3, 2025 · Wireless, wearable elastography via mechano-acoustic wave sensing for ambulatory monitoring of tissue stiffness ... Received 2025 Apr 9; Accepted ...Missing: prototypes | Show results with:prototypes
[109]
Wearable bioadhesive ultrasound shear wave elastography - Science
Feb 9, 2024 · We introduce wearable bioadhesive ultrasound elastography (BAUS-E), which can generate acoustic radiation force impulse (ARFI) to induce shear waves.
[110]
Four-dimensional (4D) phase velocity optical coherence ... - NIH
Here we propose a method to evaluate phase velocity, wave velocity at various frequencies, in four-dimensional space (x, y, z, f), called 4D-OCE phase velocity.
[111]
4D deep learning for real-time volumetric optical coherence ...
We propose a new approach for elastography estimations using a fast imaging device to acquire small image volumes at rates of 831 Hz.Missing: advances | Show results with:advances
[112]
Elastography Imaging Market Trends & Forecast to 2032
Aug 21, 2025 · The global elastography imaging market is projected to grow from US$ 4.6 Bn in 2025 to US$ 7.5 Bn by 2032, recording a CAGR of 7.2% during ...
[113]
Ultrasound Tech Trends Every Sonographer Should Know
Jun 16, 2025 · Emerging ultrasound trends include 3D/4D imaging for detailed visuals, AI for enhanced diagnostics, and portable devices for remote imaging.