Ultrasonic study of in vivo kinetic characteristics of human tissues

Ultrasound in Med. & BioL Vol. 12, No. 12, pp. 927-937, 1986 Printed in the U.S.A.

0301-5629/86 $3.00 + .00 © 1986 Pergamon Journals Ltd.

OOriginal Contribution ULTRASONIC STUDY OF IN VIVO KINETIC CHARACTERISTICS OF HUMAN TISSUES M . TRISTAM, D. C. BARBOSA, D. O. COSGROVE, D. K . NASSIRI, J. C. BAMBER, a n d C. R. H I L L Physics Department, Institute of Cancer Research, Royal Marsden Hospital, Sutton, Surrey (Received4 April 1985;Received infinal form and accepted4 July 1986) Abstract--A method is described for quantifying tissue movement in vivo from the computation of correlation coefficient between pairs of A-scans with appropriate time separation. The method yieldsquantifiableand repeatable secondary patterns of soft tissue movement in response to primary cardiac movement in a given subject, shows consistently different results as between normal livers and a variety of abdominal tumoars, and is sensitive to either progress or therapeutically-induced regression of malignant disease. While the results reported here have been obtained using somewhat simple and crude equipment, the method is well suited to implementation on a commercial real-time scanner.

INTRODUCTION

record motion and diagnose disorders of discrete structures within the heart; real-time B-scanning is also used in cardiology and in obstetrics, to monitor fetal movement (respiration, heart rate). In soft tissues, such as liver parenchyma, variations of acoustic properties occur on a scale too small to be unambiguously imaged by the ultrasonic pulse. Images are formed as a result of complex interactions between the ultrasonic beam and dynamic tissue structures and an analytical method is required to derive the form of motion from the received echoes. So far, only very few attempts have been made to ascertain soft tissue movement from their ultrasonic echo patterns. Some of these are given below.

Soft tissues of differing types and pathologies are well known to vary in their rheological or gross elastic properties: this is, of course, the basis of clinical palpation. An extension of this approach is to study details of the patterns of movement of tissues in response to a normal physiological dynamic stimulus such as cardiac motion. In comparison with other techniques, such as cineradiography and dynamic isotope scanning, ultrasound provides a fast, relatively inexpensive and non-invasive method of investigating the movement of internal body structures in measurements that range from direct imaging to computerized analyses of received echoes. Of the methods that do not rely on images, pulsed Doppler velocimetry plays a well established role in measurements of blood flow in the heart and larger blood vessels. Cardiac-induced oscillatory movements of soft tissues, however, will have velocities about 100 times smaller than that of blood flow in arteries and since the Doppler and motion frequencies are comparable, measurements by a conventional Doppler method will be difficult. The well known imaging methods are the M-mode and the real-time B-mode scans, applied to situations where motions of interest are those of acoustic interfaces that have dimensions large relative to the ultrasonic wavelength (organ boundaries, major blood vessels). The M-mode is used mainly in cardiology, to

SOFT TISSUE MOVEMENT IMAGING METHODS 1. Diffraction analysis (Nicholas, 1980) This utilizes temporal changes in the interference phenomenon between the ultrasonic beam and tissue structures, in a manner analogous to that which applies to diffraction of X-rays by crystalline structures. In this method, in vivo, data are obtained by time-gating and frequency filtering the received echo trains that arise from a small volume of tissue. The diffraction patterns formed are associated with a specific orientation of the tissue sample; tissue movement will result in a realignment of the scatterers in the interrogated volume 927

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and, thus, a different diffraction pattern. The absolute magnitude of movement could not be assessed from such measurements, which nevertheless were shown to be extremely sensitive to small changes occurring due to tissue motion.

2. Statistical method (Dickinson and Hill, 1982) This statistical method of determining the degree of correlation between temporally spaced pairs of Ascans, where correlation is interpreted as a measure of tissue movement (described below).

3. A signal processing technique (Wilson and Robinson, 1982) This signal processing technique of RF M-mode scans, was applied to measure small displacements and deformations in liver. This method differs from the one described here in that it assumes that the trajectory of a point of constant phase represents the axial component of the trajectory of a point target in the interrogated tissue sample. This approach, when applied to parenchymal tissue, makes the implicit assumption that echoes arise from discrete, specularly reflecting structures in tissue, rather than as a result of complex interference phenomena occurring in a bulk scattering medium. There exists a degree of disagreement (see eg. Chivers, 1981) as to which approach is appropriate. The authors report that, by interrogating axial shifts between successive A-scans, plots of axial tissue displacement could be obtained.

December1986, Volume 12, Number 12 ultrasonic scans as a means of clinical tissue characterization. The include the following.

1. Feature extraction by Fourier analysis The method of feature extraction by Fourier analysis (Chu and Reaside, 1977), was applied in analysis of digitised echocardiograms (i.e. M-mode scans of the heart). Using Fourier features, waveforms from the anterior mitral leaflet were classified and a good feature separation obtained between normal and diseased (mitral stenosis) cardiac states.

2. Dynamic autocorrelation analys& of A-scans (Gore et al., 1979) In this method, temporal changes of the echo characteristics were analysed by calculating a time-domain autocorrelation function and extracting features which summarize quantitatively a set of echo samples (eg. the position and shapes of peaks in the autocorrelation function, which are indicative of the distances over which there exists structural coherence). Time plots of echo features, which depict changes in scattering structure, were reported to be potentially useful in such clinical applications as diagnosis of cardiac disease and blood perfusion assessment. In this paper we describe the development to dinical implementation of the correlation technique proposed by Dickinson and Hill. Results are presented of some initial in vivo measurements.

4. A "'sonographic palpation'" method (Eisensher et al., 1983)

CORRELATION COEFFICIENT AS A MOTION PARAMETER

In this method an external source of relatively strong vibrations of a frequency 1.5 Hz, was used to induce movement in liver, pancreas and breast tissue (in the two former organs overriding natural motion due to cardiovascular activity) as a means of studying benign and malignant lesions. The authors reported that both types oftumours were diagnosed, with a high success rate, from visual analysis of recorded M-mode wave-forms. In this method, the problem of dependence of motion patterns on the direction of motion relative to the ultrasonic beam does not seem to have been adequately addressed. Additionally, the discomfort to the patient of applying a fairly powerful pressure to abdomen might prove a serious limitation in severely ill patients.

The correlation method of soft tissue motion analysis, proposed by Dickinson and Hill, is based on an assumption that, if tissues moves, the A-scan collected from the interrogated region will change and a measure of the degree of change between scans separated in time will provide information about motion in this time interval. To obtain a single value to describe A-scan changes with time, the correlation coefficient R is defined such that

OTHER METHODS Other methods have been reported which, although not aimed directly at quantitative assessment of tissue movement, are related to the present technique in that they also use analysis of temporal variations of

N

E [yz(t) - f(t)][y,(t + 7) - f;(t + r)] R(t, r) = i=1

N. ~(t). ~ t + r)

(1)

where yz(t) and yi(t + r), i = 1,N, are the sampled amplitudes of two A-scans each consisting of N pixels Yi, collected at times t and t + r, respectively; 37(0 and ay(t) represent the mean and standard deviation of the A-scan y( t). Since R has a maximum value of 1 for stationary tissue and decreases monotonically as the rate of

In vivo kinetic characteristicsof human tissues@M. TRISTAMel aL

movement increases, it is convenient to use, in analysis of motion, the value R ' = 1 - R, such that R'= 1-R

=0 >0

for for

r = 0 r:/:0

(2)

The monotonic dependence of the correlation coefficient on the rate of movement suggests that R' will be a useful measure of motion. Since, however, Ascans are formed as a result of complex interactions between ultrasonic beam and tissue response, R' depends on other factors as well, particularly the ultrasonic beam shape and pulse shape. The behaviour of the correlation coefficient was studied theoretically and experimentally by Dickinson and Hill (1982) and the parameter calibrated, using controlled motion and tissue models. The authors found that, for a given displacement, the value of R' depends, among other factors, on the direction of motion both along and normal to the ultrasonic beam axis; this fact will cause problems

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in analysis of motion in vivo, where, in general, the vector of movement will have both axial and lateral components. METHOD

To date, we have studied data obtained from scanning abdominal tumours of eight cancer patients and (when possible) their normal liver, and also the normal liver parenchyma of 12 healthy volunteers. The apparatus used was a custom-built, static B-scanner, equipped with a 3.5 MHz narrow band-width circular transducer, with aperture of 13 m m and spherically focussed over the range of 40-100 mm. Data were collected as follows. (Other technical aspects of the scanner are described in more detail elsewhere (Nicholas et al., 1985) in application to study of ultrasonic tissue texture.) Firstly, a conventional B-scan was obtained (Fig. I a) on which an electronic gate pulse was superimposed

A-scan line oF s i 9 h t

ECG

M-mode

n _ _

b

Fig. 1. a) B-scan, showing the A-scan line of sight and the gating markers; b) M-mode with the corresponding ECG trace and the box (white vertical lines) used to select the portion of image to be digitised.

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to select depth range of interest. An A-scan line of sight was then directed through this chosen region of tissue. Secondly, the display was switched to M-mode and the sound pulse repetition frequency (PRF) set to 600 Hz. The ECG trace was simultaneously monitored and displayed (Fig. l b). During collection of both the B-scan and the Mscan, subjects were requested to hold their breath. A portion of the M-mode image, together with the corresponding ECG trace, was selected for digitising and computer analysis (Fig. lb). This 256 × 256 array of pixels corresponds to 2.5 cm (depth in tissue) by 2 sec (sweep time). The method of data collection in its present form, as described above, is not ideal and is unsuitable for routine clinical examinations as it is fairly tedious for both the patients and the scanning clinician. Simultaneous display of B-mode and M-mode scans would be preferred, to ensure accurate positioning of the transducer during data collection, particularly while scanning small lesions. It has nevertheless proved adequate for collection of a limited set of data. DATA ANALYSIS Digitised M-mode displays were transferred onto a VAX-11/750 computer and analysed as follows. The coefficient R', as defined in eqns. (1) and (2), was calculated between gated portions of pairs of scans, consisting of 50 pixels Yi (which corresponds to tissue thickness of 0.5 cm), separated in time by ~-. The time separation was chosen as r = T/IO, where T is the length of cardiac cycle in seconds. Such separation was chosen empirically, as being large enough to reduce the effect of what appeared to be random fluctuations and yet small enough to detect velocities of the order 0.1 mm/sec. The correlation coefficient was calculated for overlapping zones of 0.5 cm (i.e. 0-0.5 c m , 0.10.6 cm . . . . . 2-2.5 cm from the top of the scan), thus allowing detection of spatial discontinuities of movement characteristics in the interrogated sample. Variations of the coefficient R', as a function of time, are subsequently referred to as the correlation patterns. RESULTS To facilitate comparison, all results, i.e. correlation patterns obtained from tumour tissue and normal liver parenchyma, are plotted in two consecutive cardiac cycles; the length of which 2 T (in seconds) is marked on the horizontal axis; on the vertical axis an indication is given of the maximum value of R', to which each plot is normalised.

December1986, Volume 12, Number 12

1. Normal liver Patterns of correlation were measured for liver parenchyma in a group of 12 normal volunteers. Additionally, an attempt was made to obtain, whenever applicable, non-diseased liver scans from those cancer patients (following section) in whom tumour tissue was the primary object of investigation. In healthy volunteers, the left and fight lobes of the liver were scanned. Some of the results are shown in Fig. 2, where correlation patterns selected for comparison were obtained from corresponding sites (i.e. left lobe--Figs. 2. la, 2.2a, 2.3a, right lobe--Figs. 2. lb, 2.2b, 2.3b) of three individuals (2. la-b, 2.2a-b, 2.3ab). As seen from the diagrams, certain common features can be distinguished here, as follows: 1) in the left lobe, two large peaks follow immediately the R wave of the QRS complex (arrows) and a third, smaller peak immediately precedes the next one; 2) in the right lobe, one smaller and two larger peaks follow the R wave; 3) absolute maximum values in the correlation pattern are, in a given subject, higher in the left than in the right lobe of the liver. The structure of peaks in a normal liver correlation pattern is related to the position and orientation of the interrogated tissue sample with respect to the source of movement which, for liver, consists of the heart and blood pressure in the major arteries (described below). Relative positioning of the ultrasonic beam and the vector of movement also plays some role which, to be adequately assessed, requires a real-time scanner. Non-diseased liver in cancer patients presents a separate problem, which is discussed in the next section. Tumour tissue Data were obtained from 8 patients with clinically diagnosed malignant disease, as follows: (a) 3 patients with involvement of the para-aortic lymph nodes due to testicular teratoma; (b) 1 patient with a large abdominal lymph node mass due to non-Hodgkin's lymphoma; (c) 4 patients with metastatic liver disease: 1 with carcinoma of the rectum, I with breast and 1 with lung carcinoma and 1 patient with an unknown primary. In patients with involvement of the para-aortic lymph nodes a regularity of correlation patterns was observed, not seen previously in any of the normal livers, of which examples are shown in Fig. 3. The first patient (with a primary testicular teratoma) was scanned first at the onset of his chemotherapy (Fig. 3. l a) and then six weeks later, when a dramatic response to treatment was diagnosed clinically.

In vivo kinetic characteristics of human tissues • M. TRISTAMet al.

@,96

0.96

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Fig. 2. Correlation patterns obtained from liver parenchyma in 3 healthy volunteers (la-l b, 2a-2b, 3a-3b): la, 2a, 3a--left lobe, lb, 2b, 3b--right lobe. Horizontal axis: time in seconds, normalized to 2 cardiac cycles (arrows-R wave of the ECG QRS complex). Vertical axis: values of correlation R'. The observed correlation pattern (Fig. 3.2a) has also changed in an obvious manner, from that of a single dominant peak to one featuring two distinct peaks. It was noted that the size of the lesion decreased significantly between the two examinations. Another patient, also with an abdominal mass involving para-aortic lymph nodes, in this case due to non-Hodgkin's lymphoma, was similarly scanned prior to chemotherapy (Fig. 3. I b) and a few weeks later (Fig. 3.2b). This patient's response to treatment was poor, the lesion increased in size and the patient died shortly afterwards. Comparing the two cases, a suggestion of

the opposing trends can be seen, i.e. the initial twopeak regularity being replaced by a less pronounced, but distinguishable pattern where a large dominant peak is followed by a series of smaller peaks. Although the differences between correlation patterns in Figs. 2 (normal liver) and 3 (lymph node metastases) are quite striking, it would be premature to suggest that they can be solely explained by malignant changes. Firstly, correlation patterns in the two groups were obtained from scanning histologically different types of tissue and, secondly, the dynamics of the two systems (resulting from the positioning of the inter-

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0.58 R"

8.58 R'

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t,sec 1.19

2a

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Fig. 3. Patterns of correlation obtained from secondary involvement of para-aortic lymph nodes due to testicular teratoma in one patient (la, before, and lb, after chemotherapy) and non-Hodgkin's lymphoma (2a, before, and 2b, after chemotherapy) in another. Horizontal axis: time in seconds, normalized to 2 cardiac cycles (arrows--R wave of the ECG QRS complex). Vertical axis: values of correlation R'. rogated samples with respect to sources of movement) are different in both cases. The importance, however, of the lymph node data lies not so much in their being different from normal liver patterns as in the fact that correlation patterns show a degree of change as a result of therapy and/or progress of disease; this is encouraging in view of future possible applications in studies of tumour response to treatment. Normal liver correlation patterns are best compared with those obtained from patients with secondary liver tumours (Fig. 4). In this group of patients, who were all undergoing chemotherapy, the examined liver metastases were due to the following causes: carcinoma of the rectum (Fig. 4a), breast (Fig. 4b) and lung (Fig. 4c), and an unknown primary (Fig. 4d). In all four cases, a common feature of the correlation patterns, such as was not seen in healthy subjects (see above), was a single dominant peak, followed by a series of much smaller peaks. The observed similarity was that of the shape of functions; the absolute values at the main peak were different (more than four times as great in Fig. 4d than in Fig. 4c) and a difference in the relative phase of the main peak (Figs. 4a, 4c) was also noted. (In Fig. 4, each of

the four diagrams was normalised to its own maximum, to stress the similarity of their shape). In comparison with correlation patterns obtained from healthy livers (Fig. 2), those from liver metastases were, at the maximum, between 1.5 and 7.5 times lower. Study of "normal" liver parenchyma in patients with liver metastases presents a particular problem in tissue movement analysis. Firstly, replacement of healthy tissue by progressive malignancy is often so extensive that (as was the case in three out of four patients above) an area of a size sufficient for examinations and with normal ultrasonic appearance on a Bscan cannot be located. Secondly, even if the conditions of normal ultrasonic texture and the size of a sample are satisfied, such regions would usually lie so close to malignant lesions as to be rendered "dynamically abnormal", due to effect of push and pull from tumour tissue. Thus, to address adequately the question of "normal" liver in patients with secondary disease, further investigations are required. It is envisaged that, when these become available, we should observed discontinuities in movement characteristics at the sites of metastases, as compared with non-diseased surround-

In vivo kinetic charactedsticsof human tissues• M. TRISTAMet al. 8.26

933

0.38

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a

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sec

0 . 93

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t, sec

i . 33

c,sec

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A

Fig. 4. Patterns of correlation obtained from secondary liver deposits: a) carcinoma of the rectum b) lung carcinoma, c) breast carcinoma, d) an unknown primary. Horizontal axis: time in seconds, normalized to 2 cardiac cycles (arrows--R wave of the ECG QRS complex). Vertical axis: values of correlation R'.

ing tissue, which can then serve as internal "control" in differential diagnosis of liver lesions.

Repeatability of measurements It is of importance to assess that, when a region of interrogated tissue is studied repeatedly, the recorded pattern of correlation can be repeated, within limits of statistical fluctuations and such unavoidable systematic error as results from dynamic factors (ability to locate the same scanning plane and depth, natural re-alignment of body structures etc.). To date, due to (mainly) the cumbersome method of data collection and the inability of most patients to endure the discomfort of prolonged examinations, only very few measurements have been taken to check the repeatability in tumour tissue. In normal fiver, a special measurement was taken to that effect, as follows. A longitudinal scan of the liver at the body midline was recorded and repeated at half hourly intervals (of the same individual and scanning plane as in Fig. 2. la). Six scans were collected, 3 before and 3 after a meal. An attempt was made to locate each time the same scanning plane and depth in liver, as judged by anatomical "landmarks" (nearby blood vessels, proxim-

ity of the diaphragm etc.). The results are shown in Fig. 5. On visual assessment, the only obvious difference between the six recorded correlation patterns is that, after the meal, maximum values of correlation were lower by about 25% (the significance of this observation is not understood). To assess objectively the degree of discrepancy between these functions, similarity S;j between two correlation patterns (R'(t))i and (R'(t))j was defined as [(~i,j]max

(3)

Si'j = [(])i,i]max '

where [@i,j]maxis the maximum value of the cross-correlation function calculated between the two patterns (@i,i is, of course, the autocorrelation function). Since 1 Si; = ' <1 o r > l

for for

i=j i~j

(4)

the degree of dissimilarity is best described by a parameter S~.j such that

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8.98

R'

d

0

t,, s e c

t , sec I . 31 0.98

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Fig. 5. Patterns of correlation obtained from a healthy volunteer in the repeatability measurements: a, b, c,-before meal, d, e, f--after meal. Horizontal axis: time in seconds, normalized to 2 cardiac cycles (arrows--R wave of the ECG QRS complex). Vertical axis: values of correlation R'.

s b = IS,,,- I I

(5)

S;j has a minimum value of 0 for identical functions (i = j ) and increases as the degree of discrepancy increases. Values of S~j were calculated for the patterns of correlation in Fig. 5, numbered from 1 to 6 (Figs. 5a-f, respectively). For comparison, values of S}j between the above correlation patterns and two others, selected, one of each, from the group of lymph node metastases (No 7--Fig. 3.1 a) and liver metastases (No 8--Fig. 4d). The results are shown in Table 1.

The average value of S,.j for i, j = 1, 6 was 0.047 + 0.028, while for i = 1, 6, j = 7, 8 and i = 7, 8, j = 1, 6, (S~,j --/=Sj,i since [4
In vivo kinetic characteristicsof human tissues• M. TRISTAMel al.

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Table 1.8 × 8 matrix of similarity values S~j, calculated for correlation patterns in repeatability measurement (i, j = 1, 6), lymph nodes (i, j = 7) and liver metastases (i, j = 8).

1 2 3 4 5 6 7 8

1

2

3

4

5

6

7

8

0.0 0.018 0.046 0.020 0.026 0.058 0.508 0.393

0.055 0.0 0.080 0.047 0.068 0.090 0.441 0.431

0.011 0.051 0.0 0.015 0.003 0.029 0.566 0.454

0.032 0.012 0.054 0.0 0.044 0.068 0.524 0.404

0.092 0.066 0.103 0.093 0.0 0.075 0.441 0.407

0.052 0.019 0.041 0.053 0.010 0.0 0.419 0.492

0.630 0.617 0.634 0.621 0.636 0.066 0.0 0.050

0.725 0.722 0.707 0.730 0.677 0.714 0.366 0.0

The above analysis, although more objective than simple visual comparison, remains rather descriptive. A better way of comparing variations such as correlation patterns of tissue movement lies in quantitative feature extraction, as discussed in the next section.

Character&tics of correlation patterns Although results available up to date are too few to draw general conclusions, those obtained from a preliminary set of data suggest that there might exist correlation patterns of tissue movement characteristic of malignant tumours and not observed in normal tissue. In comparison with normal liver patterns, those derived from t u m o u r tissue can be described as having lower maximum values and fewer peaks of, generally, greater regularity. Such imprecise characteristics can be defined objectively by the method of feature extraction. Currently, an attempt is being made to formally define a set of features of correlation patterns enabling their classification and separation of normal from pathology. Features such as height and phase of peaks, amplitudes and frequencies o f Fourier harmonics are all possible candidates, but for such classification to become meaningful, a statistically more significant amount of data (a "learning set") is required. Some features of potential usefulness can nevertheless be defined, of which an example is given below. On visual examinations of correlation patterns, one feature appears intuitively to provide a good indication of an important characteristics, namely, the area under the curve defined as N

Ao : ~, R~ (t) i=1

where R~(t) are the values of correlation in an N-pixel sample of the correlation pattern R'(t). Values of Ao were calculated for all correlation patterns in sections 5.1, 5.2 and 5.3 (N was the number of points in two cardiac cycles), numbered from 1 to

20 in order of their appearance in text. The results are shown in Fig. 6. As seen from the figure, the feature Ao gives good separation between normal liver correlation patterns and those obtained from tumour tissue, both lymph node and liver metastases. The 95% confidence intervals were calculated for the differences in mean values of Ao, i.e. (Ao normal - Ao tumour) = 0.261 + 0.132 for lymph node metastases and 0.273 ___0.117 for liver metastases. In both cases upper and lower confidence limits are positive, which indicates that values of the feature Ao are significantly different (higher) for normal vs tumour tissue. The optimal set of features, giving the best separation between normal and pathological tissue movement correlation patterns, will depend on a particular clinical application; it can be selected only after more data on a variety of pathologies have become available. DISCUSSION The structure of peaks in correlation patterns obtained in ultrasonic measurements of tissue movement in vivo is related to the following factors: 1. elasticity and compressibility of tissues; 2. distance and orientation of the interrogated tissue sample with respect to the source of movement which, for abdominal structures under study, consists of the beating heart and pulsative blood pressure in major arteries (with smaller vessels having probably a lesser effect); 3. relative orientation of the movement vector and the beam of ultrasound. In normal liver, the left lobe lies in close proximity to the heart, where ventricular contractions are directly "felt," giving rise to large peaks of motion at the beginning of the cardiac cycle. These are followed in time by the peak corresponding to the aortic pulse. In the right lobe, at a farther distance from the heart, the main peaks probably correspond to the aortic pulse, preceded by a small peak due to ventricular contraction. The

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lymph node metastases normal

liver metastases

liver

normal

liver

8.515

8.429

8. 343

8. 257

8.172

8. 886.

8. 888 1

2

3

4

5

6

7

8

9

18

ii

12

13

14

15

16

17

18

19

::)8

subject number

Fig. 6. Feature Ao (vertical axis, arbitrary units) calculated for correlation patterns obtained from: normal liver parenchyma, patterns 1-6, 15-20), lymph node metastases (7-10) and liver metastases (11-14).

next small peak might be attributed to the pulse in the hepatic artery. Absolute values of correlation are lower, reflecting the fact that the overall movement decreases in the fight lobe. To explain fully the observed regularities, a study of normal liver parenchyma movement--"mapping" the correlation patterns as a function of position--is to be undertaken, using a real-time scanner. In comparison with normal tissue structures, malignant tumours are often associated with hardening and stiffening of tissues, i.e. decreased elasticity and compressibility. Thus, in response to mechanical stimuli, such regions might be expected to behave as heavily damped systems where a single peak in the correlation pattern can be seen as results from the combined effect of the ventricular contraction and the aortic pulse, felt by the interrogated tumour tissue sample as a single driving impulse. This can be demonstrated, in a simplified form, using a computer simulation; the main difficulty lies in defining the shape of the driving force, which, for

abdominal structures discussed here, is a complex superposition of the "shock wave" from the contracting heart (attenuated at a distance) and subsequent changes in blood pressure in major arteries (mainly the descending aorta). To give a fully comprehensive picture of the complex phenomenon of soft tissue movement (as seen by ultrasound), further investigations are required. These are currently assisted by a series of model experiments on tissue phantoms, undergoing controlled motion. CONCLUSIONS In comparison with other techniques, the method of soft tissue movement analysis by calculating the degree of correlation between pairs of digitised A-scans has the advantage of relative simplicity; it can be implemented on a commercial scanner, equipped with image recording facility and a transducer of appropriate size, directivity and pulse shape. The results of a preliminary set of data suggest that there exist repeatable patterns of correlation char-

In vivo kinetic characteristics of human tissues • M. TRISTAMet al.

acteristic o f malignancy. Furthermore, changes in correlation patterns observed in serial studies reflect changes in tissue condition due to treatment a n d / o r progress o f disease. These observations, the significance o f which will have to be confirmed in further measurements o f m u c h i m p r o v e d statistics a n d specificity, indicate potential usefulness o f tissue m o v e m e n t analysis in two clinical applications, namely, differential diagnosis o f t u m o u r s a n d assessment o f t u m o u r response to treatment. It should be stressed that applications o f tissue m o v e m e n t analysis are not limited to cancer; diagnosis and m a n a g e m e n t o f other disorders, e.g. those associated with o e d e m a or fibrosis, where elastic properties o f tissue are pathologically altered, might well benefit from such objective m e t h o d o f tissue identification.

REFERENCES Chivers R. C. (1981) Tissue charactefisation. Ultrasound Med. Biol. 7, 1-20.

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Chu W. K. and Raeside D. E. (1978) Fourier Analysis of the Echocardiogram. Phys. Med. Biol. 23, 100-105. Dickinson R. J. and Hill C. R. (1982) Measurement of soft tissue motion using correlation between A-scans. Ultrasound Med. Biol. 8, 263-271. Eiscnsher A., Schweg-Tottier E., Pelletier G. and Jacquemard G. (1983) La palpation 6chographique rythm6e--Echosismographie. Journal de Radiologie 64, 225-261. Gore J. C., Leeman S., Metreveli C., Plessner N. T. and Willson K. (1979) Dynamic Autocorrelation Analysis of A-scans in vivo, in Ultrasonic Tissue Characterization II, M Linzer, ed. National Bureau of Standards, Spec. Publ. 525, 275-280. Lerski R. A., Barnett E., Mills P. R., Watkinson G. and MacSween R. N. M. (1982) Ultrasonic charactedsation of diffuse liver disease--the relative importance of frequency content in the A-scan signal. Ultrasound Med. Biol. 8, 155-160. Nicholas D. (1979) Ultrasonic diffractionanalysis in the investigation of liver disease. Brit. J. Radiol. 52, 949-961. Nicholas D. (1980) The analysis of soft tissue movements. In: Ultrasonic Tissue Characterisation, (Edited by J. M. Thijssen) (Staflen's Scientific Publishing Company) 281-285. Nicholas D., Nassiri D. K., Garbutt P. and Hill C. R. (1985) Tissue characterisation from ultrasound B-scan data. Ultrasound in Med. Biol. (in press). Wilson L. S. and Robinson D. E. (1982) Ultrasonic measurement of small displacements and deformations of tissue. Ultrasonic Imaging 4, 71-82.