Ultrasound Elasography: from Physical Principles to Computer-Aided Image Analysis and Quantification

Sorin M. Dudea 1, Sergiu Nedevschi 2, Cosmin Pantilie 2, Carolina Botar-Jid 1, Dana Dumitriu 1, Tiberiu Marita 2

1 - UMF Iuliu Hatieganu Cluj-Napoca 2 – UTCN

Abstract

Elastography is a new development in the field of

diagnostic ultrasound. The aim of the paper is to present the fundamental physics of the method, its advantages and limitations and the concept underlying a software product developed for improving the efficiency of the technique.

The method, as it is commercially available, uses speckle tracking to provide information on tissue strain. The specific information is displayed in color hues superimposed on a grayscale sonographic image. The extremely high motion sensitivity of the technique, although extremely useful, induces diagnostic limitations, to the point where the method has only qualitative value but no real quantification means.

To bypass some of the limitations, a software product has been developed with the specific aim to provide quantification means by image analysis. In this product, hue analysis is combined with area tracking, continuous area recognition and dynamic analysis of motion sequences.

1. Introduction

The assessment of mechanical tissue properties through palpation has been used in medicine, for ages. The main property assessed is elasticity or, as an opposite, tissue stiffness. Although information obtained by palpation has always been considered of extreme value, it is, by its nature, largely subjective and nonreproducible.

Pathologic changes in tissue structure are, by large, associated with changes in stiffness. Typically, cancer nodules are stiff while fat is elastic. On the other hand, many disease processes, although altering tissue structure and stiffness, are not accompanied by specific changes of the corresponding ultrasonographic image [1, 2].

The last 20 years witnessed tremendous efforts of the scientific community aimed at developing imaging methods for the visual characterization of tissue elasticity. Nowadays, the technique commercially available on ultrasound medical scanners is called elastography.

The purpose of this paper is to present the physical principles of elastography, to review its advantages and limitations and to introduce concepts of image analysis aimed at surpassing some of the method’s drawbacks. 2. Physical principles of ultrasound elastography

The elastographic information is obtained by applying longitudinal pressure over an area of the body and measuring, on the corresponding ultrasound image, the displacement of speckle nuclei in the tissue, as a consequence of the applied external pressure [1, 2].

Essentially, elastography implies two distinct steps: tissue excitation and information analysis, to generate the specific image [3].

Excitation is a dynamic process, achieved by permanent vibration of the ultrasound transducer, applied with the “free-hand” technique. External excitation applies the deforming force on the surface of the skin, aiming to displace the underlying tissues. Unfortunately, free-hand technique is associated with transducer translation and lateral displacement becomes significant [4].

The information analysis is based on the assumption that a stiff area in the tissue will undergo less strain than elastic one. Observing the displacement of individual tissue speckle units allows the assessment of the distribution of tissue strain (figure 1). Therefore, elastography produces a strain profile of the tissues. The method measures local axial tissue strain changes

according to the depth, as a response to an external axial compressive stimulus (figure 2).

Figure 1. Depiction of tissue strain under longitudinal

pressure, using the model of a set of springs with different stiffness. At rest (a), the two elastic springs (1 and 3) have the same size as the stiff spring (2). After

longitudinal compression (b), the whole set of springs is displaced but, while the elastic springs undergo major shape changes, the stiff spring is almost completely

unchanged

Figure 2. The strain prophile of the spring set illustrated

in figure 1. a) The change of size during compression shows that the elastic springs, located in the extremities, are deformed to a much higher degree than the central, stiff spring. The strain prophile (b) shows less percentile

strain of the central spring The strain profile can be determined along many

neighboring ultrasound axes and a two-dimensional image of tissue strain is achieved. Elasticity defines a basic tissue property and is, therefore, a more representative tissue parameter, when compared to strain profile. The strain profile is, therefore, converted into a profile of the elastic modulus [1].

The machine records the radiofrequency (RF) waves before and after the application of the deforming stimulus and assesses the longitudinal tissue displacement by tracking the speckle nuclei, using autocorrelation techniques. Congruent segments of the

pre- and postcompression RF waves are compared by cross-correlation. The time difference for the occurrence of the segment with the same RF signature is computed. To achieve this, the RF line is divided into equal finite length segments, called windows. Each precompression window is compared with its postcompression pair and displacement is calculated. Knowing the displacement of all the windows, differential displacement of successive windows can be computed. The deformity image is obtained after normalization for length and window overlapping [1, 2]. The elastogram is based on the differences that appear, in time, between segments of the RF curve, as they are obviated by cross correlation analysis, using the fast Fourier transformation (figures 3 and 4).

Figure 3. Appearance of the RF curve before and during compression. Information obtained along a single line in

space

Figure 4. Identification of movement with the

autocorrelation technique. The electronic signature of the objects is identified on the RF curves obtained before

and during compression. a) a stiff object undergoes little displacement and neglectable strain; b) an elastic object undergoes greater displacement and extensive strain, as

shown by the “wind box” squeezing of its signal.

The texture of the elastographic image is more uniform than the one of the two-dimensional image, as there is no noise. The lateral resolution of the image is very good since it is not distorted by the divergence of the ultrasound beam [5].

The combined autocorrelation method described by Shiina and coworkers [6] allows for quick detection of longitudinal displacement by processing the signal phase, without aliasing, as it can be applied to large tissue displacement. The method is optimized for the compensation of lateral tissue displacement and produces high quality, real-time images (fig. 5).

Figure 5. Ultrasound elastography of the breast.

Recently, Itoh and coworkers [4] described the

detailed technique for performing an elastographic examination. General-purpose ultrasound transducers are used, coupled with a machine capable of producing the specific software analysis necessary for obtaining the elastographic image. Light pressure is applied on the transducer. Tissue displacement should fall in the range of 0,4- 0,8 mm, with a frequency of 2 Hz.

The representation of tissue elasticity is achieved by color coding. Each pixel in the region of interest (ROI) has allotted a color, according to the magnitude of displacement. Red corresponds to the areas with the greatest displacement (elastic tissue), while blue is allotted to the areas with the least strain (stiff tissue). Green is indicative for medium strain. The color image may be superimposed on the grayscale information of in may be represented next to it (split screen).

3. The advantages of elastography

The advantages of elastography are mainly related to the fact that it depicts, visually, tissue elasticity, a property that has been subjectively assessed for a long time but that had no visual match, being entirely related to palpation, the most subjective human sense. Lesions are detected on the two-dimensional image and thereafter characterized with elastography or the method may obviate lesions that are “invisible” for the two-dimensional technique (fig. 6) [3].

Figure 6. Elastographic identification of lesions that

cannot be seen on grayscale images. Phantom images. On the right half of the image, the lesion is not seen on the grayscale image due to similar acoustic impedance. On the left half of the image, the same lesion is clearly

seen with elastography, due to its greater stiffness when compared with the surrounding structures.

4. Limitations of the method

The lack of definition of the magnitude and direction of the applied force leads to severe limitation of the method’s capacity to deliver quantitative information regarding tissue elasticity [1,2]. Manual driving of the transducer leads to a great variability in the force and frequency of the applied displacement. Color encoding provides only qualitative information on elasticity, with no real means of quantification. Therefore, the greatest drawbacks of elastography, as it is available today, are the lack of standardization in terms of the applied driving force and the impossibility to use emerging data for a quantitative diagnosis. This last issue is addressed by the work in progress described below. 5. Improving the efficiency of the technique

In order to bypass some of the limitations of the technique, efforts are made in two directions:

1. Developing a standard, automated, method of driving the transducer, to attenuate the effects of subjective manipulation of the probe (different operators would obtain slightly different results for the same subject);

2. Delivering a software product capable to quantify elasticity, as it is represented by colors, in order to bypass the subjective way the human eye perceives colors. Another objective of the software product is to provide a reproducible way to characterize lesions with complex visual appearance on the elastographic image.

5.1 Application and facilities

An application was developed with the specific aim of providing the means of quantification of the tissue elasticity. The input images have the format shown in figure 7, where the elastographic information (left) is superimposed on a copy of the grayscale echographic image (right). This application, called ElastoBreast, provides a series of features, useful in the process of evaluating the type, size, and elasticity, or the opposite – the stiffness, of the underlying tissue:

The possibility of defining a fixed shape and size selection (rectangular) or free form selection on the image, in order to apply processes or compute values of interest only for that particular selection;

The possibility of duplicating a ROI on the image;

Color texture analysis by assigning standardized numeric values to the pixels in the region of interest, according to their color;

Classifying a region of interest by its content; Computation of an elasticity ratio between

two selection regions – one considered to be pathologic and the second normal;

The possibility of applying single processes or a list of processes on individual images or on motion sequences;

The application facilitates the identification, classification and analysis of lesions in the breast tissue. Some lesions can be observed on the echo graphic image while others are obvious only on elastography. The user is able to select any region of interest of any form and duplicate (mirror) it (figure 7). Afterwards, numeric information can be provided, characterizing the region of interest according to its content or processes such as filters, enhancements or lists of processes can be applied on that selection. Furthermore when dealing with motion sequences the selection in one frame is used when processing the other frames.

5.2 Quantification of the elasticity from the color content of the image

In most of today’s commercially available devices, the elastographic information is encoded with different colors ranging from red – soft or elastic tissue, to dark blue – stiff tissue. Despite the efforts, an important amount of information is lost because of the human’s eye color perception limits. For this reason the current approach provides only a qualitative assessment.

Figure 7. Mirroring the selection from the grayscale image to the elastographic image. The selection was

made on the left hand side of the screen, on the grayscale image and mirrored on the elastographic

image.

To overcome this serious limitation, we propose an interpretation of the image content based on the hue component of each pixel. The approach is to decode the required information by hue analysis of the image. Most of the commercially available medical scanners offer 24 bit RGB compressed/uncompressed images.

The RGB color model is an additive model in which each color is obtained as a combination between different values of the three basic colors: red, green and blue. The RGB color space can be depicted as a cube (figure 8) with red, green and blue placed in opposed corners.

Figure 8. The RGB color space.

In a 24 bit representation each of the three color

components: red, green and blue is encoded on 8 bits thus having values ranging from 0 to 255 (28 combinations). In this color system 16.777.216 (=224) discrete combinations can be specified, though not necessarily distinguished. In order to obtain information regarding the elasticity of the tissue, a conversion to another color model has to be performed: HSL (Hue, Saturation,

Luminance/Intensity). The HSL color model can be represented as a double cone or a double hexcone (figure 9). The two apexes of the HSL double hexcone map to black and white while the angular parameter corresponds to hue. The distance from the axis corresponds to saturation, and distance along the black-white axis corresponds to lightness. The HSL model has the advantage over RGB of being closer to the way the human eye perceives the color.

In order to obtain the desired information the conversion is done in two steps. First the RGB values are normalized:

BGRBb

BGRGg

BGRRr

++=

++=

++= ,,

Second the normalized hue component is obtained: ( ) ( )[ ]

( ) ( )( )[ ]gbforh

bgbrgr

brgrh

≤Π∈

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−−+−

−+−⋅= −

],0[

,5.0cos 2/12

1

( ) ( )[ ]( ) ( )( )[ ]gbforh

bgbrgr

brgrh

>Π∈

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−−+−

−+−⋅−∏= −

],0[

,5.0cos2 2/12

1

The calculated hue component has, theoretically, a

range from 0 to 2π in radians, or 0 to 360 in degrees. This interval maps to the color range red to violet. In practice values close to 2π will not be encountered, since this value corresponds to violet while dark blue encodes maximum stiffness. For this reason each color from the elastographic image maps to a precise numeric value which allows for a quantitative characterization: 0 – very soft and approximately 270 - very hard.

Computing the saturation or luminance component is not of interest, because they don’t describe to any physical property of the human tissue. By computing the hue component a numeric value characterizing the elasticity can be computed for every pixel of the image or only for those pixels in a region of interest. This way lesions can be quantitatively described by computing first and second order statistics.

5.3 Computation of the elasticity ratio

For obtaining a better diagnostic value, in practice, it is often of interest to compute a ratio between two samples of tissue from distinct regions. Two regions can be compared in order to compute a relative elasticity:

1

2

R

Rrel E

EE = ,

where Erel represents the relative elasticity of the two regions, ER2, ER1 represent the mean elasticity over the evaluated respectively the reference regions.

Figure 9. The HSL color space.

5.4 Lesion segmentation method

Due to the inhomogeneity of the structure of biologic tissues, the distribution of stiffness may be random. Therefore lesions with a central stiff nucleus and lesions with a patchy distribution of smaller stiff areas may finally produce identical or similar mean values. This justifies the need of a complex description of the area of interest based on the identification of relevant homogenous regions and their numeric characterization. There are five types of lesions which need to be identified (figure 10) [4].

Figure 10. Different lesion types

For characterizing complex structures such as the

one in figure 10b an automatic segmentation method for medical images is used. The result of image segmentation is a set of regions that collectively cover the entire image. Each of the pixels in a region has a close hue value. There are several methods for segmentation. A histogram based method was implemented. Histogram based methods are efficient when compared to other image segmentation methods because they typically require only one pass through the pixels.

In this technique, a histogram is computed from all of the pixels in the image or in the selected region, and the peaks and valleys in the histogram are used to

locate the clusters in the image [7]. Hue value was used as a measure. For the region of interest shown in figure 11, six stiff regions are identified by the segmentation algorithm.

Figure 11. Characterization by segmentation

A refinement of this technique is to recursively

apply the histogram-seeking method to clusters in the image in order to divide them into smaller clusters. This is repeated with smaller and smaller clusters until no more clusters are formed [7][8]. 6. Results

Measurements performed on series of images from both phantoms and patients show a good, numeric characterization of the lesions can be obtained. Both absolute and relative elasticity characterizations can be obtained. The absolute elasticity describes the elasticity of a region as detected by the medical scanner. The relative elasticity defines a ratio between two selected regions and is useful for comparing a supposed normal tissue and an allegedly pathologic region (figure 12). This way, the images provided by the medical scanner earn further diagnostic value. As depicted by figure 10, a portion of tissue with represented by predominant red color obtains an average of 117, a predominant green with some red scores 139 and finally a predominant blue is assessed to 223. A ratio between the two regions indicated by figure 10 shows that region number two is 1,6 times harder then the first one. 7. Conclusions

Ultrasound elastography is a novel medical imaging technique that allows for visual assessment of tissular

stiffness, biological property that has only been assessed subjectively before.

Figure 12. Numeric characterization of regions.

Elasticity ratio.

In spite of its potential and sensitivity the method is hampered by intrinsic limitations, due to the technique of image acquisition. The use of supplemental image processing techniques is mandatory for the improvement of the diagnostic value of the method. References 1. Ophir J., Cespedes I., Ponnekanti H., Yazdi Y., Li X. “Elastography: a quantitative method for imaging the elasticity of biological tissues.” Ultrasonic Imaging 1991, 13, 111-134 2. Jaros J. - Ultrasound Elastography - seminar LUT2, University of Kuopio, Finland - venda.uku.fi/opiskelu/kurssit/LUT2/elastography.pdf 3. Dudea S.M., Dumitriu Dana, Botar-Jid Carolina “Principii fizice ale elastografiei ultrasonore” – Revista Romana de Ultrasonografie, vol. 9, nr. 1, pag.45 – 52 2007 4. Itoh A., Ueno E., Tohno E., Kamma H., Takahashi H., Shiina T., Yamakawa M., Matsumura T. “Breast Disease: Clinical Application of US Elastography for Diagnosis”. Radiology, 2006, 239, 341-350 5. Cespedes I., Insana M., Ophir J. “Theoretical Bounds on Strain Estimation in Elastography”. IEEE Transact Ultrason Ferroel Freq Control, 1995, 42, 969-972. 6. Shiina T., Nitta N., Ueno E., Bamber J.C. “Real time tissue elasticity imaging using the combined autocorrelation method.” J Med Ultrason, 2002, 29 (3), 119 – 128 7. Saphiro G., Stockman G. “Computer vision”, Prentice Hall, 2001, 279 – 325 8. Shi J., Malik J., “Normalized Cuts and Image Segmentation”, IEEE Conference on Computer Vision and Pattern Recognition, 731 – 737