ª Springer Science+Business Media New York (outside the USA) 2015
Abdominal Imaging
Abdom Imaging (2015) DOI: 10.1007/s00261-014-0305-8
Elastography: history, principles, and technique comparison Brian S. Garra1,2 1 2
Washington DC VA Medical Center, Washington, DC, USA US Food and Drug Administration, Silver Spring, MD, USA
Abstract Elastography is a relatively new imaging technology that creates images of tissue stiffness. It can be thought of an extension of the ancient technique of palpation but it gives better spatial localization information and is less subjective. Two main types of elastography are currently in use, strain elastography where the tissue displacement in response to gentle pressure is used to compute and image tissue strain, and shear wave elastography where the speed of shear waves traversing tissue is measured and used to create an image of tissue stiffness. Each method has advantages and disadvantages but generally strain imaging is excellent for focal lesions and shear wave imaging, being more quantitative, is best for diffuse organ diseases. Strain imaging requires additional training in acquisition technique to obtain high quality images. Pitfalls to avoid and tips for good images are provided. Improvements in strain imaging are focused on better quality indicators and better methods for quantification. Improvements in shear wave imaging will be higher frame rates, greater accuracy in focal lesions, and making results more comparable between different ultrasound systems. Both methods will continue to improve and will provide ever more powerful new tools for diagnosis of diffuse and focal diseases. Key words: Elastography—Elasticity—Shear wave—Ultrasound—Breast—Thyroid—Lymph node
Historical background Hardness/stiffness is a key property of abnormal tissues and organs. The value of assessing this property has been known since the earliest days of civilization. Writings in the Edwin Smith papyrus [1] concerning the use of palpation as a diagnostic tool have been ascribed to physi-
Correspondence to: Brian S. Garra; email: [email protected], [email protected]
cian, priest and architect Imhotep who lived between 3000 and 2500 BCE [2]. Texts from China, particularly the 18 volume Huangdi Neijing which dates from 475 to 221 BCE [3] (and was based on much older practices) mention palpation as a key diagnostic method. Both benign and cancerous masses are well known to be firmer than surrounding tissues and hardness along with lack of lesion mobility are classic signs of malignancy [4]. Soft tissue imaging methods such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) have largely replaced palpation for detection and characterization of masses and for determination of organ sizes. In many cases however, these methods have not been able to distinguish the various types of masses from one another and for this reason, clinicians have resorted advanced imaging methods such as dynamic contrast enhancement, ultrasound contrast agents and MRI apparent diffusion coefficient mapping, with considerable success. These methods are relatively costly and generally require high end imaging equipment and considerable expertise to perform and interpret, all factors that limit their availability. The advent of both qualitative and quantitative image of tissue hardness offers a new way to help distinguish masses and diffuse diseases from one another.
Tissue stiffness imaging using b-mode grayscale ultrasound: Palpation remains a pillar of modern physical diagnosis, but it is very subjective and dependent on the skill and experience of the examiner. For this reason, more accurate and reproducible methods of assessing tissue hardness have been sought. Gray scale ultrasound imaging is a readily available method that may be used for assessment of tissue stiffness. main methods have long been available but are not widely known or used: (a) lesion compression, (b) echopalpation and (c) fremitus. Compression ultrasound is a very simple procedure, intended for focal lesions or masses. One centers the transducer over the lesion and compresses. The amount
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 1. Compression ultrasound of subcutaneous lesion. Precompression image on the left and post compression image on the right. The subcutaneous fat layer compresses from 1.37 to 0.82 cm (40%) whereas the lesion
decreases in diameter from .91 to .77 cm (15%) during compression suggesting that the lesion is harder than the adjacent fat. This was confirmed at real time compression examination.
of flattening that the lesion undergoes is compared to that of the adjacent known tissue (Fig. 1). This gives an estimate of the relative hardness of the lesion compared to the adjacent tissue. This method is subjective and can be used to find an Isoechoic lesion such as a breast tumor in fat where fat lobules may look like masses. The method has also been used in a quantitative way to distinguish cancers from benign tumors such as fibroadenomas [5]. Echopalpation is similar to compression except the lesion or mass is compressed with a finger or wire held in the ultrasound beam while watching with real time ultrasound. The finger or wire is slid through a gel standoff allowing the operator to feel the lesion and watch what is beneath the finger or wire, all at the same time. This method allows the operator to assess local compressibility and also to precisely determine on the image where the palpable edge of the lesion is. This is an excellent method for correlating ultrasound images with palpation. In general, stiff lesions move downward as a unit in response to compression and soft lesions compress like a sponge (Fig. 2). Fremitus is a technique for watching vibrations in tissue induced by the patient humming. While the patient is phonating, vibrations are transmitted from the larynx to the remainder of the body where they can be observed using color and power Doppler. Stiff lesions vibrate less than softer tissues and become visible as a dark area surrounded by color (Fig. 3). With increased sensitivity of color and power Doppler instruments, this method
had significant potential, but never became widespread because of problems with inconsistent display of lesions due in part to differences in the pitch of phonation, attenuation of the vibrations, and variability in the intensity of phonation.
History of elastographic imaging True ultrasonic tissue stiffness assessment grew out of tissue motion studies performed principally in England during the 1980s [6, 7]. These methods used data from the m-mode scan to track movement. By 1988 researchers at the University of Rochester had developed a system that used modified color Doppler to track tissue movement and make tissue stiffness based images. The method, called sonoelasticity imaging [8, 9] was able to image stiff lesions in the prostate gland and other organs as dark areas against a green background of moving tissue. As with vibrational Doppler imaging (a related technique) the lesion appears dark against a color background of vibrating tissue (Fig. 4) but the image is relatively low resolution and requires an inconvenient external vibratory device to induce the tissue motion. More recently, the application of a second vibration source operating at a slightly different frequency, was found to produce a shifting interference pattern termed ‘‘crawling waves’’ that could be used to estimate local shear wave velocity and tissue stiffness [10]. The first widely successful means of imaging tissue elasticity was elastography, now known as strain elas-
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 3. Fremitus. Having the patient hum produces vibrations in tissues that can be imaged using Color Doppler. The stiff lesion vibrates less than the softer surrounding tissue and appears as a dark area in the color image.
Fig. 2. Echopalpation. Localized deformity of the skin and mass just below the skin is seen as the paperclip is dragged across the lesion, A at the left hand end of the lesion (arrow), B at the middle of the lesion (arrow), and C at the right hand margin of the lesion. The local deformity of the lesion caused by the paperclip suggests that it is a relatively soft mass.
tography, first reported in 1991 by Cespedes and Ophir [11]. This method uses the response of tissue to a gentle compression by the ultrasound transducer to generate an image of relative tissue stiffness. High quality strain images of tissue hardness quickly became possible with this technology with the first applications being in detection of prostate cancer and characterization of breast masses as benign or malignant [12]. Strain elas-
Fig. 4. Sonoelasticity image of liver post radio-frequency ablation. The ablated tissue is much stiffer than the surrounding tissue and vibrates less producing the dark area (outlined in pink and blue) on the image. Image from www.rochester.edu website courtesy of Diane Dalecki and Deborah Rubens.
tography is now a relatively mature technology with most manufacturers providing this capability on at least some of their US systems. Strain elastography was soon followed by the development of shear wave elastography in which the speed at which shear waves traverse tissue is used to generate an image of tissue stiffness. One of the first developments in this field was a non-imaging device known as the FibroscanTM which uses shear wave speed
B.S. Garra: Elastography: history, principles, and technique comparison
techniques published in 2003 [13] to estimate liver stiffness. Imaging devices using shear wave speed soon followed. Shear wave elastography (which can be performed using ultrasound or MRI) has rapidly become popular because obtaining good images is relatively easy and the images of shear wave speed relate directly to the shear and elastic moduli of tissue, not relative stiffness as in strain elastography.
Methods of elastographic imaging As noted above, many methods for imaging tissue stiffness have been demonstrated but currently two main approaches have become dominant, those generating strain images and those generating shear wave speed images. Strain elastography involves one or more gradual compressions of tissue and does not monitor or image any vibrations or waves in the tissue. For this reason it is termed a ‘‘static’’ or ‘‘quasistatic’’ technique whereas shear wave elastography and sonoelastography create images based on moving waves and are thus termed ‘‘dynamic’’ techniques. These terms are often used in the literature to classify elastographic methods.
Strain (static) elastography In strain elastography, radio-frequency (RF) ultrasound signals are acquired before and after a slight compression of the tissue by the ultrasound transducer. The scattering signals from the post compression data set are displaced slightly toward the transducer. The RF signal along each A-line from the precompression image data set is divided into short segments and those segments are cross-correlated with the corresponding post compression RF waveform to estimate tissue displacement at each depth (Fig. 5). The rate of change in tissue displacement vs. depth/distance from the transducer (strain values) is then computed. Strain in soft materials is large since tissue displacement is greatest near the compressor and decreases at greater distances. Strain in stiff materials is low since stiff materials tend to move as a unit so that material both close and far away from the transducer all moves about the same distance- as shown in Fig. 6). The strain values are then displayed as an image known as a strain elastogram (Fig. 7). Strain elastograms are images of relative stiffness (similar to MR images being relative signal intensity images) and cannot be easily processed to provide quantitative stiffness estimates. Because of this feature, a strain elastogram is excellent for display of local variations in stiffness such as masses but poor for display of diffuse abnormality which will simply appear on the image as homogenous material. Strain images may span a larger range of strain values than can properly be displayed on an 8 bit grayscale display. For this reason, windowing to show only certain ranges of strain values is employed or color coding is
Fig. 5. Tracking tissue displacement with cross-correlation. For each radio-frequency (RF) waveform returned from the tissue, a cross-correlation function is used to find matching segments in the post compression waveform compared to the precompression waveform. The shift in distance of the matching segment of the waveform from the transducer A–B represents the amount of tissue displacement (D) at that depth in the image. This is done for all the RF waveforms to create a 2D map of displacements vs. depth.
Fig. 6. Strain is the change in tissue displacement vs. depth. This is ordinarily calculated by taking the derivative of the displacement function vs. depth. But for a simpler case, consider the displacement at two points, one being the front (superficial) edge of a mass and the second being the back (deep) edge of a mass. The change in displacement is simply the difference between the displacement of the front and back edges. For a soft lesion the displacement toward the transducer of the front edge is smaller than the back edge as shown (distance D > distance K) but for the hard lesion the displacements of the front and back edges (D and K) are nearly equal. Thus the strain for lesion S is much greater than that for lesion H.
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 7. Strain elastogram of cancer. The left hand image is a sonogram and the right an elastogram of an invasive ductal breast carcinoma. The elastogram shows a dark (stiff) lesion
with irregular margins that is larger than the lesion on the corresponding sonogram. The apparent size differential is a characteristic of most breast cancers on strain elastography.
Fig. 8. Grayscale elastogram (A) of a breast lesion shows the lesion to be dark (stiff). The color elastogram (B) in the same patient shows the lesion well. Many color maps are
possible but most scanners will have a mode for displaying the Hitachi color map so that the Tsukuba scale can be used.
used. On grayscale images, black is usually used for small strain values and white for large values. For color displays various color schemes are used depending on the
manufacturer (Fig. 8). One of the first clinical units to employ strain elastography was a Hitachi system and this system uses blue to denote stiff tissues (Fig. 9). The
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 9. Color strain elastogram of prostate cancer. The strain elastogram (left) shows a dark blue (hard) area (arrows) in the left peripheral zone corresponding to a hypoechoic area
on the b-mode ultrasound (right). At biopsy this lesion was a prostatic carcinoma. Image from www.hitachi-medicalsystems.eu.
graphic lesion size with the sonographic size but impairs the visualization of heterogeneity in the elastographic image. Lesion size difference is a useful criterion for breast cancer diagnosis (Fig. 11). It is also possible to compress tissue using an acoustic ‘‘push pulse’’ followed by tracking the resulting tissue displacement as described above. This method, called acoustic radiation force impulse imaging (ARFI imaging) [15] can create high quality strain elastograms but is limited to shallower depths due to the limited ability of the ARFI pulse to displace tissue sufficiently at depths greater than approximately 6 cm.
Strain elastogram quantification Fig. 10. The Tsukuba grading system for strain elastograms. Higher grade lesions are more likely to be cancerous. A sixth category (not shown) showing a characteristic color striped appearance is characteristic of a cyst.
widely used Tsukuba classification system [14] for strain elastograms was developed using Hitachi systems and so employs the same color scheme (Fig. 10). For this reason, most other manufacturers include a color map similar to the Hitachi map so that their customers may use the Tsukuba system. Images may be displayed side by side with the sonogram or as a color overlay. The color overlay somewhat improves the observer’s ability to compare the elasto-
As noted above, the strain elastogram is an image of relative stiffness similar to MRI where each image has its own brightness scale so that there is no fixed relationship between brightness and stiffness. There is great interest in having images with a direct and fixed relationship between image brightness and Young’s modulus (the most common measure of material stiffness) or some other mechanical property of tissue. There are two methods by which quantification can be achieved from strain images. The first is to place some sort of pressure sensor on the ultrasound transducer so that the actual pressure applied during compression is measured. If the pressure applied is known and the strain is known, Young’s modulus can in theory be calculated. In practice the calculation of Young’s modulus from a strain image is quite complex
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 11. Lesion size differential. grayscale ultrasound (right) and strain elastogram (left) of an invasive ductal carcinoma. Note the larger size of the lesion on the elastogram compared
with the hypoechoic lesion on the sonogram. If one includes the echogenic surrounding material on the sonogram in the size estimate, the difference is smaller but still present.
requiring a model of the material including its shape and knowledge of the boundary conditions under which the measurement was taken. Also, putting a reliable pressure sensor on an ultrasound transducer without degrading ultrasound imaging performance or transducer ergonomics has been challenging. For these reasons progress has been slow on developing clinically useful ways of estimating Young’s modulus from strain data (the so called ‘‘inverse problem’’). Steady progress is being made however [16, 17] and reasonable Young’s modulus images from strain elastograms are now possible (Fig. 12). The second approach is to obtain semi-quantitative results by taking the ratio of the strain measured in an area of interest to that from tissue elsewhere in the image that is presumed to be normal and for which strain values are relatively stable. This ‘‘strain ratio’’ approach has produced better results for cancer diagnosis than using a subjective lesion grading scheme in the breast [18–20], in lymph nodes [21], in thyroid [22–24], and in the prostate [25]. Several elastographic systems now offer strain ratio calculation and others will likely follow suit (Fig. 13). Even though strain ratios have been shown to be useful in the literature, experience in clinical practice has been variable. Much of this could be due to using poor quality strain elastograms to measure strain ratios but another factor could be the lack of guidance from manufacturers on how to select images for high quality strain ratio calculation and how to pick regions of interest to make the calculation. Recently work has begun at the FDA on modeling some of the factors that
may degrade the quality of strain ratio estimates. Work using simple phantoms and phantom models has shown that the location of the ‘‘normal’’ tissue ROI can greatly affect the strain ratio value [26]. This is due to distortion of the strain values in surrounding tissue caused by the presence of a lesion. Both distance from the lesion of interest and the direction of placement of the comparison ROI are important (Fig. 14). Work using more complex finite element models and tissue mimicking phantoms is underway.
Strain elastogram quality The quality of a strain elastogram can vary greatly from acquisition to acquisition. Even during an acquisition (one or more compression-relaxation cycles) the quality varies between compression and relaxation and between mid-compression and end compression (Fig. 15). Generally a high quality elastogram will be derived from high quality displacement data and have a smooth background and high lesion contrast if a lesion is present. A low quality elastogram will have a background filled with small high and low brightness foci (Fig. 16) denoting poor strain estimates which can be recognized on a correlation coefficient image as generalized low correlation values in the image. Lesion visibility is not a good indicator of a high quality elastogram because the lesion may be quite visible in an extremely noisy elastogram (Fig. 17). Because it is often difficult for a reader to tell which of the hundreds of images obtained during several
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 12. Phantom images (A) barely show the Isoechoic lesion on b-mode ultrasound. On the strain image (s) and on the Young’s modulus image (Y) the lesion stands out clearly. Note that the Young’s modulus image is calibrated. The reconstructed Young’s modulus image (B) of an in vivo invasive ductal breast cancer shows high resolution (200 lm) similar to a sonogram. This allows the texture of the cancer to be analyzed. Shear wave elastograms are also calibrated but
are much lower in spatial resolution. An image of the nonlinear stiffening parameter (C) shows the lesion clearly also. This non-linear parameter may be a useful feature for cancer diagnosis as cancers become stiffer with precompression much more rapidly than do benign tissues. The phantom images are from Pan et al. [40] with permission and the cancer images are courtesy of Assad Oberai and Paul Barbone.
acquisition cycles are good, an indicator is usually provided on-screen to identify good and poor elastograms (Fig. 18). The algorithms and criteria that are used to determine which images are of high quality vary among manufacturers which could lead to some interpretation variability. When strain elastography became widely available and many articles were published on the usefulness of the technique, many others purchased systems and got less impressive results, probably because they did not receive adequate training and practice. The basic technique is to apply several cycles of gentle compression—relaxation using the transducer to compress about 2–5 mm on each cycle. The technique is simple in theory but is quite different from the technique used for sonography. Some tips for producing high quality strain elastograms are:
downward compressions without sideways transducer motion are achieved. This is a critical step. The compression should be at a constant rate as nearly vertical to the skin as possible with no sliding either along the transducer long axis or perpendicular to the long axis. Sliding motions result in poor quality, noisy elastograms. 2. Start with the transducer barely touching the skin. One common problem is that the operator begins with the tissue already compressed because tissue compression usually leads to higher quality sonograms. When tissue is already compressed prior to making an elastogram the tissue is already stiffer than it normally would be which reduces the contrast between the lesion and the surrounding tissue. Also precompression can make the tissue so stiff that lesions tend to slide sideways away from the transducer further degrading
1. Support your forearm so that very controllable
B.S. Garra: Elastography: history, principles, and technique comparison
tually no compression whereas others require more. During a compression—relaxation cycle, good elastograms may be achieved during both compression and relaxation. In fact, many systems tend to produce better elastograms during the relaxation phase of the cycle. 5. Watch the b-mode scan during compression and relaxation for sideways movement. As noted previously, sideways movement seriously degrades strain elastograms. Sideways movement can occur if too much precompression is applied, if the transducer face is tilted with respect to the skin or underlying structure against which compression is applied or if the surface against which compression is applied is curved (for example if compression is applied to muscle overlying a bone). If sideways movement is observed then a slight downward tilt of the transducer on the side toward which the tissue is moving will usually reduce or eliminate the unwanted motion. If the tissue appears to be changing but not compressing or moving sideways, then it may be moving out of the imaging plane (perpendicular to the transducer). This movement is extremely detrimental to good elastography. Sometimes changing the patient position or the plane of imaging will reduce or eliminate this problem.
Fig. 13. Strain ratios. A Quantitative stiffness estimation in the Gastrocnemius muscle using the calibrated standoff pad method. Hard and soft standoff pads are both included to cover the expected range of stiffness values within the muscle. From: Chino et al. [41]. B Strain ratio calculation for a breast mass compared to breast fat. Image from Hitachi web site: http://www.hitachi-medical-systems.eu/products-andservices/ultrasound/clinical-applications/clinical-applicationsfor-hitachi-platforms/internal-medicine.html#Clinical-Images-4. Accessed 27 October 2014.
the images. To achieve minimal precompression one must start in full contact with the skin then back the transducer away so that the skin is pulled slightly upward by suction of the skin against the transducer face before beginning the compression-relaxation imaging cycle. 3. Use a thin gel. Thick gels are relatively stiff compared to some body tissues and may result in unwanted precompression if used. If a thicker gel must be used then the method outlined above to remove precompression becomes a necessity. 4. Find the correct rate of compression. Each system will have a slightly different compression rate that produces the best quality elastograms. Some require vir-
The final step in improving and maintaining elastography quality is the need to follow accepted standards for acquisition, display and interpretation. As mentioned previously, the first standard method for interpretation was the Tsukuba classification scheme for breast lesions. This same standard was later applied to other organs as a way to classify lesions as stiff vs soft lesions and has met with some success in lymph nodes and thyroid nodules [21, 24, 27]. The European Union (EU) has since published standards for acquisition of both strain and shear wave elastograms [28, 29].
Elastography using dynamic techniques Dynamic Elastography Currently Encompasses Two Major Methods, sonoelasticity imaging—crawling wave elastography and shear wave elastography. Shear wave elastography has become the dominant method by far, surpassing even strain elastography in many situations, perhaps because it does not require an external vibration device which is seen by most as too inconvenient and time consuming to set up in a clinical environment. For this reason the focus of the remainder of this section will be on shear wave elastography. Shear wave elastography involves the estimation of shear wave speed which is high in stiff tissues and low in soft tissues. Normal acoustic waves are compressional
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 14. Background Strain Distortion. A finite element model (FEM) of a hard lesion in a softer surrounding material (upper left) shows distortion of the strain values in the background material by the presence of the lesion or mass. The background material should be a uniform medium gray. An ultrasound strain simulation using the same FEM shows the same background errors (upper right). A real gel phantom with a hard inclusion is imaged using an ultrasound system (lower left). The strain elastogram of the phantom shows the same background strain distortions (lower right).
(longitudinal) waves that travel at high speeds through tissue (1450–1550 m/s). These waves also travel at higher speed through stiff materials than they do in soft materials, but they are difficult to track using ultrasonic methods. Shear waves on the other hand are transverse waves that propagate much more slowly (usually 1– 10 m/s) in tissue. The waves can be generated by mechanical means or can be generated at the margins of an acoustic (compressional) wave similar to an ARFI pulse as it traverses tissue (Fig. 19A, B). A shear wave generated from an acoustic wave travels perpendicular to the acoustic wave and can be tracked using multiple tracking pulses emitted lateral to the original pushing acoustic pulse to monitor tissue displacement as the shear wave passes (Fig. 19C). Shear wave speed (SWS) can be converted to elastic (Young’s) modulus using the simple equation: E ¼ 3qV2 where E is Young’s modulus, q is tissue density (often assumed to be 1.0) and V is the shear wave speed. The constant ‘‘3’’ relates the shear modulus to the Young’s modulus for isotropic homogeneous materials. Most clinical systems compute the Young’s modulus using the above equation, and warnings are present in either the
manual or on-screen about the assumptions being made when it is calculated. For heterogeneous and/or anisotropic materials (usually those having micro or macroscopic structural alignment in one or more directions) such as skeletal muscle, or renal medulla, one can simply use the SWS which is a reliable surrogate for tissue stiffness. In these materials the SWS will be different in the direction aligned with the structures as opposed to a direction of propagation perpendicular to the aligned structures, for example along muscle fibers vs. across the fibers. A shear wave speed image is usually presented as a color overlay on a grayscale b-mode image with either red or blue denoting stiff tissues. A color bar at the side of the image will relate the colors to either shear wave speed or calculated Young’s modulus and usually the manufacturer will identify which colors represent stiff and which represent soft. Since the image is quantitative the operator may place a cursor or define a region of interest and obtain the average SWS or Young’s modulus estimate from that location (Fig. 20). Additional factors that may affect the SWS and Young’s modulus estimates include the distance from the transducer which may result from errors in the SWS estimation algorithm, especially for convex or other fan
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 15. Variation in image quality during a compression-decompression cycle. A Good quality elastogram during a strain elastography compression cycle. B Poor quality elastogram from the same cycle.
beam transducers, the amount of precompression applied, tissue heterogeneity which may produce malformed shear waves, and tissue attenuation which may cause the shear waves to be too weak to measure accurately. Manufacturers generally have an on-screen display that indicates poor SWS estimates such as giving an error indication when estimates are out of the expected range of realistic values but errors in estimates within the expected range often go undetected. The user must be aware of the fact that SWS estimation is in an early stage and there is always the possibility of error in SWS images and numerical estimates.
A related problem may occur in focal lesions where erroneous estimates of SWS appear within the lesion. This problem has been noted in breast cancers and appears as low SWS values in lesions that are clearly cancerous on b-mode imaging and strain elastography. This finding was initially attributed to tissue necrosis at the tumor center but in many cases tissue necrosis was not present on histological examination. The cause of the apparent error in SWS is not entirely clear but may be due to reflected shear waves at lesion boundary partially canceling the shear waves emanating from the ARFI pulse causing the shear wave speed algorithm to fail. This
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 16. Correlation coefficients. A High quality elastogram with high correlation coefficients (white in correlation coefficient map). B Poor quality elastogram with low correlation coefficients (dark on correlation coefficient map).
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 17. Lesion visibility in a noisy image. The sonogram (right) shows a suspicious lesion (circled). The elastogram on the left shows the corresponding lesion despite the fact that
most of the image is very poor having the speckled appearance of multiple cross-correlation failures.
is a serious error because it might cause a cancerous lesion to be incorrectly classified as benign. Usually a few high SWS foci are seen at the periphery of the lesion or immediately adjacent to the lesion (Fig. 21) to help identify the nature of the lesion. This is an example where strain elastography is useful to aid in correct classification of a lesion since it is less affected by tissue heterogeneity and boundaries resulting from a focal lesion.
acquired. The quantitative imaging biomarker alliance (QIBA) is an initiative involving representatives from academia, industry and government formed by the radiological society of America (RSNA) to promote the accurate estimation of quantitative biomarkers derived from imaging for use in clinical practice and research. It includes biomarkers such as CT estimation of lung lesion size and was recently extended to include SWS estimation in the liver. Equipment parameters, acquisition techniques and data reduction are carefully specified to achieve specified levels bias and variance. The QIBA SWS committee is examining all potential sources of bias and variance in an effort to minimize variability to improve diagnostic accuracy of shear wave elastography for liver fibrosis assessment [31]. One important source of variability between scanners is the fact that SWS depends on the frequency of the shear wave which varies from manufacturer to manufacturer. This speed dependence on frequency is due to the viscosity of tissue which affects the propagation of shear waves and is unaccounted for when using only Young’s modulus (related to the storage modulus in a viscoelastic model of materials) to characterize tissues. Whether the viscosity component (known as the loss modulus in a viscoelastic model) is useful for characterizing abnormities in tissue is so far unknown. The QIBA SWS committee is in the process of testing commercial scanners with viscoelastic phantoms to develop strategies for relating SWS estimates at one frequency with those taken at another.
Shear wave elastogram quality As mentioned above, manufacturers of display an error message or indicator when shear wave speed estimates are out of range. For images a single indicator does not show where incorrect values are present so companies may flag poor estimates with a special color, or use black to indicate unreliable values. Other indicators may include a second image showing where poor estimates are likely. The evaluation of SWS estimates for errors and how to display them is still a work in progress and improvements in this area are likely. Just as in strain elastography, proper acquisition technique is critical for accurate and high quality images. All manufacturers discuss the need to apply very light pressure to avoid precompression of tissues and all discuss the need to acquire at a standard depth to avoid variability from one exam to the next due to depth induced variation. As in strain elastography, national [30] and international guidelines [28, 29] are emerging that will help to standardize how shear wave elastograms are
B.S. Garra: Elastography: history, principles, and technique comparison
B.S. Garra: Elastography: history, principles, and technique comparison
Quality indicators. On-screen elastogram quality indicators are outlined by yellow circles. The most common indicators are bar or pie chart based where the longer the bar or the more pie sections are filled in, the better the quality of the elastogram. The variables that go into the quality estimation vary but usually involve correlation coefficients to estimate the quality of the tissue displacement estimates, and average strain level in the image.
b Fig. 18.
Application to abdominal diseases Elastography in one form or another has been tried in most intra-abdominal diseases with varying success. In the liver, diffuse liver disease, especially fibrosis, has been studied extensively as has the prostate. Focal liver lesions including hepatocellular carcinoma (HCC) and cholan-
Fig. 19. Longitudinal or compressional waves and shear waves. A Longitudinal (compressional) wave. The particles making up the material move along the same axis as the direction of propagation. B Transverse wave (shear wave). The particles making up the material move along an axis perpendicular to the direction of propagation. C Creation and tracking of shear waves. The ultrasound transducer emits a beam consisting of a series of compressional wave pulses which push the tissue away from the transducer and cause shear waves to be generated at the margins of the push beam. The shear waves travel away from the push pulse beam and are tracked by lower power tracking pulses also emitted by the ultrasound transducer. In this way the speed of the shear wave can be estimated.
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 20. Shear wave speed image. A region of interest (ROI) has been placed to obtain average or median shear wave speed in that location. From the Supersonic Imagine web site: http://www.supersonicimagine.com/var/ezwebin_site/storage/ images/aixplorer-r/general-imaging/pediatrics/smc12-3-liverfibrosis-on-a-21d-old-child-aixplorer-supersonic-imagine/71914-eng-GB/SMC12-3-Liver-fibrosis-on-a-21d-old-child-AixplorerSuperSonic-Imagine.jpg. Accessed 10-26-2014.
giocarcinoma have also been studied but studies have shown considerable overlap between HCC stiffness values and those of benign lesions [32, 33]. The spleen has been studied primarily as a means to detect worsening portal hypertension and varices [34]. The pancreas has been studied primarily by means of endoscopic ultrasound elastography (strain elastography) and this form of elastography has also been used to study ulcerative colitis and Crohn’s disease. Kidney elastography has been successfully performed in patients with diffuse renal disease, but the results do not clearly show a correlation with extent of diffuse renal disease [35]. Limited work has also been performed on focal kidney lesions [36]. Intraabdominal lymph nodes have primarily been studied using endoscopic ultrasound with promising results [37]. Finally the uterus and adnexa have been studied looking for changes in uterine stiffness during pregnancy and for evaluation of fibroids, adenomyosis [38] and ovarian tumors [39].
Fig. 21. Breast cancer with low apparent SWS in a stiff (hard) Lesion. Hypoechoic invasive ductal cancer (yellow arrows) corresponds to blue (soft) tissue on SWS image but such cancers are typically very stiff. Reflection of the shear waves at lesion margins is one proposed cause of the erroneous SWS values. Taking the stiffness estimate at the lesion margins where high SWS may be found helps to correctly classify the lesion. Strain elastography may also be performed to confirm a uniformly stiff lesion.
Comparison of strain and shear elastography for abdominal applications Table 1 shows a rough comparison of strain elastography with shear wave elastography for abdominal applications. The choice of elastography may depend on the specific intended application and since the capability of each method is rapidly evolving, the choice may change
B.S. Garra: Elastography: history, principles, and technique comparison
Table 1. Comparison of strain and shear wave elastography Attribute
Strain elastography
Shear wave elastography
Availability
Widely available, many manufacturers and models
Image quality
Excellent if properly performed
Real time imaging
Yes
Maximum depth Quantification
Good. Depends on force and displacement applied Limited (strain ratios). Elastic modulus reconstructions generally not yet available Better for focal than diffuse. Since image is not calibrated diffuse stiffness may appear the same on an image as diffuse softness Considerable. Hands on training usually required
Limited availability but improving. Only five FDA cleared systems Good to excellent. All systems limit the color elastogram to a region of interest of variable size No. Color elastograms are only obtainable at a very low frame rate Limited. Typically 6 cm or less for good quality Excellent. Quantification from a region of interest and color display is calibrated Better for diffuse than focal. Algorithms and computations are designed for diffuse diseases. May give erroneous values in focal lesions Limited if operator already knows sonography. But operator must carefully follow instructions for accurate elasticity estimates
Diffuse/focal disease Operator dependence
over time. At the present time diffuse organ diseases may be best evaluated using shear wave elastography which has quantification built in and thus can give some sort of global numerical estimate of stiffness. Focal lesions may be evaluated using both methods and to avoid errors in either method, the best course may be to perform both types of elastography. Progress toward quantification of strain elastography may allow this method to better evaluate diffuse disease, particularly for depths greater than what can be achieved using shear wave elastography (about 6 cm). On the other hand, improvements in ARFI and shear wave speed estimators may increase the depth at which SWS measurement can be performed. Both methods have the potential to be valuable contributors to diagnosis of many of the diseases encountered in the abdomen and retroperitoneum. References 1. Allen JP (2005) The art of medicine in ancient egypt. New York: The Metropolitan Museum of Art, p 70 2. Breasted JH (1991) The Edwin Smith surgical papyrus: published in facsimile and hieroglyphic transliteration with translation and commentary in two volumes. Chicago: University of Chicago Press, p 9 3. Huangdi Nijing, Wikipedia entry. http://en.wikipedia.org/wiki/ Huangdi_Neijing#cite_note-wdl-1. Accessed 10 April 2014 4. Fu KL, Fu YS, Bassett LW, Cardall LW, Lopen JK (2005) Invasive malignancies. In: Bassett LW (ed) Diagnosis of diseases of the breast, 2nd edn. Philadelphia: Saunders 5. Kelly KM (1996) Breast ultrasound. Crit Rev Diagn Imaging 37:79–161 6. Tristam M, Barbosa DC, Cosgrove DO, et al. (1986) Ultrasonic study of in vivo kinetic characteristics of human tissues. Ultrasound Med Biol 12:927–937 7. Tristam M, Barbosa DC, Cosgrove DO, Bamber JC, Hill CR (1988) Application of Fourier analysis to clinical study of patterns of tissue movement. Ultrasound Med. Biol. 14:695–707 8. Lerner RM, Parker KJ, Holen J, Gramiak R, Waag RC (1988) Sonoelasticity: medical elasticity images derived from ultrasound signals in mechanically vibrated targets. Acoust Imaging 16:317–327 9. Parker KJ, Huang SR, Musulin RA, Lerner RM (1990) Tissue response to mechanical vibrations for ‘‘sonoelasticity imaging’’. Ultrasound Med Biol 16:241–246 10. Wu Z, Taylor LS, Ruben DJ, Parker KJ (2004) Sonoelastographic imaging of interference patterns for estimation of the shear velocity of homogeneous biomaterials. Phys Med Biol 49:911–922
11. Ophir J, Ce´spedes I, Ponnekanti H, Yazdi Y, Li X (1991) Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging. 13(2):111–134 12. Garra BS, Ophir J, Spratt SR, et al. (1997) Elastography of breast lesions: initial clinical results. Radiology 202:79–86 13. Sandrin L, Fourquet B, Hasquenoph JM, et al. (2003) Transient elastography: a new noninvasive method for assessment of hepatic fibrosis. Ultrasound Med Biol 29:1705–1713 14. Itoh A, Ueno E, Tohno E, et al. (2006) Breast disease: clinical application of US elastography for diagnosis. Radiology 239:341– 350 15. Nightingale K, Soo MS, Nightingale R, Trahey G (2002) Acoustic radiation force impulse imaging: in vivo demonstration of clinical feasibility. Ultrasound Med Biol. 28(2):227–235 16. Goenezen S, Dord JF, Sink J, et al. (2012) Linear and nonlinear elastic modulus imaging: an application to breast cancer diagnosis. IEEE Trans Med Imaging 31(8):1628–1637 17. Yan Z, Zhang S, Alam SK, et al. (2012) Modulus reconstruction from prostate ultrasound images using finite element modeling. In: Proceedings of SPIE 8320, medical imaging 2012: ultrasonic imaging, tomography, and therapy, 832016. doi:10.1117/12.911088 18. Zhi H, Xiao X-Y, Yang H-Y, et al. (2010) Ultrasonic elastography in breast cancer diagnosis: strain ratio vs 5-point scale. Acad Radiol 17(10):1227–1233 19. Cho N, Moon WK, Kim HY, et al. (2010) Sonoelastographic strain index for differentiation of benign and malignant nonpalpable breast masses. J Ultrasound Med 29(1):1–7 20. Gheonea IA, Stoica Z, Bondari S (2011) Differential diagnosis of breast lesions using ultrasound elastography. Indian J Radiol Imaging 21(4):301–305 21. Ying L, Hou Y, Zheng H-M, et al. (2012) Real-time elastography for the differentiation of benign and malignant superficial lymph nodes: a meta-analysis. Eur J Radiol 81(10):2576–2584 22. Lyshchik A, Higashi T, Asato R, et al. (2005) Thyroid gland tumor diagnosis at US elastography. Radiology 237(1):202–211 23. Bae U, Dighe M, Dubinsky T, et al. (2007) Ultrasound thyroid elastography using carotid artery pulsation: preliminary study. J Ultrasound Med 26(6):797–805 24. Wang H, Brylka D, Sun LN, et al. (2013) Comparison of strain ratio with elastography score system in differentiating malignant from benign thyroid nodules. Clin Imaging 37(1):50–55 25. Zhang Y, Tang J, Li YM, et al. (2012) Differentiation of prostate cancer from benign lesions using strain index of transrectal realtime tissue elastography. Eur J Radiol 81:857–862 26. Jia C, Alam SK, Zahiri Azar R, Garra B (2014) Estimation of shear modulus ratio between inclusion and background using strain ratios in 2D ultrasound elastography. IEEE Trans UFFC 61(4):611– 619 27. Moon HJ, Sung JM, Kim EK, et al. (2012) Diagnostic performance of gray-scale US and elastography in solid thyroid nodules. Radiology 262(3):1002–1013
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28. Bamber J, Cosgrove D, Dietrich CF, et al. (2013) EFSUMB gidelines and recommendations on the clinical use of ultrasound elastography. Part I: basic principles and technology. Ultraschall Med 34:169–184 29. Cosgrove D, Piscaglia F, Bamber J, et al. (2013) EFSUMB gidelines and recommendations on the clinical use of ultrasound elastography. Part II: clinical applications. Ultraschall Med 34:238–253 30. Society of Radiologists in Ultrasound (2014) Consensus conference on ultrasound elastography for assessment of diffuse liver disease, Denver, CO, 21–22 October 2014 31. Hall TJ, Milkowski A, Garra B, et al. (2013) IEEE international ultrasonics symposium (IUS) on RSNA/QIBA: shear wave speed as a biomarker for liver fibrosis staging, pp 397, 400, 21–25 July 2013 32. Onur MR, Poyraz AK, Ucak EE, et al. (2012) Semiquantitative strain elastography of liver masses. J Ultrasound Med 31(7):1061– 1067 33. Guibal A, Boularan C, Bruce M, et al. (2013) Evaluation of shearwave elastography for the characterisation of focal liver lesions on ultrasound. Eur Radiol 23(4):1138–1149 34. Calvaruso V, Bronte F, Conte E, et al. (2013) Modified spleen stiffness measurement by transient elastography is associated with presence of large oesophageal varices in patients with compensated hepatitis C virus cirrhosis. J Viral Hepat 20:867–874
35. Asano K, Ogata A, Tanaka K, et al. (2014) Acoustic radiation force impulse elastography of the kidneys: is shear wave velocity affected by tissue fibrosis or renal blood flow? J Ultrasound Med 33:793–801 36. Tan S, Ozcan MF, Tezcan F, et al. (2013) Real-time elastography for distinguishing angiomyolipoma from renal cell carcinoma: preliminary observations. Am J Roentgenol. 200:369–375 37. Xu W, Shi J, Zeng X, et al. (2011) EUS elastography for the differentiation of benign and malignant lymph nodes: a meta-analysis. GI Endosc 74:1001–1009 38. Stoelinga B, Hehenkamp WJK, Brolmann HAM, Huirne JAF (2014) Real-time elastography for assessment of uterine disorders. Ultrasound Obstet Gynecol 43(2):218–226 39. Xie M, Zhang X, Zhan J, Hua K (2013) Application of real-time ultrasound elastography for discrimination of low- and high-grade serous ovarian carcinoma. J Ultrasound Med 32:257–262 40. Pan X, et al. (2014) A regularization-free elasticity reconstruction method for ultrasound elastography with freehand scan. Biomed Eng 13:132 41. Chino K, Akagi R, Dohi M, Fukashiro S, Takahashi H (2012) Reliability and validity of quantifying absolute muscle hardness using ultrasound elastography. PLoS ONE 7(9):e45764. doi: 10.1371/journal.pone.0045764
Abdominal Imaging
Abdom Imaging (2015) DOI: 10.1007/s00261-014-0305-8
Elastography: history, principles, and technique comparison Brian S. Garra1,2 1 2
Washington DC VA Medical Center, Washington, DC, USA US Food and Drug Administration, Silver Spring, MD, USA
Abstract Elastography is a relatively new imaging technology that creates images of tissue stiffness. It can be thought of an extension of the ancient technique of palpation but it gives better spatial localization information and is less subjective. Two main types of elastography are currently in use, strain elastography where the tissue displacement in response to gentle pressure is used to compute and image tissue strain, and shear wave elastography where the speed of shear waves traversing tissue is measured and used to create an image of tissue stiffness. Each method has advantages and disadvantages but generally strain imaging is excellent for focal lesions and shear wave imaging, being more quantitative, is best for diffuse organ diseases. Strain imaging requires additional training in acquisition technique to obtain high quality images. Pitfalls to avoid and tips for good images are provided. Improvements in strain imaging are focused on better quality indicators and better methods for quantification. Improvements in shear wave imaging will be higher frame rates, greater accuracy in focal lesions, and making results more comparable between different ultrasound systems. Both methods will continue to improve and will provide ever more powerful new tools for diagnosis of diffuse and focal diseases. Key words: Elastography—Elasticity—Shear wave—Ultrasound—Breast—Thyroid—Lymph node
Historical background Hardness/stiffness is a key property of abnormal tissues and organs. The value of assessing this property has been known since the earliest days of civilization. Writings in the Edwin Smith papyrus [1] concerning the use of palpation as a diagnostic tool have been ascribed to physi-
Correspondence to: Brian S. Garra; email: [email protected], [email protected]
cian, priest and architect Imhotep who lived between 3000 and 2500 BCE [2]. Texts from China, particularly the 18 volume Huangdi Neijing which dates from 475 to 221 BCE [3] (and was based on much older practices) mention palpation as a key diagnostic method. Both benign and cancerous masses are well known to be firmer than surrounding tissues and hardness along with lack of lesion mobility are classic signs of malignancy [4]. Soft tissue imaging methods such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) have largely replaced palpation for detection and characterization of masses and for determination of organ sizes. In many cases however, these methods have not been able to distinguish the various types of masses from one another and for this reason, clinicians have resorted advanced imaging methods such as dynamic contrast enhancement, ultrasound contrast agents and MRI apparent diffusion coefficient mapping, with considerable success. These methods are relatively costly and generally require high end imaging equipment and considerable expertise to perform and interpret, all factors that limit their availability. The advent of both qualitative and quantitative image of tissue hardness offers a new way to help distinguish masses and diffuse diseases from one another.
Tissue stiffness imaging using b-mode grayscale ultrasound: Palpation remains a pillar of modern physical diagnosis, but it is very subjective and dependent on the skill and experience of the examiner. For this reason, more accurate and reproducible methods of assessing tissue hardness have been sought. Gray scale ultrasound imaging is a readily available method that may be used for assessment of tissue stiffness. main methods have long been available but are not widely known or used: (a) lesion compression, (b) echopalpation and (c) fremitus. Compression ultrasound is a very simple procedure, intended for focal lesions or masses. One centers the transducer over the lesion and compresses. The amount
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 1. Compression ultrasound of subcutaneous lesion. Precompression image on the left and post compression image on the right. The subcutaneous fat layer compresses from 1.37 to 0.82 cm (40%) whereas the lesion
decreases in diameter from .91 to .77 cm (15%) during compression suggesting that the lesion is harder than the adjacent fat. This was confirmed at real time compression examination.
of flattening that the lesion undergoes is compared to that of the adjacent known tissue (Fig. 1). This gives an estimate of the relative hardness of the lesion compared to the adjacent tissue. This method is subjective and can be used to find an Isoechoic lesion such as a breast tumor in fat where fat lobules may look like masses. The method has also been used in a quantitative way to distinguish cancers from benign tumors such as fibroadenomas [5]. Echopalpation is similar to compression except the lesion or mass is compressed with a finger or wire held in the ultrasound beam while watching with real time ultrasound. The finger or wire is slid through a gel standoff allowing the operator to feel the lesion and watch what is beneath the finger or wire, all at the same time. This method allows the operator to assess local compressibility and also to precisely determine on the image where the palpable edge of the lesion is. This is an excellent method for correlating ultrasound images with palpation. In general, stiff lesions move downward as a unit in response to compression and soft lesions compress like a sponge (Fig. 2). Fremitus is a technique for watching vibrations in tissue induced by the patient humming. While the patient is phonating, vibrations are transmitted from the larynx to the remainder of the body where they can be observed using color and power Doppler. Stiff lesions vibrate less than softer tissues and become visible as a dark area surrounded by color (Fig. 3). With increased sensitivity of color and power Doppler instruments, this method
had significant potential, but never became widespread because of problems with inconsistent display of lesions due in part to differences in the pitch of phonation, attenuation of the vibrations, and variability in the intensity of phonation.
History of elastographic imaging True ultrasonic tissue stiffness assessment grew out of tissue motion studies performed principally in England during the 1980s [6, 7]. These methods used data from the m-mode scan to track movement. By 1988 researchers at the University of Rochester had developed a system that used modified color Doppler to track tissue movement and make tissue stiffness based images. The method, called sonoelasticity imaging [8, 9] was able to image stiff lesions in the prostate gland and other organs as dark areas against a green background of moving tissue. As with vibrational Doppler imaging (a related technique) the lesion appears dark against a color background of vibrating tissue (Fig. 4) but the image is relatively low resolution and requires an inconvenient external vibratory device to induce the tissue motion. More recently, the application of a second vibration source operating at a slightly different frequency, was found to produce a shifting interference pattern termed ‘‘crawling waves’’ that could be used to estimate local shear wave velocity and tissue stiffness [10]. The first widely successful means of imaging tissue elasticity was elastography, now known as strain elas-
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 3. Fremitus. Having the patient hum produces vibrations in tissues that can be imaged using Color Doppler. The stiff lesion vibrates less than the softer surrounding tissue and appears as a dark area in the color image.
Fig. 2. Echopalpation. Localized deformity of the skin and mass just below the skin is seen as the paperclip is dragged across the lesion, A at the left hand end of the lesion (arrow), B at the middle of the lesion (arrow), and C at the right hand margin of the lesion. The local deformity of the lesion caused by the paperclip suggests that it is a relatively soft mass.
tography, first reported in 1991 by Cespedes and Ophir [11]. This method uses the response of tissue to a gentle compression by the ultrasound transducer to generate an image of relative tissue stiffness. High quality strain images of tissue hardness quickly became possible with this technology with the first applications being in detection of prostate cancer and characterization of breast masses as benign or malignant [12]. Strain elas-
Fig. 4. Sonoelasticity image of liver post radio-frequency ablation. The ablated tissue is much stiffer than the surrounding tissue and vibrates less producing the dark area (outlined in pink and blue) on the image. Image from www.rochester.edu website courtesy of Diane Dalecki and Deborah Rubens.
tography is now a relatively mature technology with most manufacturers providing this capability on at least some of their US systems. Strain elastography was soon followed by the development of shear wave elastography in which the speed at which shear waves traverse tissue is used to generate an image of tissue stiffness. One of the first developments in this field was a non-imaging device known as the FibroscanTM which uses shear wave speed
B.S. Garra: Elastography: history, principles, and technique comparison
techniques published in 2003 [13] to estimate liver stiffness. Imaging devices using shear wave speed soon followed. Shear wave elastography (which can be performed using ultrasound or MRI) has rapidly become popular because obtaining good images is relatively easy and the images of shear wave speed relate directly to the shear and elastic moduli of tissue, not relative stiffness as in strain elastography.
Methods of elastographic imaging As noted above, many methods for imaging tissue stiffness have been demonstrated but currently two main approaches have become dominant, those generating strain images and those generating shear wave speed images. Strain elastography involves one or more gradual compressions of tissue and does not monitor or image any vibrations or waves in the tissue. For this reason it is termed a ‘‘static’’ or ‘‘quasistatic’’ technique whereas shear wave elastography and sonoelastography create images based on moving waves and are thus termed ‘‘dynamic’’ techniques. These terms are often used in the literature to classify elastographic methods.
Strain (static) elastography In strain elastography, radio-frequency (RF) ultrasound signals are acquired before and after a slight compression of the tissue by the ultrasound transducer. The scattering signals from the post compression data set are displaced slightly toward the transducer. The RF signal along each A-line from the precompression image data set is divided into short segments and those segments are cross-correlated with the corresponding post compression RF waveform to estimate tissue displacement at each depth (Fig. 5). The rate of change in tissue displacement vs. depth/distance from the transducer (strain values) is then computed. Strain in soft materials is large since tissue displacement is greatest near the compressor and decreases at greater distances. Strain in stiff materials is low since stiff materials tend to move as a unit so that material both close and far away from the transducer all moves about the same distance- as shown in Fig. 6). The strain values are then displayed as an image known as a strain elastogram (Fig. 7). Strain elastograms are images of relative stiffness (similar to MR images being relative signal intensity images) and cannot be easily processed to provide quantitative stiffness estimates. Because of this feature, a strain elastogram is excellent for display of local variations in stiffness such as masses but poor for display of diffuse abnormality which will simply appear on the image as homogenous material. Strain images may span a larger range of strain values than can properly be displayed on an 8 bit grayscale display. For this reason, windowing to show only certain ranges of strain values is employed or color coding is
Fig. 5. Tracking tissue displacement with cross-correlation. For each radio-frequency (RF) waveform returned from the tissue, a cross-correlation function is used to find matching segments in the post compression waveform compared to the precompression waveform. The shift in distance of the matching segment of the waveform from the transducer A–B represents the amount of tissue displacement (D) at that depth in the image. This is done for all the RF waveforms to create a 2D map of displacements vs. depth.
Fig. 6. Strain is the change in tissue displacement vs. depth. This is ordinarily calculated by taking the derivative of the displacement function vs. depth. But for a simpler case, consider the displacement at two points, one being the front (superficial) edge of a mass and the second being the back (deep) edge of a mass. The change in displacement is simply the difference between the displacement of the front and back edges. For a soft lesion the displacement toward the transducer of the front edge is smaller than the back edge as shown (distance D > distance K) but for the hard lesion the displacements of the front and back edges (D and K) are nearly equal. Thus the strain for lesion S is much greater than that for lesion H.
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 7. Strain elastogram of cancer. The left hand image is a sonogram and the right an elastogram of an invasive ductal breast carcinoma. The elastogram shows a dark (stiff) lesion
with irregular margins that is larger than the lesion on the corresponding sonogram. The apparent size differential is a characteristic of most breast cancers on strain elastography.
Fig. 8. Grayscale elastogram (A) of a breast lesion shows the lesion to be dark (stiff). The color elastogram (B) in the same patient shows the lesion well. Many color maps are
possible but most scanners will have a mode for displaying the Hitachi color map so that the Tsukuba scale can be used.
used. On grayscale images, black is usually used for small strain values and white for large values. For color displays various color schemes are used depending on the
manufacturer (Fig. 8). One of the first clinical units to employ strain elastography was a Hitachi system and this system uses blue to denote stiff tissues (Fig. 9). The
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 9. Color strain elastogram of prostate cancer. The strain elastogram (left) shows a dark blue (hard) area (arrows) in the left peripheral zone corresponding to a hypoechoic area
on the b-mode ultrasound (right). At biopsy this lesion was a prostatic carcinoma. Image from www.hitachi-medicalsystems.eu.
graphic lesion size with the sonographic size but impairs the visualization of heterogeneity in the elastographic image. Lesion size difference is a useful criterion for breast cancer diagnosis (Fig. 11). It is also possible to compress tissue using an acoustic ‘‘push pulse’’ followed by tracking the resulting tissue displacement as described above. This method, called acoustic radiation force impulse imaging (ARFI imaging) [15] can create high quality strain elastograms but is limited to shallower depths due to the limited ability of the ARFI pulse to displace tissue sufficiently at depths greater than approximately 6 cm.
Strain elastogram quantification Fig. 10. The Tsukuba grading system for strain elastograms. Higher grade lesions are more likely to be cancerous. A sixth category (not shown) showing a characteristic color striped appearance is characteristic of a cyst.
widely used Tsukuba classification system [14] for strain elastograms was developed using Hitachi systems and so employs the same color scheme (Fig. 10). For this reason, most other manufacturers include a color map similar to the Hitachi map so that their customers may use the Tsukuba system. Images may be displayed side by side with the sonogram or as a color overlay. The color overlay somewhat improves the observer’s ability to compare the elasto-
As noted above, the strain elastogram is an image of relative stiffness similar to MRI where each image has its own brightness scale so that there is no fixed relationship between brightness and stiffness. There is great interest in having images with a direct and fixed relationship between image brightness and Young’s modulus (the most common measure of material stiffness) or some other mechanical property of tissue. There are two methods by which quantification can be achieved from strain images. The first is to place some sort of pressure sensor on the ultrasound transducer so that the actual pressure applied during compression is measured. If the pressure applied is known and the strain is known, Young’s modulus can in theory be calculated. In practice the calculation of Young’s modulus from a strain image is quite complex
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 11. Lesion size differential. grayscale ultrasound (right) and strain elastogram (left) of an invasive ductal carcinoma. Note the larger size of the lesion on the elastogram compared
with the hypoechoic lesion on the sonogram. If one includes the echogenic surrounding material on the sonogram in the size estimate, the difference is smaller but still present.
requiring a model of the material including its shape and knowledge of the boundary conditions under which the measurement was taken. Also, putting a reliable pressure sensor on an ultrasound transducer without degrading ultrasound imaging performance or transducer ergonomics has been challenging. For these reasons progress has been slow on developing clinically useful ways of estimating Young’s modulus from strain data (the so called ‘‘inverse problem’’). Steady progress is being made however [16, 17] and reasonable Young’s modulus images from strain elastograms are now possible (Fig. 12). The second approach is to obtain semi-quantitative results by taking the ratio of the strain measured in an area of interest to that from tissue elsewhere in the image that is presumed to be normal and for which strain values are relatively stable. This ‘‘strain ratio’’ approach has produced better results for cancer diagnosis than using a subjective lesion grading scheme in the breast [18–20], in lymph nodes [21], in thyroid [22–24], and in the prostate [25]. Several elastographic systems now offer strain ratio calculation and others will likely follow suit (Fig. 13). Even though strain ratios have been shown to be useful in the literature, experience in clinical practice has been variable. Much of this could be due to using poor quality strain elastograms to measure strain ratios but another factor could be the lack of guidance from manufacturers on how to select images for high quality strain ratio calculation and how to pick regions of interest to make the calculation. Recently work has begun at the FDA on modeling some of the factors that
may degrade the quality of strain ratio estimates. Work using simple phantoms and phantom models has shown that the location of the ‘‘normal’’ tissue ROI can greatly affect the strain ratio value [26]. This is due to distortion of the strain values in surrounding tissue caused by the presence of a lesion. Both distance from the lesion of interest and the direction of placement of the comparison ROI are important (Fig. 14). Work using more complex finite element models and tissue mimicking phantoms is underway.
Strain elastogram quality The quality of a strain elastogram can vary greatly from acquisition to acquisition. Even during an acquisition (one or more compression-relaxation cycles) the quality varies between compression and relaxation and between mid-compression and end compression (Fig. 15). Generally a high quality elastogram will be derived from high quality displacement data and have a smooth background and high lesion contrast if a lesion is present. A low quality elastogram will have a background filled with small high and low brightness foci (Fig. 16) denoting poor strain estimates which can be recognized on a correlation coefficient image as generalized low correlation values in the image. Lesion visibility is not a good indicator of a high quality elastogram because the lesion may be quite visible in an extremely noisy elastogram (Fig. 17). Because it is often difficult for a reader to tell which of the hundreds of images obtained during several
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 12. Phantom images (A) barely show the Isoechoic lesion on b-mode ultrasound. On the strain image (s) and on the Young’s modulus image (Y) the lesion stands out clearly. Note that the Young’s modulus image is calibrated. The reconstructed Young’s modulus image (B) of an in vivo invasive ductal breast cancer shows high resolution (200 lm) similar to a sonogram. This allows the texture of the cancer to be analyzed. Shear wave elastograms are also calibrated but
are much lower in spatial resolution. An image of the nonlinear stiffening parameter (C) shows the lesion clearly also. This non-linear parameter may be a useful feature for cancer diagnosis as cancers become stiffer with precompression much more rapidly than do benign tissues. The phantom images are from Pan et al. [40] with permission and the cancer images are courtesy of Assad Oberai and Paul Barbone.
acquisition cycles are good, an indicator is usually provided on-screen to identify good and poor elastograms (Fig. 18). The algorithms and criteria that are used to determine which images are of high quality vary among manufacturers which could lead to some interpretation variability. When strain elastography became widely available and many articles were published on the usefulness of the technique, many others purchased systems and got less impressive results, probably because they did not receive adequate training and practice. The basic technique is to apply several cycles of gentle compression—relaxation using the transducer to compress about 2–5 mm on each cycle. The technique is simple in theory but is quite different from the technique used for sonography. Some tips for producing high quality strain elastograms are:
downward compressions without sideways transducer motion are achieved. This is a critical step. The compression should be at a constant rate as nearly vertical to the skin as possible with no sliding either along the transducer long axis or perpendicular to the long axis. Sliding motions result in poor quality, noisy elastograms. 2. Start with the transducer barely touching the skin. One common problem is that the operator begins with the tissue already compressed because tissue compression usually leads to higher quality sonograms. When tissue is already compressed prior to making an elastogram the tissue is already stiffer than it normally would be which reduces the contrast between the lesion and the surrounding tissue. Also precompression can make the tissue so stiff that lesions tend to slide sideways away from the transducer further degrading
1. Support your forearm so that very controllable
B.S. Garra: Elastography: history, principles, and technique comparison
tually no compression whereas others require more. During a compression—relaxation cycle, good elastograms may be achieved during both compression and relaxation. In fact, many systems tend to produce better elastograms during the relaxation phase of the cycle. 5. Watch the b-mode scan during compression and relaxation for sideways movement. As noted previously, sideways movement seriously degrades strain elastograms. Sideways movement can occur if too much precompression is applied, if the transducer face is tilted with respect to the skin or underlying structure against which compression is applied or if the surface against which compression is applied is curved (for example if compression is applied to muscle overlying a bone). If sideways movement is observed then a slight downward tilt of the transducer on the side toward which the tissue is moving will usually reduce or eliminate the unwanted motion. If the tissue appears to be changing but not compressing or moving sideways, then it may be moving out of the imaging plane (perpendicular to the transducer). This movement is extremely detrimental to good elastography. Sometimes changing the patient position or the plane of imaging will reduce or eliminate this problem.
Fig. 13. Strain ratios. A Quantitative stiffness estimation in the Gastrocnemius muscle using the calibrated standoff pad method. Hard and soft standoff pads are both included to cover the expected range of stiffness values within the muscle. From: Chino et al. [41]. B Strain ratio calculation for a breast mass compared to breast fat. Image from Hitachi web site: http://www.hitachi-medical-systems.eu/products-andservices/ultrasound/clinical-applications/clinical-applicationsfor-hitachi-platforms/internal-medicine.html#Clinical-Images-4. Accessed 27 October 2014.
the images. To achieve minimal precompression one must start in full contact with the skin then back the transducer away so that the skin is pulled slightly upward by suction of the skin against the transducer face before beginning the compression-relaxation imaging cycle. 3. Use a thin gel. Thick gels are relatively stiff compared to some body tissues and may result in unwanted precompression if used. If a thicker gel must be used then the method outlined above to remove precompression becomes a necessity. 4. Find the correct rate of compression. Each system will have a slightly different compression rate that produces the best quality elastograms. Some require vir-
The final step in improving and maintaining elastography quality is the need to follow accepted standards for acquisition, display and interpretation. As mentioned previously, the first standard method for interpretation was the Tsukuba classification scheme for breast lesions. This same standard was later applied to other organs as a way to classify lesions as stiff vs soft lesions and has met with some success in lymph nodes and thyroid nodules [21, 24, 27]. The European Union (EU) has since published standards for acquisition of both strain and shear wave elastograms [28, 29].
Elastography using dynamic techniques Dynamic Elastography Currently Encompasses Two Major Methods, sonoelasticity imaging—crawling wave elastography and shear wave elastography. Shear wave elastography has become the dominant method by far, surpassing even strain elastography in many situations, perhaps because it does not require an external vibration device which is seen by most as too inconvenient and time consuming to set up in a clinical environment. For this reason the focus of the remainder of this section will be on shear wave elastography. Shear wave elastography involves the estimation of shear wave speed which is high in stiff tissues and low in soft tissues. Normal acoustic waves are compressional
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 14. Background Strain Distortion. A finite element model (FEM) of a hard lesion in a softer surrounding material (upper left) shows distortion of the strain values in the background material by the presence of the lesion or mass. The background material should be a uniform medium gray. An ultrasound strain simulation using the same FEM shows the same background errors (upper right). A real gel phantom with a hard inclusion is imaged using an ultrasound system (lower left). The strain elastogram of the phantom shows the same background strain distortions (lower right).
(longitudinal) waves that travel at high speeds through tissue (1450–1550 m/s). These waves also travel at higher speed through stiff materials than they do in soft materials, but they are difficult to track using ultrasonic methods. Shear waves on the other hand are transverse waves that propagate much more slowly (usually 1– 10 m/s) in tissue. The waves can be generated by mechanical means or can be generated at the margins of an acoustic (compressional) wave similar to an ARFI pulse as it traverses tissue (Fig. 19A, B). A shear wave generated from an acoustic wave travels perpendicular to the acoustic wave and can be tracked using multiple tracking pulses emitted lateral to the original pushing acoustic pulse to monitor tissue displacement as the shear wave passes (Fig. 19C). Shear wave speed (SWS) can be converted to elastic (Young’s) modulus using the simple equation: E ¼ 3qV2 where E is Young’s modulus, q is tissue density (often assumed to be 1.0) and V is the shear wave speed. The constant ‘‘3’’ relates the shear modulus to the Young’s modulus for isotropic homogeneous materials. Most clinical systems compute the Young’s modulus using the above equation, and warnings are present in either the
manual or on-screen about the assumptions being made when it is calculated. For heterogeneous and/or anisotropic materials (usually those having micro or macroscopic structural alignment in one or more directions) such as skeletal muscle, or renal medulla, one can simply use the SWS which is a reliable surrogate for tissue stiffness. In these materials the SWS will be different in the direction aligned with the structures as opposed to a direction of propagation perpendicular to the aligned structures, for example along muscle fibers vs. across the fibers. A shear wave speed image is usually presented as a color overlay on a grayscale b-mode image with either red or blue denoting stiff tissues. A color bar at the side of the image will relate the colors to either shear wave speed or calculated Young’s modulus and usually the manufacturer will identify which colors represent stiff and which represent soft. Since the image is quantitative the operator may place a cursor or define a region of interest and obtain the average SWS or Young’s modulus estimate from that location (Fig. 20). Additional factors that may affect the SWS and Young’s modulus estimates include the distance from the transducer which may result from errors in the SWS estimation algorithm, especially for convex or other fan
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 15. Variation in image quality during a compression-decompression cycle. A Good quality elastogram during a strain elastography compression cycle. B Poor quality elastogram from the same cycle.
beam transducers, the amount of precompression applied, tissue heterogeneity which may produce malformed shear waves, and tissue attenuation which may cause the shear waves to be too weak to measure accurately. Manufacturers generally have an on-screen display that indicates poor SWS estimates such as giving an error indication when estimates are out of the expected range of realistic values but errors in estimates within the expected range often go undetected. The user must be aware of the fact that SWS estimation is in an early stage and there is always the possibility of error in SWS images and numerical estimates.
A related problem may occur in focal lesions where erroneous estimates of SWS appear within the lesion. This problem has been noted in breast cancers and appears as low SWS values in lesions that are clearly cancerous on b-mode imaging and strain elastography. This finding was initially attributed to tissue necrosis at the tumor center but in many cases tissue necrosis was not present on histological examination. The cause of the apparent error in SWS is not entirely clear but may be due to reflected shear waves at lesion boundary partially canceling the shear waves emanating from the ARFI pulse causing the shear wave speed algorithm to fail. This
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 16. Correlation coefficients. A High quality elastogram with high correlation coefficients (white in correlation coefficient map). B Poor quality elastogram with low correlation coefficients (dark on correlation coefficient map).
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 17. Lesion visibility in a noisy image. The sonogram (right) shows a suspicious lesion (circled). The elastogram on the left shows the corresponding lesion despite the fact that
most of the image is very poor having the speckled appearance of multiple cross-correlation failures.
is a serious error because it might cause a cancerous lesion to be incorrectly classified as benign. Usually a few high SWS foci are seen at the periphery of the lesion or immediately adjacent to the lesion (Fig. 21) to help identify the nature of the lesion. This is an example where strain elastography is useful to aid in correct classification of a lesion since it is less affected by tissue heterogeneity and boundaries resulting from a focal lesion.
acquired. The quantitative imaging biomarker alliance (QIBA) is an initiative involving representatives from academia, industry and government formed by the radiological society of America (RSNA) to promote the accurate estimation of quantitative biomarkers derived from imaging for use in clinical practice and research. It includes biomarkers such as CT estimation of lung lesion size and was recently extended to include SWS estimation in the liver. Equipment parameters, acquisition techniques and data reduction are carefully specified to achieve specified levels bias and variance. The QIBA SWS committee is examining all potential sources of bias and variance in an effort to minimize variability to improve diagnostic accuracy of shear wave elastography for liver fibrosis assessment [31]. One important source of variability between scanners is the fact that SWS depends on the frequency of the shear wave which varies from manufacturer to manufacturer. This speed dependence on frequency is due to the viscosity of tissue which affects the propagation of shear waves and is unaccounted for when using only Young’s modulus (related to the storage modulus in a viscoelastic model of materials) to characterize tissues. Whether the viscosity component (known as the loss modulus in a viscoelastic model) is useful for characterizing abnormities in tissue is so far unknown. The QIBA SWS committee is in the process of testing commercial scanners with viscoelastic phantoms to develop strategies for relating SWS estimates at one frequency with those taken at another.
Shear wave elastogram quality As mentioned above, manufacturers of display an error message or indicator when shear wave speed estimates are out of range. For images a single indicator does not show where incorrect values are present so companies may flag poor estimates with a special color, or use black to indicate unreliable values. Other indicators may include a second image showing where poor estimates are likely. The evaluation of SWS estimates for errors and how to display them is still a work in progress and improvements in this area are likely. Just as in strain elastography, proper acquisition technique is critical for accurate and high quality images. All manufacturers discuss the need to apply very light pressure to avoid precompression of tissues and all discuss the need to acquire at a standard depth to avoid variability from one exam to the next due to depth induced variation. As in strain elastography, national [30] and international guidelines [28, 29] are emerging that will help to standardize how shear wave elastograms are
B.S. Garra: Elastography: history, principles, and technique comparison
B.S. Garra: Elastography: history, principles, and technique comparison
Quality indicators. On-screen elastogram quality indicators are outlined by yellow circles. The most common indicators are bar or pie chart based where the longer the bar or the more pie sections are filled in, the better the quality of the elastogram. The variables that go into the quality estimation vary but usually involve correlation coefficients to estimate the quality of the tissue displacement estimates, and average strain level in the image.
b Fig. 18.
Application to abdominal diseases Elastography in one form or another has been tried in most intra-abdominal diseases with varying success. In the liver, diffuse liver disease, especially fibrosis, has been studied extensively as has the prostate. Focal liver lesions including hepatocellular carcinoma (HCC) and cholan-
Fig. 19. Longitudinal or compressional waves and shear waves. A Longitudinal (compressional) wave. The particles making up the material move along the same axis as the direction of propagation. B Transverse wave (shear wave). The particles making up the material move along an axis perpendicular to the direction of propagation. C Creation and tracking of shear waves. The ultrasound transducer emits a beam consisting of a series of compressional wave pulses which push the tissue away from the transducer and cause shear waves to be generated at the margins of the push beam. The shear waves travel away from the push pulse beam and are tracked by lower power tracking pulses also emitted by the ultrasound transducer. In this way the speed of the shear wave can be estimated.
B.S. Garra: Elastography: history, principles, and technique comparison
Fig. 20. Shear wave speed image. A region of interest (ROI) has been placed to obtain average or median shear wave speed in that location. From the Supersonic Imagine web site: http://www.supersonicimagine.com/var/ezwebin_site/storage/ images/aixplorer-r/general-imaging/pediatrics/smc12-3-liverfibrosis-on-a-21d-old-child-aixplorer-supersonic-imagine/71914-eng-GB/SMC12-3-Liver-fibrosis-on-a-21d-old-child-AixplorerSuperSonic-Imagine.jpg. Accessed 10-26-2014.
giocarcinoma have also been studied but studies have shown considerable overlap between HCC stiffness values and those of benign lesions [32, 33]. The spleen has been studied primarily as a means to detect worsening portal hypertension and varices [34]. The pancreas has been studied primarily by means of endoscopic ultrasound elastography (strain elastography) and this form of elastography has also been used to study ulcerative colitis and Crohn’s disease. Kidney elastography has been successfully performed in patients with diffuse renal disease, but the results do not clearly show a correlation with extent of diffuse renal disease [35]. Limited work has also been performed on focal kidney lesions [36]. Intraabdominal lymph nodes have primarily been studied using endoscopic ultrasound with promising results [37]. Finally the uterus and adnexa have been studied looking for changes in uterine stiffness during pregnancy and for evaluation of fibroids, adenomyosis [38] and ovarian tumors [39].
Fig. 21. Breast cancer with low apparent SWS in a stiff (hard) Lesion. Hypoechoic invasive ductal cancer (yellow arrows) corresponds to blue (soft) tissue on SWS image but such cancers are typically very stiff. Reflection of the shear waves at lesion margins is one proposed cause of the erroneous SWS values. Taking the stiffness estimate at the lesion margins where high SWS may be found helps to correctly classify the lesion. Strain elastography may also be performed to confirm a uniformly stiff lesion.
Comparison of strain and shear elastography for abdominal applications Table 1 shows a rough comparison of strain elastography with shear wave elastography for abdominal applications. The choice of elastography may depend on the specific intended application and since the capability of each method is rapidly evolving, the choice may change
B.S. Garra: Elastography: history, principles, and technique comparison
Table 1. Comparison of strain and shear wave elastography Attribute
Strain elastography
Shear wave elastography
Availability
Widely available, many manufacturers and models
Image quality
Excellent if properly performed
Real time imaging
Yes
Maximum depth Quantification
Good. Depends on force and displacement applied Limited (strain ratios). Elastic modulus reconstructions generally not yet available Better for focal than diffuse. Since image is not calibrated diffuse stiffness may appear the same on an image as diffuse softness Considerable. Hands on training usually required
Limited availability but improving. Only five FDA cleared systems Good to excellent. All systems limit the color elastogram to a region of interest of variable size No. Color elastograms are only obtainable at a very low frame rate Limited. Typically 6 cm or less for good quality Excellent. Quantification from a region of interest and color display is calibrated Better for diffuse than focal. Algorithms and computations are designed for diffuse diseases. May give erroneous values in focal lesions Limited if operator already knows sonography. But operator must carefully follow instructions for accurate elasticity estimates
Diffuse/focal disease Operator dependence
over time. At the present time diffuse organ diseases may be best evaluated using shear wave elastography which has quantification built in and thus can give some sort of global numerical estimate of stiffness. Focal lesions may be evaluated using both methods and to avoid errors in either method, the best course may be to perform both types of elastography. Progress toward quantification of strain elastography may allow this method to better evaluate diffuse disease, particularly for depths greater than what can be achieved using shear wave elastography (about 6 cm). On the other hand, improvements in ARFI and shear wave speed estimators may increase the depth at which SWS measurement can be performed. Both methods have the potential to be valuable contributors to diagnosis of many of the diseases encountered in the abdomen and retroperitoneum. References 1. Allen JP (2005) The art of medicine in ancient egypt. New York: The Metropolitan Museum of Art, p 70 2. Breasted JH (1991) The Edwin Smith surgical papyrus: published in facsimile and hieroglyphic transliteration with translation and commentary in two volumes. Chicago: University of Chicago Press, p 9 3. Huangdi Nijing, Wikipedia entry. http://en.wikipedia.org/wiki/ Huangdi_Neijing#cite_note-wdl-1. Accessed 10 April 2014 4. Fu KL, Fu YS, Bassett LW, Cardall LW, Lopen JK (2005) Invasive malignancies. In: Bassett LW (ed) Diagnosis of diseases of the breast, 2nd edn. Philadelphia: Saunders 5. Kelly KM (1996) Breast ultrasound. Crit Rev Diagn Imaging 37:79–161 6. Tristam M, Barbosa DC, Cosgrove DO, et al. (1986) Ultrasonic study of in vivo kinetic characteristics of human tissues. Ultrasound Med Biol 12:927–937 7. Tristam M, Barbosa DC, Cosgrove DO, Bamber JC, Hill CR (1988) Application of Fourier analysis to clinical study of patterns of tissue movement. Ultrasound Med. Biol. 14:695–707 8. Lerner RM, Parker KJ, Holen J, Gramiak R, Waag RC (1988) Sonoelasticity: medical elasticity images derived from ultrasound signals in mechanically vibrated targets. Acoust Imaging 16:317–327 9. Parker KJ, Huang SR, Musulin RA, Lerner RM (1990) Tissue response to mechanical vibrations for ‘‘sonoelasticity imaging’’. Ultrasound Med Biol 16:241–246 10. Wu Z, Taylor LS, Ruben DJ, Parker KJ (2004) Sonoelastographic imaging of interference patterns for estimation of the shear velocity of homogeneous biomaterials. Phys Med Biol 49:911–922
11. Ophir J, Ce´spedes I, Ponnekanti H, Yazdi Y, Li X (1991) Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging. 13(2):111–134 12. Garra BS, Ophir J, Spratt SR, et al. (1997) Elastography of breast lesions: initial clinical results. Radiology 202:79–86 13. Sandrin L, Fourquet B, Hasquenoph JM, et al. (2003) Transient elastography: a new noninvasive method for assessment of hepatic fibrosis. Ultrasound Med Biol 29:1705–1713 14. Itoh A, Ueno E, Tohno E, et al. (2006) Breast disease: clinical application of US elastography for diagnosis. Radiology 239:341– 350 15. Nightingale K, Soo MS, Nightingale R, Trahey G (2002) Acoustic radiation force impulse imaging: in vivo demonstration of clinical feasibility. Ultrasound Med Biol. 28(2):227–235 16. Goenezen S, Dord JF, Sink J, et al. (2012) Linear and nonlinear elastic modulus imaging: an application to breast cancer diagnosis. IEEE Trans Med Imaging 31(8):1628–1637 17. Yan Z, Zhang S, Alam SK, et al. (2012) Modulus reconstruction from prostate ultrasound images using finite element modeling. In: Proceedings of SPIE 8320, medical imaging 2012: ultrasonic imaging, tomography, and therapy, 832016. doi:10.1117/12.911088 18. Zhi H, Xiao X-Y, Yang H-Y, et al. (2010) Ultrasonic elastography in breast cancer diagnosis: strain ratio vs 5-point scale. Acad Radiol 17(10):1227–1233 19. Cho N, Moon WK, Kim HY, et al. (2010) Sonoelastographic strain index for differentiation of benign and malignant nonpalpable breast masses. J Ultrasound Med 29(1):1–7 20. Gheonea IA, Stoica Z, Bondari S (2011) Differential diagnosis of breast lesions using ultrasound elastography. Indian J Radiol Imaging 21(4):301–305 21. Ying L, Hou Y, Zheng H-M, et al. (2012) Real-time elastography for the differentiation of benign and malignant superficial lymph nodes: a meta-analysis. Eur J Radiol 81(10):2576–2584 22. Lyshchik A, Higashi T, Asato R, et al. (2005) Thyroid gland tumor diagnosis at US elastography. Radiology 237(1):202–211 23. Bae U, Dighe M, Dubinsky T, et al. (2007) Ultrasound thyroid elastography using carotid artery pulsation: preliminary study. J Ultrasound Med 26(6):797–805 24. Wang H, Brylka D, Sun LN, et al. (2013) Comparison of strain ratio with elastography score system in differentiating malignant from benign thyroid nodules. Clin Imaging 37(1):50–55 25. Zhang Y, Tang J, Li YM, et al. (2012) Differentiation of prostate cancer from benign lesions using strain index of transrectal realtime tissue elastography. Eur J Radiol 81:857–862 26. Jia C, Alam SK, Zahiri Azar R, Garra B (2014) Estimation of shear modulus ratio between inclusion and background using strain ratios in 2D ultrasound elastography. IEEE Trans UFFC 61(4):611– 619 27. Moon HJ, Sung JM, Kim EK, et al. (2012) Diagnostic performance of gray-scale US and elastography in solid thyroid nodules. Radiology 262(3):1002–1013
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28. Bamber J, Cosgrove D, Dietrich CF, et al. (2013) EFSUMB gidelines and recommendations on the clinical use of ultrasound elastography. Part I: basic principles and technology. Ultraschall Med 34:169–184 29. Cosgrove D, Piscaglia F, Bamber J, et al. (2013) EFSUMB gidelines and recommendations on the clinical use of ultrasound elastography. Part II: clinical applications. Ultraschall Med 34:238–253 30. Society of Radiologists in Ultrasound (2014) Consensus conference on ultrasound elastography for assessment of diffuse liver disease, Denver, CO, 21–22 October 2014 31. Hall TJ, Milkowski A, Garra B, et al. (2013) IEEE international ultrasonics symposium (IUS) on RSNA/QIBA: shear wave speed as a biomarker for liver fibrosis staging, pp 397, 400, 21–25 July 2013 32. Onur MR, Poyraz AK, Ucak EE, et al. (2012) Semiquantitative strain elastography of liver masses. J Ultrasound Med 31(7):1061– 1067 33. Guibal A, Boularan C, Bruce M, et al. (2013) Evaluation of shearwave elastography for the characterisation of focal liver lesions on ultrasound. Eur Radiol 23(4):1138–1149 34. Calvaruso V, Bronte F, Conte E, et al. (2013) Modified spleen stiffness measurement by transient elastography is associated with presence of large oesophageal varices in patients with compensated hepatitis C virus cirrhosis. J Viral Hepat 20:867–874
35. Asano K, Ogata A, Tanaka K, et al. (2014) Acoustic radiation force impulse elastography of the kidneys: is shear wave velocity affected by tissue fibrosis or renal blood flow? J Ultrasound Med 33:793–801 36. Tan S, Ozcan MF, Tezcan F, et al. (2013) Real-time elastography for distinguishing angiomyolipoma from renal cell carcinoma: preliminary observations. Am J Roentgenol. 200:369–375 37. Xu W, Shi J, Zeng X, et al. (2011) EUS elastography for the differentiation of benign and malignant lymph nodes: a meta-analysis. GI Endosc 74:1001–1009 38. Stoelinga B, Hehenkamp WJK, Brolmann HAM, Huirne JAF (2014) Real-time elastography for assessment of uterine disorders. Ultrasound Obstet Gynecol 43(2):218–226 39. Xie M, Zhang X, Zhan J, Hua K (2013) Application of real-time ultrasound elastography for discrimination of low- and high-grade serous ovarian carcinoma. J Ultrasound Med 32:257–262 40. Pan X, et al. (2014) A regularization-free elasticity reconstruction method for ultrasound elastography with freehand scan. Biomed Eng 13:132 41. Chino K, Akagi R, Dohi M, Fukashiro S, Takahashi H (2012) Reliability and validity of quantifying absolute muscle hardness using ultrasound elastography. PLoS ONE 7(9):e45764. doi: 10.1371/journal.pone.0045764
