On System-Dependent Sources of Uncertainty and Bias in Ultrasonic Quantitative Shear-Wave Imaging

Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography

BACKGROUND

Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal-to-noise ratio (SNR) can be very low.

PURPOSE

The purpose of this study is to develop a deep learning approach for denoising shear wavefields in ultrasound shear wave elastography. This may lead to improved reconstruction of shear wave phase velocity image maps.

METHODS

The study addresses noise by transforming particle velocity data into a time-frequency representation. A neural network with encoder and decoder convolutional blocks effectively decomposes the input and extracts the signal of interest, improving the SNR in high-noise scenarios. The network is trained on simulated phantoms with elasticity values ranging from 3  to 60 kPa. A total of 1 85 570 samples with 80%-20 % $\%$ split were used for training and validation. The approach is tested on experimental phantom and ex-vivo goat liver tissue data. Performance was compared with the traditional filtering methods such as bandpass, median, and wavelet filtering. Kruskal-Wallis one-way analysis of variance was performed to check statistical significance. Multiple comparisons were performed using the Mann-Whitney U test and Holm-Bonferroni adjustment ofp - values $p-{\rm values}$ .

RESULTS

The results are evaluated using SNR and the percentage of pixels that can be reconstructed in the phase velocity maps. The SNR levels in experimental data improved from -2 to 9.9 dB levels to 15.6 to 30.3 dB levels. Kruskal-Wallis one-way analysis showed statistical significance (p < 0.05 $p<0.05$ ). Multiple comparisons with p-value corrections also showed statistically significant improvement when compared to the bandpass and wavelet filtering scheme (p < 0.05 $p<0.05$ ). Smoother phase velocity maps were reconstructed after denoising. The coefficient of variation is less than5 % $5\%$ in CIRS phantom and less than18 % $18\%$ in ex-vivo goat liver tissue.

CONCLUSIONS

The proposed approach demonstrates improvement in shear wave phase velocity image map reconstruction and holds promise that deep learning methods can be effectively utilized to extract true shear wave signal from measured noisy data.

Quality Assurance of Point and 2D Shear Wave Elastography through the Establishment of Baseline Data Using Phantoms

Ultrasound elastography has been available on most modern systems; however, the implementation of quality processes tends to be ad hoc. It is essential for a medical physicist to benchmark elastography measurements on each system and track them over time, especially after major software upgrades or repairs. This study aims to establish baseline data using phantoms and monitor them for quality assurance in elastography. In this paper, we utilized two phantoms: a set of cylinders, each with a composite material with varying Young's moduli, and an anthropomorphic abdominal phantom containing a liver modeled to represent early-stage fibrosis. These phantoms were imaged using three ultrasound manufacturers' elastography functions with either point or 2D elastography. The abdominal phantom was also imaged using magnetic resonance elastography (MRE) as it is recognized as the non-invasive gold standard for staging liver fibrosis. The scaling factor was determined based on the data acquired using MR and US elastography from the same vendor. The ultrasound elastography measurements showed inconsistency between different manufacturers, but within the same manufacturer, the measurements showed high repeatability. In conclusion, we have established baseline data for quality assurance procedures and specified the criteria for the acceptable range in liver fibrosis phantoms during routine testing.

Probing Tissue Viscoelasticity With STL Ultrasound Shearwave Spectroscopy Using Cole-Cole Diagrams

OBJECTIVE

Viscoelasticity is mapped by dispersion in shearwave elastography. Incomplete spectral information of shearwaves is therefore used to estimate mechanical stiffness. We propose capturing the "full-waveform-information" of the shear wave spectra to better resolve complex shear modulus μ* (ω). Approach is validated on phantom models, animal tissues, and feasibility demonstrated on human post-delivery placenta.

METHODS

We captured robust estimates of μ* in ex-vivo livers subjected to water bath ablation, glutaraldehyde exposure and in the placenta.

RESULTS

Complex modulus at 200 Hz is more reflective of tissue stiffness than cross-correlation estimate. Bias increased in phantoms with higher gelatin (G) (0.65: 6% G) and oil (O) (0.58: 6% G and 40% O) concentration, compared to elastic phantoms with low stiffness (0.33: 3% G). Actual tissues also reported higher bias in cross-correlation estimate (rabbit liver: 0.61, porcine liver: 2.20, and human placenta: 0.63). Stiffness is sensitive to ablation temperature, where the overall modulus changed from 3.02 KPa at 16 °C to 2.75 KPa at 56 °C in water bath. With exposure to Glutaraldehyde, the overall modulus increased from 2.37 to 9.03 KPa. Reconstruction errors in the loss modulus decreased by 68% with the power law compared to a Maxwell model in porcine livers with Cole-Cole inverse fitting.

CONCLUSION

Omitting Shear wave attenuation leads to bias. Reconstruction of rheological response with a model is sensitive to its architecture and also the framework.

SIGNIFICANCE

We use "full spectral information" in ultrasound shear wave elastography to better map μ*(ω) changes in viscoelastic tissues.

Shear Wave Elastography Implementation on a Portable Research Ultrasound System: Initial Results

Ultrasound shear wave elastography (SWE) has emerged as a promising technique that enables the quantitative estimation of soft tissue stiffness. However, its practical implementation is complicated and presents a number of engineering challenges, including high-energy burst transmission, high-frame rate data acquisition and high computational requirements to process huge datasets. Therefore, to date, SWE has only been available for high-end commercial systems or bulk and expensive research platforms. In this work, we present a low-cost, portable and fully configurable 256-channel research system that is able to implement various SWE techniques. We evaluated its transmit capabilities using various push beam patterns and developed algorithms for the reconstruction of tissue stiffness maps. Three different push beam generation methods were evaluated in both homogeneous and heterogeneous experiments using an industry-standard elastography phantom. The results showed that it is possible to implement the SWE modality using a portable and cost-optimized system without significant image quality losses.

Quantitative Estimation of Mechanical Anisotropy Using Acoustic Radiation Force (ARF)-Induced Peak Displacements (PD): In Silico and Experimental Demonstration

Elastic degree of anisotropy (DoA) is a diagnostically relevant biomarker in muscle, kidney, breast, and other organs. Previously, elastic DoA was qualitatively assessed as the ratio of peak displacements (PD) achieved with the long-axis of a spatially asymmetric Acoustic Radiation Force Impulse (ARFI) excitation point spread function (PSF) aligned along versus across the axis of symmetry (AoS) in transversely isotropic materials. However, to better enable longitudinal and cross-sectional analyses, a quantitative measure of elastic DoA is desirable. In this study, qualitative ARFI PD ratios are converted to quantitative DoA, measured as the ratio of longitudinal over transverse shear elastic moduli, using a model empirically derived from Field II and finite element method (FEM) simulations. In silico, the median absolute percent error (MAPE) in ARFI-derived shear moduli ratio (SMR) was 1.75%, and predicted SMRs were robust to variations in transverse shear modulus, Young's moduli ratio, speed of sound, attenuation, density, and ARFI excitation PSF dimension. Further, ARFI-derived SMRs distinguished two materials when the true SMRs of the compared materials differed by as little as 10%. Experimentally, ARFI-derived SMRs linearly correlated with the corresponding ratios measured by Shear Wave Elasticity Imaging (SWEI) in excised pig skeletal muscle ( [Formula: see text], MAPE = 13%) and in pig kidney, in vivo ( [Formula: see text], MAPE = 5.3%). These results demonstrate the feasibility of using the ARFI PD to quantify elastic DoA in biological tissues.

Time-Aligned Plane Wave Compounding Methods for High-Frame-Rate Shear Wave Elastography: Experimental Validation and Performance Assessment on Tissue Phantoms

Shear wave elastography (SWE) is an ultrasonic technique able to quantitatively assess the mechanical properties of tissues by combining acoustic radiation force and ultrafast imaging. While utilizing coherent plane wave compounding enhances echo and shear wave motion signal-to-noise ratio (SNR), it also reduces the effective pulse repetition frequency (PRF_e), affecting the accuracy of the measurements of motion and, consequently, of material properties. It is important to maintain both high-motion SNR and PRF_e, particularly for the characterization of (material and/or geometrical) dispersive tissues such as arteries. This work proposes a method for SWE measurements with high SNR, while maintaining a high PRF_e, using conventional clinical ultrasound scanners. A time alignment process is applied after acquiring data from plane wave transmissions at different angles. The time alignment uses interpolation to obtain data points at higher frame rates, and the time-aligned data are compounded to increase the SNR. The method is used for SWE in tissue-mimicking phantoms of different stiffness and is compared with traditional plane wave compounding. Increases of 58% and 36% in spatial and temporal bandwidth compared with conventional plane wave compounding, respectively, can be achieved for SWE measurements of representative arterial stiffness values. Improvements in phase velocity accuracy and bandwidth in an arterial phantom are also described, to emphasize the beneficial advantage in dispersive cases.

Main Uncertainties in the RF Ultrasound Scanning Simulation of the Standard Ultrasound Phantoms

Ultrasound echoscopy technologies are continuously evolving towards new modalities including quantitative parameter imaging, elastography, 3D scanning, and others. The development and analysis of new methods and algorithms require an adequate digital simulation of radiofrequency (RF) signal transformations. The purpose of this paper is the quantitative evaluation of RF signal simulation uncertainties in resolution and contrast reproduction with the model of a phased array transducer. The method is based on three types of standard physical phantoms. Digital 3D models of those phantoms are composed of point scatterers representing the weak backscattering of the background material and stronger backscattering from inclusions. The simulation results of echoscopy with sector scanning transducer by Field II software are compared with the RF output of the Ultrasonix scanner after scanning standard phantoms with 2.5 MHz phased array. The quantitative comparison of axial, lateral, and elevation resolutions have shown uncertainties from 9 to 22% correspondingly. The echoscopy simulation with two densities of scatterers is compared with contrast phantom imaging on the backscattered RF signals and B-scan reconstructed image, showing that the main sources of uncertainties limiting the echoscopy RF signal simulation adequacy are an insufficient knowledge of the scanner and phantom’s parameters. The attempt made for the quantitative evaluation of simulation uncertainties shows both problems and the potential of echoscopy simulation in imaging technology developments. The analysis presented could be interesting for researchers developing quantitative ultrasound imaging and elastography technologies looking for simulated raw RF signals comparable to those obtained from real ultrasonic scanning.

Viscoelastic parameter estimation using simulated shear wave motion and convolutional neural networks

Ultrasound shear wave elastography (SWE) techniques have been very useful for the analysis of tissue rheological properties, but there are still obstacles for robust evaluation of viscoelastic tissue properties. In this proof-of-concept study, we investigate whether convolutional neural networks (CNN) are capable of retrieving the elasticity and viscosity parameters from simulated shear wave motion images. Staggered-grid finite difference simulations based on a Kelvin-Voigt rheological model were used to generate data for this study. The wave motion datasets were created using Kelvin-Voigt shear elasticity values ranging from 1 to 25 kPa, shear viscosities ranging from 0 to 10 Pa⋅s, and two different push profiles using f-numbers of 1 and 2. The CNN architectures, optimized using mean squared error loss, were then trained to retrieve a specific viscoelastic parameter. Both elasticity and viscosity values were successfully retrieved, with regression R² values above 0.99 when correlating the estimated mechanical properties versus the true mechanical properties. The CNN performance was also compared to estimation of shear elasticity and viscosity from fitting dispersion curves estimated from two-dimensional Fourier transform analysis. The results demonstrated that the CNN models were robust to noise, vertical position and partially to f-number. The architecture was proven to be robust to multiple push profiles if trained properly. The CNN results showed higher accuracy over the full viscoelastic parameter range compared to the Fourier-based analysis. The overall results showed the CNNs' potential to be an alternative to complex mathematical analyses such as Fourier analysis and dispersion curve estimation used currently for shear wave viscoelastic parameter estimation.

Radiological Society of North America/Quantitative Imaging Biomarker Alliance Shear Wave Speed Bias Quantification in Elastic and Viscoelastic Phantoms

OBJECTIVES

To quantify the bias of shear wave speed (SWS) measurements between different commercial ultrasonic shear elasticity systems and a magnetic resonance elastography (MRE) system in elastic and viscoelastic phantoms.

METHODS

Two elastic phantoms, representing healthy through fibrotic liver, were measured with 5 different ultrasound platforms, and 3 viscoelastic phantoms, representing healthy through fibrotic liver tissue, were measured with 12 different ultrasound platforms. Measurements were performed with different systems at different sites, at 3 focal depths, and with different appraisers. The SWS bias across the systems was quantified as a function of the system, site, focal depth, and appraiser. A single MRE research system was also used to characterize these phantoms using discrete frequencies from 60 to 500 Hz.

RESULTS

The SWS from different systems had mean difference 95% confidence intervals of ±0.145 m/s (±9.6%) across both elastic phantoms and ± 0.340 m/s (±15.3%) across the viscoelastic phantoms. The focal depth and appraiser were less significant sources of SWS variability than the system and site. Magnetic resonance elastography best matched the ultrasonic SWS in the viscoelastic phantoms using a 140 Hz source but had a - 0.27 ± 0.027-m/s (-12.2% ± 1.2%) bias when using the clinically implemented 60-Hz vibration source.

CONCLUSIONS

Shear wave speed reconstruction across different manufacturer systems is more consistent in elastic than viscoelastic phantoms, with a mean difference bias of < ±10% in all cases. Magnetic resonance elastographic measurements in the elastic and viscoelastic phantoms best match the ultrasound systems with a 140-Hz excitation but have a significant negative bias operating at 60 Hz. This study establishes a foundation for meaningful comparison of SWS measurements made with different platforms.

A review of physical and engineering factors potentially affecting shear wave elastography

It has been recognized that tissue stiffness provides useful diagnostic information, as with palpation as a screening for diseases such as cancer. In recent years, shear wave elastography (SWE), a technique for evaluating and imaging tissue elasticity quantitatively and objectively in diagnostic imaging, has been put into practical use, and the amount of clinical knowledge about SWE has increased. In addition, some guidelines and review papers regarding technology and clinical applications have been published, and the status as a diagnostic technology is in the process of being established. However, there are still unclear points about the interpretation of shear wave speed (SWS) and converted elastic modulus in SWE. To clarify these, it is important to investigate the factors that affect the SWS and elastic modulus. Therefore, physical and engineering factors that potentially affect the SWS and elastic modulus are discussed in this review paper, based on the principles of SWE and a literature review. The physical factors include the propagation properties of shear waves, mechanical properties (viscoelasticity, nonlinearity, and anisotropy), and size and shape of target tissues. The engineering factors include the region of interest depth and signal processing. The aim of this review paper is not to provide an answer to the interpretation of SWS. It is to provide information for readers to formulate and verify the hypothesis for the interpretation. Therefore, methods to verify the hypothesis for the interpretation are also reviewed. Finally, studies on the safety of SWE are discussed.

Which Confounders Have the Largest Impact in Shear Wave Elastography of Muscle and How Can They be Minimized? An Elasticity Phantom, Ex Vivo Porcine Muscle and Volunteer Study Using a Commercially Available System

The goal of the study was to investigate the quantitative impact of region of interest (ROI), software choice, muscle fiber orientation and preload tension on shear wave velocity (SWV). First, SWV was assessed in an isotropic elasticity phantom and ex vivo porcine muscle using a commercially available clinical ultrasound system. Secondly, SWV was acquired in relaxed and stretched calf muscles of healthy volunteers (dorsal extension of the talocrural joint), for both parallel and transverse probe direction to the fibers, as well as for different ROIs and software versions. The effect of intermediate probe-fiber alignments was also analyzed. Finally, the impact of confounding factors on SWV reproducibility was minimized with a second force-controlled volunteer study, in which the calf was isometrically loaded, and fiber orientation and ROI were well-defined. 2046 in vivoSWE images were acquired to analyze SWV reproducibility with different confounder settings. In healthy volunteers, the main variance-contributing factors were in order of importance muscle tension, fiber orientation, horizontal ROI size and insertion depth. Regression analysis showed significantly reduced SWV with increasing insertion depth for each study material. Parallel probe-fiber orientation, muscle stretch and increasing horizontal ROI size led to significantly higher SWV. Based on the results of the study, we provide recommendations to minimize the impact of confounders in musculoskeletal elastography and discuss the main confounding mechanisms and trade-offs between confounding variables. Coefficients of variation can be significantly reduced with a controlled protocol, if the confounders are clinically taken into account.

Evaluation of Reconstruction Parameters for 2-D Comb-Push Ultrasound Shear Wave Elastography

Shear wave elastography (SWE) is a noninvasive ultrasound imaging modality used in the assessment of the mechanical properties of tissues such as the liver, kidney, skeletal muscle, thyroid, and the breast. Among the methods used to perform SWE is the comb-push ultrasound shear elastography method. This method uses multiple focused ultrasound beams to generate push beams with acoustic radiation force. Applying these push beams generates propagating shear waves. The propagation motion is measured with ultrafast ultrasound imaging. The shear wave motion data are directionally filtered, and a 2-D shear wave velocity (SWV) algorithm is applied to create group velocity maps. This algorithm uses a moving window and a specified patch for performing cross-correlations of time-domain signals. We performed a parametric study of how the choice of the patch and window size affected the reconstruction of the SWV in homogeneous and inclusion phantoms. We quantified the mean velocity and coefficient of variation in the homogeneous phantoms. We measured the contrast-to-noise ratio and bias in the inclusion phantoms. In each of these cases, we found that particular combinations of the patch and window provided optimal values of these evaluation metrics for the phantoms tested. This study provides a basis to construct algorithms to produce optimal SWV reconstructions for various clinical applications.

Plane-Wave Imaging Improves Single-Track Location Shear Wave Elasticity Imaging

Single-track location shear wave elasticity imaging (STL-SWEI) is immune to speckle bias, but the quality of the images is depth dependent. We hypothesize that plane-wave imaging can reduce the depth dependence of STL-SWEI. To test this hypothesis, we developed a novel technique known as plane-wave STL-SWEI (pSTL-SWEI). To evaluate the pSTL-SWEI's potential, we performed studies on phantoms and excised murine pancreatic tumors. The mean shear wave speeds measured with STL-SWEI and pSTL-SWEI were similar. However, the elastographic signal-to-noise ratio (SNR_e) of pSTL-SWEI elastograms was noticeably higher than that produced with STL-SWEI. Specifically, we observed an improvement in SNR_e ranging from 39.9%-55.1%, depending on tissue stiffness. The spatial resolution of pSTL-SWEI elastograms was 2.7%-12.1% lower than that produced with STL-SWEI. pSTL-SWEI elastograms displayed higher contrast-to-noise ratio (CNR_e) than those produced with STL-SWEI, especially when imaging was performed with low push pulse intensities and low pulse durations.

Fourier-Domain Shift Matching: A Robust Time-of-Flight Approach for Shear Wave Speed Estimation

Our primary objective of this work was to design and test a new time-of-flight (TOF) method that allows measurements of shear wave speed (SWS) following impulsive excitation in soft tissues. Particularly, under the assumption of the local plane shear wave, this work named the Fourier-domain shift matching (FDSM) method, estimates SWS by aligning a series of shear waveforms either temporally or spatially using a solution space deduced by characteristic curves of the well-known 1-D wave equation. The proposed SWS estimation method was tested using computer-simulated data, and tissue-mimicking phantom and ex vivo tissue experiments. Its performance was then compared with three other known TOF methods: lateral time-to-peak (TTP) method with robust random sampling consensus (RANSAC) fitting method, Radon sum transformation method, and a modified cross correlation method. Hereafter, these three TOF methods are referred to as the TTP-RANSAC, Radon sum, and X-corr methods, respectively. In addition to an adapted form of the 2-D Fourier transform (2-D FT)-based method in which the (group) SWS was approximated by averaging phase SWS values was considered for comparison. Based on data evaluated, we found that the overall performance of the above-mentioned temporal implementation of the proposed FDSM method was most similar to the established Radon sum method (correlation = 0.99, scale factor = 1.03, and mean difference = 0.07 m/s), and the 2-D FT (correlation = 0.98, scale factor = 1.00, and mean difference = 0.10 m/s) at high signal quality. However, results obtained from the 2-D FT method diverged (correlation = 0.201) from these of the proposed temporal implementation in the presence of diminished signal quality, whereas the agreement between the Radon sum approach and the proposed temporal implementation largely remained the same (correlation = 0.98).

Determination of Normal Skin Elasticity by Using Real-time Shear Wave Elastography

OBJECTIVES

To define the reference ranges of normal skin elasticity measurements associated with shear wave elastography (SWE) in healthy volunteers and analyze the factors that may affect SWE.

METHODS

Mean skin thickness and elastic modulus values from 90 healthy volunteers were evaluated with B-mode ultrasonography and SWE in the right fingers and forearms, anterior chest, and abdominal walls. Reference ranges of normal skin elasticity were calculated by using lower and upper limits at the 2.5th and 97.5th percentiles. To investigate the effects of potential factors (site, sex, age, body mass index, and skin thickness) on skin elasticity measurements, a 1-way analysis of variance, the Student t test, and the Pearson correlation test were performed.

RESULTS

Skin elasticity was significantly different at different sites (P < .05). Mean elastic modulus values were 30.3 kPa for the finger, 14.8 kPa for the forearm, 17.8 kPa for the chest wall, and 9.5 kPa for the abdominal wall, and reference ranges of normal skin elasticity were 12.1 to 48.4kPa for the finger, 3.5 to 26.0 kPa for the forearm, 6.6 to 28.9 kPa for the chest wall, and 3.5 to 15.5 kPa for the abdominal wall. Our study revealed that men had higher skin elasticity measurements than women (P < .05), and they were more elevated in participants aged 20 to 50 years than in the other groups at the finger (P < .05). The body mass index and skin thickness had a negligible impact on skin elasticity measurements (P > .05).

CONCLUSIONS

This study revealed that the site, sex, and age should be taken into account when determining the reference ranges of normal skin elasticity by skin elasticity measurements.

Current status of musculoskeletal application of shear wave elastography

Ultrasonography (US) is a very powerful diagnostic modality for the musculoskeletal system due to the ability to perform real-time dynamic high-resolution examinations with the Doppler technique. In addition to acquiring morphologic data, we can now obtain biomechanical information by quantifying the elasticity of the musculoskeletal structures with US elastography. The earlier diagnosis of degeneration and the ability to perform follow-up evaluations of healing and the effects of treatment are possible. US elastography enables a transition from US-based inspection to US-based palpation in order to diagnose the characteristics of tissue. Shear wave elastography is considered the most suitable type of US elastography for the musculoskeletal system. It is widely used for tendons, ligaments, and muscles. It is important to understand practice guidelines in order to enhance reproducibility. Incorporating viscoelasticity and overcoming inconsistencies among manufacturers are future tasks for improving the capabilities of US elastography.

Ultrasonic Shear Wave Elasticity Imaging Sequencing and Data Processing Using a Verasonics Research Scanner

Ultrasound elasticity imaging has been developed over the last decade to estimate tissue stiffness. Shear wave elasticity imaging (SWEI) quantifies tissue stiffness by measuring the speed of propagating shear waves following acoustic radiation force excitation. This paper presents the sequencing and data processing protocols of SWEI using a Verasonics system. The selection of the sequence parameters in a Verasonics programming script is discussed in detail. The data processing pipeline to calculate group shear wave speed (SWS), including tissue motion estimation, data filtering, and SWS estimation, is demonstrated. In addition, the procedures for calibration of beam position, scanner timing, and transducer face heating are provided to avoid SWS measurement bias and transducer damage.

Strain sonoelastography of inflammatory myopathies: comparison with clinical examination, magnetic resonance imaging and pathologic findings

OBJECTIVE

To evaluate strain sonoelastography (SSE) in patients with inflammatory myopathies (IM) compared with clinical examination, MRI and pathologic findings.

METHODS

18 lesions from 17 consecutive patients with IM (5 males and 12 females; mean age, 41.2 years; range, 11-67 years) were assessed with SSE after MRI. The ratio of strain in the target muscle (A) and a nearby normal muscle (B), defined as the strain index value (SR) (B/A), was calculated automatically. Elastograms were assigned an elasticity score according to the degree and distribution of strain induced by manual compression. Ultrasonography and MRI were analyzed in conjunction with clinical information, biochemical data, final clinical diagnosis and grading of pathology. Correlations between SR and qualitative analyses of MRI and ultrasonography, elasticity score, biochemical data and final clinical diagnosis were analyzed using Pearson's correlation coefficient.

RESULTS

The SR of the target muscles was high in patients with IM (mean 3.14; range, 0.95-5.93 ± 1.42). The correlations between SR and pathologic grading and elasticity score were statistically significant (p < 0.05). There was no significant agreement between SR and other clinical and radiologic parameters.

CONCLUSION

Muscle hardness, as semi-quantitatively measured by SSE, was increased in cases of IM. The correlation between the SR and the pathologic grading suggests that SSE could be an important tool in not only the diagnosis of but also in measuring the degree of muscular inflammation.

ADVANCES IN KNOWLEDGE

This work describes a correlation between tissue elasticity and pathology in IM.