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European Journal of Echocardiography 2004 5(5):375-385; doi:10.1016/j.euje.2004.02.004
© 2004 by European Society of Cardiology
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Copyright © 2004, The European Society of Cardiology

Tissue motion imaging of the left ventricle—quantification of myocardial strain, velocity, acceleration and displacement in a single image

Camilla Storaaa,*, Peter Cainb, Bjørn Olstadc, Britta Lindb and Lars-Åke Brodinb

aDivision of Medical Engineering, Karolinska Institutet, Novum F60, SE-14186 Huddinge, Sweden
bDepartment of Clinical Physiology, Karolinska University Hospital Huddinge, Stockholm, Sweden
cDepartment of Computer and Information Science, The Norwegian University of Science and Technology, Trondheim, Norway

Received 11 September 2003; received in revised form 4 February 2004; accepted after revision 24 February 2004.

* Corresponding author. Tel.: +46-8-585-83758; fax: +46-8-585-87779. camilla.storaa{at}labmed.ki.se


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Conclusions
 References
 
Aims: Several methods of parametric imaging of left ventricular function including tissue velocity imaging (TVI) and strain rate imaging (SRI) have previously been presented, however, they have the limitation that they can, respectively, portray only one physiological myocardial parameter. The aims of this pilot study were to implement and validate tissue motion imaging (TMI) for the first time, a visualization technique which permits acceleration, velocity, displacement and strain to be interpreted quantitatively or semi-quantitatively in a single image.

Methods and results: TMI is achieved by the color coding of temporal tissue velocity integrals. The principles behind this technique are validated, and case examples demonstrating its use in the clinical setting are provided. Limitations of the method as well as future applications and improvements are discussed.

Conclusion: As this method allows representation of a multitude of variables and is visually attractive, it may facilitate more widespread use of myocardial quantitation in everyday practice.

Keywords: Tissue motion; Tissue Doppler; Tissue tracking strain; Ventricular function


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Conclusions
 References
 
Assessment of left ventricular systolic function has traditionally been achieved with visual analysis of radial myocardial motion. The mechanics of the left ventricular function are, however, complex and involve multiple axes of motion which may not be appreciated visually.1 The longitudinally arranged myocardial fibers that are susceptible to ischaemia due to their predominately subendocardial location2 have particularly been the focus of multiple quantitative approaches over recent years.

The development of tissue Doppler imaging (TDI) over the last decade has provided several methods for studying the longitudinal function of the heart, such as tissue velocity imaging (TVI),3 strain rate and strain imaging (SRI).4–6 No one technique, however, has successfully combined accuracy, feasibility, and reproducibility and therefore the choice of quantitative measure needs to be tailored to the clinical scenario. Because the measurement of these techniques often requires a significant degree of image post-processing and are not visually intuitive, none have therefore successfully completed the transition from the research arena into the clinical domain. These issues could, however, be addressed if an imaging modality was developed that could represent all of these quantitative measures in a visually intuitive manner.

Tissue motion imaging (TMI), developed from the integral of the tissue velocity curve in the longitudinal axis, represents a temporal–spatial color map of myocardial movement throughout the cardiac cycle. This technique allows simultaneous visual and quantitative interpretation of regional tissue velocity, strain, displacement, and acceleration for the whole cardiac cycle in one image. TMI thus presents itself as a potential ‘hybrid’ of previous techniques and may facilitate the use of myocardial quantitation in routine clinical practice.

The aims of this pilot study were to implement TMI for the first time, validate the principles behind this technique, and to provide case examples demonstrating its use in the clinical setting. The primary motive for implementing TMI is that the method incorporates more cardiac parameters than any previous tissue Doppler method.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Conclusions
 References
 
Study subjects
The tissue motion imaging method was implemented in four patients (one healthy subject with no coronary artery disease at angiography, three patient with severe systolic dysfunction). Table 1 describes this patient population.


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  		Table 1 Patient characteristics

 
Image acquisition
Imaging was performed in the left lateral decubitus position throughout the test, using commercially available systems (System Five and Vivid 7, General Electric Vingmed, Horten, Norway). Images were obtained in the apical view using a standard 2.5 MHz transducer. Two-dimensional gray scale and color Doppler data were saved at each stage of the test in each patient during end expiratory apnea. Color Doppler frame rates varied between 96 and 130 frames/s (depending on the sector width). Two cardiac cycle loops triggered to the QRS complex were stored digitally for off-line analysis. For noise reduction the velocities were cine-compounded, i.e. each point at each time instant of the first heart cycle in relation to the ECG and M-mode was averaged with the same point at the same time instant of the second heart cycle.

Development of tissue motion images
TMI was primarily developed using ‘in house’ software. The middle of each myocardial wall was marked using a standard curved M-mode7,8 (Fig. 1a). For every point along the curved M-mode the longitudinal velocities were identified (Fig. 1b) and averaged over two heartbeats to reduce signal noise. Each velocity was then integrated over time to identify regional motion with the initial value set to 0 mm at the R-peak of the ECG. Ranges of motion were then assigned to different color categories (Fig. 1c and Table 2). Tissue motion images were achieved by the development of temporo-spatial images of each color category of motion (Fig. 1d).


Figure 1
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  		Figure 1 Derivation of the tissue motion image: a) The ultrasound grayscale image with a curved M-mode marked up in red. b) The velocity of the green point in a). c) The integral of the velocity in b) with color coding. d) The motion image of the whole curved M-mode of a) built of color-coded curves such as the one in c). The example shows case 1 (IVR, isovolumic relaxation; E-wave, rapid early filling; A-wave, atrial-induced filling; IVC, isovolumic contraction).

 


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  		Table 2 The colors assigned to the different motion intervals

 
Interpretation of tissue motion images
Displacement from TMI images
The motion of the myocardial wall was expressed as a color given in Table 2 and adopts a similar approach to that used by previously published studies of tissue tracking9 or displacement.10 Using this color-coded classification, the furthest point reached in the color scale of Fig. 2 or Table 2 expresses the maximum displacement of the myocardium in the longitudinal axis.


Figure 2
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  		Figure 2 Schematic display of how velocity (v), acceleration and strain ({varepsilon}) can be interpreted from the tissue motion image. Velocity is calculated from the rate of change of color bands in the temporal (horizontal) axis. The closer together, the higher the velocity. Rate of change of velocity (acceleration) may also be appreciated. Strain can be calculated from the spacing of the color bands in the spatial (vertical) axis. The closer the bands together, the higher the strain. The red and blue boxes indicate the examples from the text.

 
Velocity from TMI images
The motion image may also offer information about velocity for all regions of the myocardium during the whole cardiac cycle in a visually attractive form. As the temporal derivative of the image is myocardial velocity, the width of each color band in the x-axis (horizontal) direction (i.e. time) is inversely proportional to the velocity. To illustrate this concept the text in the blue box of Fig. 2 indicates a region with motion transition from green to orange to yellow categories. From the color coding we know that at the transition point between the yellow and orange categories the motion d is exactly 4 mm, and between orange and green exactly 6 mm. If the width of the orange color band in the temporal direction, wt is 0.1 s at one point, the velocity over the orange color band (vo) will be:



Formula 1

(1)

Acceleration from TMI images
It is also possible to form an impression of the second order temporal derivative, i.e. the acceleration. A narrow TMI band followed (in the temporal/horizontal direction) by a wide band implies a decrease in velocity and thus negative acceleration (Fig. 2). Likewise, a transition from a wide to a narrow color band will indicate positive acceleration.

Strain from TMI images
The spatial derivative of the motion image in the y-axis (i.e. space) is inversely proportional to myocardial strain. One-dimensional Lagrangian strain ({varepsilon}) is defined as:11


Formula 2

(2)
where L is the current length of a segment and L0 is the initial length. The current length (L) will be the width of the color band in the spatial or y-axis (vertical) direction of the motion image, ws. The initial length will be the length of the color band before the motion. Using the example in the red box in Fig. 2, we know that the apical border of the green color band has a motion of 6 mm, and the end the farthest away from apex a motion of 8 mm. Thus the length of the color band must have decreased 2 mm (8 mm – 6 mm) in comparison to its initial length, and the initial length is L0=ws+2. This yields the strain:



Formula 3

(3)

This is true for any band of any color as long as the spatial increment of each color band is 2 mm. If a color band like in the example of the red box in Fig. 2 is measured to be 20 mm, the strain of the region is –2 mm/(20 + 2)mm = –9.1%. Hence a narrow color band in the spatial y-axis represents a region with higher strain compared to that of a widercolor band.

Reliability compared to traditional approaches
Based on these calculation principles, velocities (Eq. (1)) and strains (Eq. (3)) calculated from TMI images were compared with the tissue Doppler velocities and strains4,5 recorded by the ultrasound system to confirm the reliability of these approaches.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Conclusions
 References
 
Visual interpretation
Normal appearance of TMI
Displacement
Fig. 3 (case 1) gives an example of a TMI from a healthy individual. Upon reaching end systole (timepoint {approx} 0.4 s) the maximal displacement (14 mm) is, as expected, seen basally and is consistent with previously described mitral plane ranges of movement.12,13


Figure 3
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  		Figure 3 Case1: tissue motion image from the septum of a normal subject. See text for description of velocity and strain patterns (IVR, isovolumic relaxation; E-wave, rapid early filling; A-wave, atrial-induced filling; IVC, isovolumic contraction).

 
Velocity and acceleration
During the first part of systole (time 0.1 s–0.25 s) there is a rapid appearance of new colors reflecting the increasing displacement from brisk movement of normal myocardial movement towards the apex. In early diastole there is a very rapid reversal of this process with the fast filling of the ventricle initially occurring at a faster rate than in systole although slowing in later diastole.

Strain
The spacing of the color bands in the spatial (vertical) axis demonstrates that there is a degree of inhomogeneity in strain from base to apex. Inhomogeneities are not unexpected.14,15 While the spacing is similar in the base and mid segments, the narrower bands apically signify a higher strain in this region. Broad spacing of the color bands in diastole reflect the much lower strain in the part of the cardiac cycle as expected.

TMI during systolic dysfunction
Displacement
Fig. 4 (case 2) shows a clearly pathological pattern TMI. Most notable is the decreased apically directed displacement (8–10 mm) in the base of the ventricle. The maximal displacements are thus also decreased in the whole of the myocardial wall with longitudinally directed dyskinesia apically (black band).


Figure 4
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  		Figure 4 Case 2: systolic dysfunction demonstrating a dyskinetic region near apex. In this area there are low/reversed velocities and strain patterns (IVR, isovolumic relaxation; E-wave, rapid early filling; A-wave, atrial-induced filling; IVC, isovolumic contraction).

 
Velocity and acceleration
The rate of change of color bands (and thus velocity) initially has a normal appearance but slows near end systole especially in the mid and apical segments. Despite this there is still a rapid return of lower colored bands reflecting maintained diastolic velocities.

Strain
The strain pattern displays the useful regional quantitative information in this case. The broad bands in the mid region of the ventricle suggest very low strain while the negative displacement band (black) in the apex reflects expansion in this region.

TMI during diastolic dysfunction
Displacement
In case 3 (Fig. 5) we see that the systolic displacements are generally decreased across the whole wall suggesting a degree of coexistent systolic dysfunction.


Figure 5
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  		Figure 5 Case 3: diastolic dysfunction with a clearly delayed relaxation pattern in diastole demonstrated by lower velocities (broader bands of color) in systole than in diastole. No clear E- and A-waves are present (IVR, isovolumic relaxation; IVC, isovolumic contraction).

 
Velocity and acceleration
In contrast to previous examples where the rate of diastolic relaxation exceeded that of systole, in this case there are lower diastolic velocities (broader bands in the temporal/horizontal direction) compared to that of systole. There appears also to be a loss of the subsequent atrial activity (late in diastole) reflecting the absence of active atrial filling in atrial fibrillation.

Strain
As with displacement, the strain is generally and uniformly decreased across the ventricular wall.

TMI demonstrating post-systolic thickening
In this example (case 4, Fig. 6) there is severe right wall ischaemia with marked post-systolic thickening. Prior to this time point, there is no displacement information recorded. The narrow bands in the temporal direction suggest that this is both a high velocity and short lived event (as previously suggested16). Despite this the relatively broad bands in the spatial direction imply that the strain contained in this post-systolic event is probably low.


Figure 6
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  		Figure 6 Case 4: a post-systolic shortening can clearly be seen in this image from the right wall (IVR, isovolumic relaxation; E-wave, rapid early filling; A-wave, atrial-induced filling; IVC, isovolumic contraction).

 
Reliability of TMI quantitation
Fig. 7 shows the reliability of velocity and strain calculations from tissue motion imaging compared to traditional echocardiographic measurements. The horizontal and vertical bars on the images represent the calculated velocities and strains and are overlayed on the raw velocity and strain waveforms. Very high agreement was achieved in almost all measurements for both velocity and strain and confirms the reliability of these techniques.


Figure 7
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  		Figure 7 Top row: basal velocities from tissue Doppler measurements (dashed line) together with values calculated from the tissue motion images (solid lines). Bottom row: corresponding comparison of strain over the wall from apex to base at end systole calculated traditionally (dashed curve) and from tissue motion images (solid lines). In all images the colors of the solid lines correspond to the color coding of the displacement of the segment, except for the gray color corresponding to a displacement of –2 to 0 mm.

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Conclusions
 References
 
This pilot study has demonstrated the new technique of tissue motion imaging which may allow simultaneous visual assessment of systolic and diastolic left ventricular events with myocardial displacement, velocity, strain, and to a lesser extent acceleration for the whole cardiac cycle. This approach may be attractive clinically because it includes all of the tissue Doppler imaging parameters of left ventricular function in a visually intuitive form for immediate analysis.

Quantization of longitudinal function
Longitudinal fiber function plays a key role in ejection of blood from the left ventricle.17 Because the majority are located in the subendocardium,18 they are particularly susceptible to ischaemia.19 Initial attempts at quantification of longitudinal fiber function included use of M-mode echocardiography to assess the mitral plane displacement during systole towards the apex.20 While this approach has demonstrated correlation with ischaemia and presence of coronary artery disease,21 this imaging approach had several drawbacks including lack of truly regional quantization and the requirement of the acquisition of M-mode images at the time of imaging, which may be particularly challenging during peak exercise stress echocardiography. However, the maximum longitudinal global motions found in our study are comparable to previous findings,12,13,22 where the mean maximum motion lies between 13.4–16.5 mm and 8.0–13.8 mm in normals and diseased, respectively. Our finding of velocities being higher during diastole than during systole in normals is also in accordance with previous findings.22

Previous attempts at quantization of regional myocardial function
Interpretation of regional wall motion performance, particularly during stress echocardiography, is difficult and requires significant training in an experienced center.23 A number of approaches to quantization of regional myocardial function have been attempted during the last two decades. Initially these involved assessment of radial myocardial function, including the centerline method24 and color kinesis,25 however, these techniques were highly reliant on good endocardial definition, required lengthy image analysis, and thus have not been implemented routinely in clinical practice.

The measurement of regional long axis fiber function has become feasible with the development of tissue Doppler. Long axis velocity was initially explored with pulsed wave tissue Doppler; this demonstrated good accuracy (85–92%) for the diagnosis of coronary disease, but was limited by the need to sample each segment at the time of the study.26 Color tissue Doppler has allowed tissue Doppler acquisition of each view, with post-acquisition analysis of all segments. This technique is accurate for the diagnosis of coronary artery disease,27 but the interpreting clinician must still analyze waveforms derived from the acquired images. This requires some training to achieve good reproducibility and accuracy. Even more recently, strain and strain rate have offered the promise of being truly reflective of regional extent of deformation during systole.4 However, both have suffered from significant noise in the waveforms, and thus have not demonstrated good reproducibility and accuracy to date.28,29

Clinical application of quantitative myocardial echocardiography has been limited by time of image post-processing, less than ideal inter-user reproducibility (especially with strain), and lack of ease of interpretation. The development of a polar map display, as used in SPECT, offers some promising possibilities with regards to visual interpretation of regional wall motion.30 However, attempts to simulate this "bull's-eye" approach have been quite time-consuming, and an intuitive method for the display of long axis function has been particularly evasive.

The development of the tissue motion imaging offers a way to bring together all of the recent variants of tissue Doppler imaging into one visually attractive and immediately interpretable form. Indeed, this technique may have some advantages over each form of tissue Doppler imaging. For example, strain rate and strain imaging have proved to be quite noisy and analysis of strain rate waveform suffers from poor inter-observer variability.28,29 To minimize this noise the ‘offset’ distance4 for strain imaging is typically set to 12–15 mm. However, tissue motion imaging may offer a much higher spatial resolution of strain (2–4 mm) with lower ‘noise’. Tissue motion imaging should be less noise sensitive than SRI as all SRI computations are based on the velocities extracted in two localizations rather than a single point for the tissue motion imaging. Furthermore, the assessment of multiple adjacent segments at any given time allows visual comparison not available in the waveform variants of tissue Doppler and strain imaging. Tissue motion imaging may reduce the confusion regarding myocardial placement of the reference cursor as is required by these approaches.

Feasibility and limitations
The tissue motion technique was feasible in all of the ultrasound cine-loops. The validity of the method should be clear as it is closely related to tissue tracking which has been validated against strain rates,31 contrast cineangiography,9 and magnetic resonance imaging.32 Achieving good tissue motion images was predominately dependent on the establishment of a carefully shaped M-mode placed in the mid-level of the myocardium. Without any tracking there is a risk of the M-mode falling into the bloodpool at some time during the cardiac cycle. However, because the lateral resolution is poor, especially in the far field, and the Doppler velocity is an average of all velocities recorded in the lateral direction,33 the velocities will in any case be composed of velocities from both the myocardium and the blood, whether the M-mode is placed close to the myocardium in the bloodpool or in the myocardial wall. Without tracking of the M-mode the anatomic region studied differs slightly over the heart cycle. These are, however, problems of the M-mode technique itself, and as such outside the scope of the current study.

The time consuming part of the process is opening the file in the post-processing program and drawing the M-mode. After this is done the generation of the TMI is done within a couple of seconds. In ultrasound machines which today provide real-time M-mode imaging there should be no obstacle for including the TMI as a real-time feature.

We arbitrarily chose to set the zero point for tissue motion imaging at the R-peak of the ECG because this point is more easily identified than e.g. the beginning of the QRS complex and lies just before the commencement of the myocardial contraction and the myocardium should be at its most elongated state at this point thus resulting in all motions subsequently registering as positive towards the apex. However, in practice, the myocardium has continuous deformation during the heart cycle, which leads to the minimum point of motion not occurring exactly at the R-peak for all points of the myocardium. In addition, the ‘integrational drift’, i.e. whenever there is a small offset in the original data it adds up throughout the integration causing the result to drift off linearly, may occur over multiple cardiac cycles and may lead to measurement uncertainty and error. As the integrational drift has the behavior of a constant being continuously added to the wanted result, the effect can be reduced by estimating this constant and subtracting it from the measurements.

TMI is applicable to any wall or segment of the heart and it is possible to extend the curved M-mode into the shape of a ‘horseshoe’ to display both walls of a two- or four-chamber ultrasound image in the same tissue motion image. It is also possible to concatenate images from subsequent heart cycles to get a better impression of the myocardial behavior over the QRS complex.

It should be kept in mind that the methods for calculating velocities and strains from the tissue motion images make use of quite wide ranges of the dataset for each calculation (several milliseconds or millimeters, respectively). This in reality has the same effect as a low-pass filter, and leads to rapid changes and high, fast peaks being lost, which is also seen in the reliability display of Fig. 7, especially in cases 3 and 4. What is lost in timing accuracy is, however, regained in the intuitivity of the display.

One way to increase the possibility of tracking rapid changes would be to change the colormap definitions. Instead of using the cut-offs of Table 2 where each color ranges over 2 cm/s, one could let each color span e.g. 1 or 0.5 cm/s. Our experience was that when the color spans became smaller than 2 cm/s the color bands in certain cases, especially during the E-wave, became so thin that this complicated the interpretation. However, we feel that in a final application the user should be able to adjust both the colors and the span of each color himself after his own preferences.

Future development would include a large number of normal and diseased subjects in order to describe the different TMI patterns in further detail.

Clinical applications
The application of tissue motion imaging could include quantization of stress echocardiography. A challenging area of imaging in clinical practice is that of assessment of diastolic dysfunction. The visual interpretation of rate of relaxation provided by tissue motion imaging may offer a regionally specific method of diagnosing such abnormalities, however, this requires further investigation.


   Conclusions
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusions
References
 
In this pilot study we have presented a new imaging technique, tissue motion imaging, and shown that it is feasible to visualize myocardial velocities, accelerations, displacements and strains for both systolic and diastolic function for the whole left ventricle from one image only. This approach may overcome many of the obstacles between previously described clinical tissue Doppler techniques and clinical use.


   Acknowledgements
 
The work is financed by the Swedish Heart Lung Foundation and the Swedish Foundation for Strategic Research.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusions
 References
 

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