- Research Article
- Open Access
Determination of Three-Dimensional Left Ventricle Motion to Analyze Ventricular Dyssyncrony in SPECT Images
© Marina de Sá Rebelo et al. 2010
- Received: 29 April 2009
- Accepted: 16 September 2009
- Published: 9 November 2009
A method to compute three-dimension (3D) left ventricle (LV) motion and its color coded visualization scheme for the qualitative analysis in SPECT images is proposed. It is used to investigate some aspects of Cardiac Resynchronization Therapy (CRT). The method was applied to 3D gated-SPECT images sets from normal subjects and patients with severe Idiopathic Heart Failure, before and after CRT. Color coded visualization maps representing the LV regional motion showed significant difference between patients and normal subjects. Moreover, they indicated a difference between the two groups. Numerical results of regional mean values representing the intensity and direction of movement in radial direction are presented. A difference of one order of magnitude in the intensity of the movement on patients in relation to the normal subjects was observed. Quantitative and qualitative parameters gave good indications of potential application of the technique to diagnosis and follow up of patients submitted to CRT.
- Left Ventricle
- Cardiac Resynchronization Therapy
- Optical Flow
- Spherical Coordinate System
- Septal Wall
The automatic quantification of dynamic events, like the heart movement, is one of the most challenging applications in the field of medical image analysis. The normal Left Ventricle (LV) wall deformation occurring throughout the cardiac cycle may be affected by cardiac diseases. Thus, some pathological conditions could be identified by the change they produce in the expected normal movement .
Ventricular dyssyncrony is an example of a condition that modifies the normal behavior of the cardiac muscle . Cardiac Resynchronization Therapy (CRT) is one of the procedures applied to patients with intraventricular dyssynchrony and aims to restore the normal contraction pattern by the stimulation of both right and left ventricles simultaneously . Several studies have shown the effectiveness of CRT in patients with heart failure [4, 5]. However, among the patients submitted to CRT, 25–30% do not respond to the treatment [6–9] (nonresponder). For this reason, when choosing CRT for a patient, several factors have to be considered. Besides being highly complex, it is an expensive therapy  and implantation of CRT device is not without risks to the patient . The decision of recommending CRT to a patient is therefore a balance of these risks with its potential benefits.
At present, there is a lack of specific measures to characterize the degree of synchrony  as well as a factor, which prior to the application of the CRT, can discriminate patients who are going to respond to the therapy from those who are not. A number of researchers have been working to reach this goal in the last years [12–14]. Recently, several studies have used gated scintigraphic images to evaluate the ventricles synchrony by means of phase and amplitude images . However, these two techniques involve a global analysis and may cause a loss of important information about the regional movement of the walls.
Electrocardiographic gating of Cardiac Single-Photon-Emission Computed Tomography (gated-SPECT) provides the clinician with a temporal set of 3D images that enables the visualization of the distribution of radioactive counts within the myocardium and surrounding structures throughout the cardiac cycle. It provides the ability to determine the severity of abnormalities in wall motion and wall thickening associated with myocardial dysfunction . A number of techniques have been used in order to describe and quantify the nonrigid motion of the cardiac structures. Among these techniques, Optical Flow methods are used to accurately model nonrigid motion present during the cardiac cycle so that a one-to-one mapping is found between each voxel of two gated volumes [16, 17].
In previous works, we have described cardiac motion by means of the velocity flow field. The velocity estimation for each voxel in a volume was based on Optical Flow techniques . In this technique, 3D LV motion is described by a series of 3D velocity vector fields computed automatically for each voxel on the sequence of cardiac volumes. The analysis and even the visualization of the velocity field in a cardiac volume are extremely difficult tasks, due to the high amount of information presented simultaneously. To make this bunch of information useful for diagnostic purposes, it is necessary to find compact and friendly representations for it.
In this work we propose a color coded visualization scheme for the qualitative analysis of the velocity components, with the definition of three movement directions. The coded velocity information obtained from Optical Flow in SPECT images is used to assess some aspects of CRT. In particular, we investigate the ability of velocity derived measurements to assess the effectiveness of CRT and velocity patterns that might be able to distinguish responder patients from the nonresponder, before the application of CRT. The assessment is performed on sets of images from thirty normal subjects and sixteen patients with idiopathic dilated cardiomyopathy.
In this section the proposed methods to compute (Section 2.1) and analyze (Section 2.2) the left ventricle motion are described. In Section 2.3 the image acquisition protocol and data sets used for methods evaluation are presented, as well as the criteria used for classification of the patients as responders or nonresponders to the CRT.
2.1. Description of Heart Movement Through Velocity Fields
2.1.1. Velocity Field Calculation
where the first term is the OF constraint, the second is a measure of the Optical Flow field smoothness, and is a weighting factor that controls the influence of the smoothness constraint. Ex, Ey, Ez and Et are the image derivatives in the x, y, z and t directions; u, v and w are the components of the local velocity vector v along the x, y and z directions, respectively.
The minimization of this function leads to a linear algebraic system, whose solution is the velocity component to each voxel and the coefficients are determined by the spatial and temporal derivatives of the images as follows:
2.1.2. Computational Description of the Left Ventricular Movement
2.2. Qualitative Analysis of the Movement: Color Coding the Velocity Field in Spherical Coordinates
2.2.1. Spherical Coordinate System
The radial movement can be described by the unit vector for the component, the horizontal rotation by the unit vector for the component, and the vertical movement by the unit vector for the component.
The center of the spherical coordinate system is essential when representing the left ventricular motion, as the origin is the reference point for the motion. The results for the velocity components are going to be highly dependent on the choice of this point. How to choose the center of the left ventricle is not a simple task. The anatomical center or the center of mass might be used as a central point, but this choice would fail to find the center in images from patients with myocardial infarction or any disease in which the counts are decreased at certain regions of the cardiac muscle. In this work, the center is defined as the geometrical center of the LV and is selected manually by a trained physician.
2.2.2. Color Scheme
A desired feature of the visualization scheme is that all information concerning a movement direction be presented in a single image. Thus each image must present information about the orientation and the intensity of the velocity component. The color coding scheme is therefore defined as following: for each component, the color assigned to a voxel indicates the orientation of the movement, being either positive or negative, and the strength of the color indicates the intensity of the velocity vector in this direction.
Radial: expansion is positive; contraction is negative,
Horizontal rotation: clockwise rotation is negative, and counterclockwise rotation is positive;
Vertical rotation: downwards motion is positive, and upwards rotation is negative.
2.3. Acquisition and Processing of Patient Images
The method was applied to 3D gated-SPECT (99mTc-MIBI) images obtained from sixteen patients with idiopathic dilated cardiomyopathy, chronic heart failure in New York Heart Association functional class III or IV, LV Ejection Fraction 35% and left bundle branch block (QRS 120 milliseconds), referred for implantation of a CRT device. The proposed protocol was approved by the Ethics Committee of the University of Sao Paulo Medical School and an informed consent was obtained from all study subjects and/or their families. The image acquisitions were performed at the Nuclear Medicine Department of the Heart Institute (InCor) HCFMUSP. All acquisitions were performed after the intravenous injection of 10 mCi of [technetium-99 m] sestamibi at rest in a dual-head rotating gamma camera (ADAC Cardio-MD with a LEAP Collimator). The acquisition process is synchronized with the electrocardiogram and the cardiac cycle was divided into 8 frames/cycle. A total of 64 projections were obtained over a semicircular 180-deg orbit. All projection images were stored using a , 16-bit matrix. The transverse tomograms were reconstructed with a thickness of 1 pixel/slice (6.47 mm). The volume of transverse tomograms was reoriented, and sets of slices perpendicular to the long axis (short axis view) and of slices parallel to the long axis (vertical long axis view and horizontal long axis view) were created. For each patient the images were acquired in two different conditions: at rest and after pharmacological induced stress.
From the group of sixteen patients, eight were responders to the CRT (Group1), and eight patients were nonresponders (Group2). For each patient, the rest and stress data sets were analyzed before to and after CRT, respectively. This gave a total of 64 gated-SPECT data sets included in this analysis. Before the implantation of the CRT device, the clinical condition of the patients was assessed and they were subsequently scanned with three different image modalities: gated-SPECT, echocardiography, and gated blood pool imaging. The aim was to gain an estimated left ventricular Ejection Fraction (EF) from each image modality, for later use as a quantitative measure of the response. After a three-month follow-up, the patients were submitted to the same procedures as prior to CRT. The majority of patients improve immediately their EF or functional class post-CRT implant. Estimates of the EF from each image modality were acquired a second time and compared with the estimated baseline EF. A positive response to CRT was defined as an increase of at least 5 percent points in one or more of the three modalities in addition to a positive clinical assessment. Patients who showed a positive response are named responders, and the ones who did not are named nonresponders.
The method was also applied to image sets of thirty normal subjects (The normal subjects whose images were used in this work were part of a Research protocol approved by the Ethics comittee of the University of Sao Paulo Medical School.), whose acquisition protocol is the same as the one described for the patient images.
3.1. Results for Normal Subjects
By analyzing normal left ventricles, the resulting visualization of the motion patterns can be compared with the motion expected from the heart physiology (seen in Figure 1).
3.1.1. Radial Movement
During systole, as the left ventricle ejects blood, the myocardium contracts starting at the apex and moving upwards to the base. Simultaneously, the septal and lateral walls move towards the center of the left ventricle. Therefore, the expected result in systole is the contraction which is presented in the Figure 4, line 2, column a. In this image, the contraction movement is represented by different tones of red, indicating the contraction with varying intensities. After ejection the heart enters the diastole, where the overall motion is opposite of the contraction. The expected colors are therefore also the opposite of the ones observed in systole. The results of a normal wall behavior can be observed in Figure 4, line 1, column a, where the expansion movement is depicted as different tones of blue.
3.1.2. Horizontal Rotation
The analysis of this movement is quite complicated. If one studies the anatomy and physiology of the subepicardial and subendocardial myofibres during both systole and diastole, it would be expected that images would show opposite rotations in the outer and inner sides of the myocardium. This could not be seen in any of the slices of any subjects. Instead, it seems like different rotary motions govern at different parts of the myocardium. The results in the midcavity slices form four corners, where opposing corners have movements with the same direction (see Figure 4, lines 1 and 2, column b). This pattern was similar in all normal left ventricles, hence it was assumed as the normal pattern in the horizontal motion. The results obtained show the expected opposite relationship between systole and diastole.
3.1.3. Vertical Rotation
In the ejection phase, the apex is pressed upwards during contraction to force the blood out through the aortic valve. The expected result of the vertical rotation in systole is therefore an upwards rotation, which is coded as red, this is also seen in Figure 4, line 2, column c. In diastole, the images show movement in the opposite direction.
3.2. Results for Patients
3.2.1. Radial Movement
Prior to CRT a dyssynchrony is present in form of a blue (expanding) septal wall and a red (contracting) lateral wall. In the image after CRT, an improved synchrony is visible; here the blue color in the circle is replaced by a weak red color. During diastole in Figure 5, only the lateral wall is expanding before CRT, as the septal wall was expanding in systole. After CRT a more synchronic expansion is detected. It is seen that the overall intensity of the movement is weaker when compared to the normal subjects. The analysis of synchrony in the Group2 showed that there was no improvement in most of patients, as expected.
3.2.2. Horizontal Rotation
For Group2 no such pattern in the horizontal rotation was detected. One patient showed a pattern similar to a normal pattern in systole, but a worsening in diastole, while others showed the opposite or a mixture of rotations. None of the patients had a similar pattern of improvement or deterioration in synchrony. The intensity of motion was similar prior to and after CRT in all patients in systole, but in diastole half of the patients had a high intensity in horizontal rotation before CRT, which decreased after CRT. This behavior of noticeable decreased velocity intensity values was not detected in the Group1.
3.2.3. Vertical Rotation
3.3. Analysis of Radial Movement—Normal and Patient
Mean intensity values for radial motion of one normal subject and two patients, one of Group1 and one of Group2, before and after CRT. Values presented for the walls depicted in Figure 1—anterior, inferior, septal, and lateral—for the LV midcavity portion. The computed intensity values are mapped to a scale that allows a maximum of and a correspondent minimum of .
The comparison between patients (Group1 and Group2) and normal subjects shows that not only the synchrony of the movement is compromised, but the intensity is seriously decreased in this set of patients, which reflects the impaired heart function. The numerical values representing the quantity of movement of the normal subjects are ten times higher than the patients. Although this quantity does not change considerably before and after the CRT, the responder patient presents an overall increase in the clinical conditions due to the fact that the synchrony of the movement has been restored. This fact can be seen in Table 1 by the change in the expected sign for the measurement. Patients from the Group2 did not present this improvement in synchrony.
The analysis of the velocity field from cardiac volumes can give important clues about the dynamic events occurring during the cardiac cycle, which may help to understand how some treatments improve heart function. In this work, the results were presented in a slice of the short axis view and we proposed a scheme for displaying the wall movements which are displayed using a compressive color code that integrates orientation and intensity of the velocity vector at each voxel.
The most important feature of this method is its capability to evaluate LV motion in a more comprehensive way since it allows a regional analysis by assessing the movement in three predefined directions. Other techniques (like echocardiography and phase images derived from Fourier transform of radionuclide ventriculography or even gated single photon emission computed tomography) use previously defined points (or regions) and establish a comparison between them or evaluate indices that characterize global LV synchrony [2, 7, 10, 19].
In this study, the results from the normal subjects were used as the reference for normality in each of the directions. The representation of the velocity components in a color coded image has shown to be an efficient tool for regional inspection of the LV wall movement that could improve the optimal site of LV electrode implant. Actually, the method allows a local analysis, since the results are obtained for each voxel of the volume. This is an important advantage of this method when compared to other global techniques such as phase and amplitude.
Table 1 shows a quantitative comparison of one data set obtained in normal controls, and two patients, one who responded to CRT and one nonresponder. A difference of one order of magnitude in the intensity of the movement on patients in relation to the normal subjects was observed. The evaluation of radial motion before CRT in a nonresponder patient (group 2) showed a movement pattern different from normal in both phases of the cardiac cycle. The responder (group 1) showed motion in the opposite direction from normal controls only in inferior and septal walls. This fact could suggest that responders are different from nonresponders before therapy. After therapy, the direction of the motion of inferior and lateral walls of the nonresponder became similar to normal controls, but not the direction of anterior and septal walls. The group 1 patient showed a normal motion pattern except in inferior wall after therapy. The qualitative and quantitative parameters obtained with this method could add information to a better selection of patients who would respond to TRC and provide a measurable tool to the follow-up in this population.
the spherical coordinate system was chosen for calculating the orientation and intensity of the left ventricular motion. A key issue to the proposed scheme is the center of the spherical coordinate system since it is the reference point for the motions and therefore essential in the visualization of the velocity components. A change in center will influence both the intensity and orientation of the left ventricular motion. Choosing the center is difficult as it should be the exact point or axis from which the motion starts and ends. In the present work, the center was determined manually by a trained observer as the geometrical center of the LV.
Another limitation is the poor resolution of SPECT images that sometimes makes it difficult to analyze the movements. It must be added, however, that the proposed method is not applicable to nuclear medicine imaging only and can be extended to other modalities.
Future Perspectives and Conclusions
the results are preliminary indications obtained via a qualitative assessment. Quantitative indexes can be created based on these images that would be able to quantitatively assess both the effectiveness and prediction of CRT response. These indexes could be based on the creation of normal distributions of the velocity field for each direction. An alternative and elegant approach for defining quantitative tools for the analysis of the movement patterns is the creation of a functional bull's eye [20–23]. Once the bull's eyes of the described movement patterns have been built, many studies can be performed for the assessment of the patient's condition. In order to find an index to predict response to CRT therapy, extensive clinical studies must be performed and involve the acquisition of a statistically significant number of images from normal subjects and patients.
In this study, the left ventricular three-wall movements were studied using a compressive color code that characterizes the integration of orientation and intensity of the velocity vector at each voxel. This new technique of myocardial synchronization assessment might be able to distinguish responder patients from the nonresponders and improve the follow up of patients who underwent CRT.
The authors would like to thank Dr. Ramon Moreno, Maurício Higa, and Carlos Santos for their valuable discussions at the elaboration of this work. This work was supported in part by the Foundation of Aid for Research of São Paulo State (FAPESP) Grant no. 2006/06612-4, the National Council for Scientific and Technological Development (CNPq) Grant no. 300499/2005-1, the National Institute of Science and Technology—Medicine Assisted by Scientific Computing INCT MACC, and the Zerbini Foundation.
- Remme EW, Young AA, Augenstein KF, Cowan B, Hunter PJ: Extraction and quantification of left ventricular deformation modes. IEEE Transactions on Biomedical Engineering 2004, 51(11):1923-1931. 10.1109/TBME.2004.834283View ArticleGoogle Scholar
- Chen J, Garcia EV, Folks RD, et al.: Onset of left ventricular mechanical contraction as determined by phase analysis of ECG-gated myocardial perfusion SPECT imaging: development of a diagnostic tool for assessment of cardiac mechanical dyssynchrony. Journal of Nuclear Cardiology 2005, 12(6):687-695. 10.1016/j.nuclcard.2005.06.088View ArticleGoogle Scholar
- Rioual K, Unanua E, Laguitton S, et al.: MSCT labelling for pre-operative planning in cardiac resynchronization therapy. Computerized Medical Imaging and Graphics 2005, 29(6):431-439.View ArticleGoogle Scholar
- Auricchio A, Abraham WT: Cardiac resynchronization therapy: current state of the art: cost versus benefit. Circulation 2004, 109(3):300-307. 10.1161/01.CIR.0000115583.20268.E1View ArticleGoogle Scholar
- Linde C, Braunschweig F, Gadler F, Bailleul C, Daubert J-C: Long-term improvements in quality of life by biventricular pacing in patients with chronic heart failure: results from the MUltisite STimulation In Cardiomyopathy Study (MUSTIC). American Journal of Cardiology 2003, 91(9):1090-1095. 10.1016/S0002-9149(03)00154-1View ArticleGoogle Scholar
- Lecoq G, Leclercq C, Leray E, et al.: Clinical and electrocardiographic predictors of a positive response to cardiac resynchronization therapy in advanced heart failure. European Heart Journal 2005, 26(11):1094-1100. 10.1093/eurheartj/ehi146View ArticleGoogle Scholar
- Bleeker GB, Bax JJ, Fung JW-H, et al.: Clinical versus echocardiographic parameters to assess response to cardiac resynchronization therapy. American Journal of Cardiology 2006, 97(2):260-263. 10.1016/j.amjcard.2005.08.030View ArticleGoogle Scholar
- Yu C-M, Bleeker GB, Fung JW-H, et al.: Left ventricular reverse remodeling but not clinical improvement predicts long-term survival after cardiac resynchronization therapy. Circulation 2005, 112(11):1580-1586. 10.1161/CIRCULATIONAHA.105.538272View ArticleGoogle Scholar
- Yeim S, Bordachar P, Reuter S, et al.: Predictors of a positive response to biventricular pacing in patients with severe heart failure and ventricular conduction delay. Pacing and Clinical Electrophysiology 2007, 30(8):970-975. 10.1111/j.1540-8159.2007.00794.xView ArticleGoogle Scholar
- Brandão SCS, Giorgi MCP, de Miche RT, et al.: Ventricular synchrony in patients with dilated cardiomyopathy and normal individuals: assessment by radionuclide ventriculography. Arquivos Brasileiros de Cardiologia 2007, 88(5):596-601. 10.1590/S0066-782X2007000500016View ArticleGoogle Scholar
- Strickberger SA, Conti J, Daoud EG, et al.: Patient selection for cardiac resynchronization therapy: from the Council on Clinical Cardiology Subcommittee on Electrocardiography and Arrhythmias and the Quality of Care and Outcomes Research Interdisciplinary Working Group, in collaboration with the Heart Rhythm Society. Circulation 2005, 111(16):2146-2150. 10.1161/01.CIR.0000161276.09685.4AView ArticleGoogle Scholar
- O'Connell JW, Schreck C, Moles M, et al.: A unique method by which to quantitate synchrony with equilibrium radionuclide angiography. Journal of Nuclear Cardiology 2005, 12(4):441-450. 10.1016/j.nuclcard.2005.05.006View ArticleGoogle Scholar
- Declerck J, Feldmar J, Ayache N: Definition of a four-dimensional continuous planispheric transformation for the tracking and the analysis of left-ventricle motion. Medical Image Analysis 1998, 2(2):197-213. 10.1016/S1361-8415(98)80011-XView ArticleGoogle Scholar
- Tecelão SRR, Zwanenburg JJM, Kuijer JPA, et al.: Quantitative comparison of 2D and 3D circumferential strain using MRI tagging in normal and LBBB hearts. Magnetic Resonance in Medicine 2007, 57(3):485-493. 10.1002/mrm.21142View ArticleGoogle Scholar
- Klein GJ, Reutter BW, Huesman RH: Non-rigid summing of gated PET via optical flow. IEEE Transactions on Nuclear Science 1997, 44(4):1509-1512. 10.1109/23.632704View ArticleGoogle Scholar
- Gutierrez MA, Rebelo MS, Furuie SS, Meneghetti JC: Automatic quantification of three-dimensional kinetic energy in gated myocardial perfusion single-photon-emission computerized tomography improved by a multiresolution technique. Journal of Electronic Imaging 2003, 12(1):118-124. 10.1117/1.1526104View ArticleGoogle Scholar
- Horn BKP, Schunck BG: Determining optical flow. Artificial Intelligence 1981, 17(1–3):185-203.View ArticleGoogle Scholar
- Cerqueira MD, Weissman NJ, Dilsizian V, et al.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation 2002, 105(4):539-542. 10.1161/hc0402.102975View ArticleGoogle Scholar
- Brandão SCS, Nishioka SAD, Giorgi MCP, et al.:Cardiac resynchronization therapy evaluated by myocardial scintigraphy with -MIBI: changes in left ventricular uptake, dyssynchrony, and function. European Journal of Nuclear Medicine and Molecular Imaging 2009, 36(6):986-996. 10.1007/s00259-008-1029-1View ArticleGoogle Scholar
- Qureshi RJ, Husain SA: Design of na expert system for diagnosis of coronary artery disease using myocardial perfusion imaging. Proceedings of the National Conference on Emerging Technologies, 2004 100-105.Google Scholar
- Rougon N, Petitjean C, Prêteux F, Cluzel P, Grenier P: A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures. Medical Image Analysis 2005, 9(4):353-375. 10.1016/j.media.2005.01.005View ArticleGoogle Scholar
- Gérard O, Billon AC, Rouet J-M, Jacob M, Fradkin M, Allouche C: Efficient model-based quantification of left ventricular function in 3-D echocardiography. IEEE Transactions on Medical Imaging 2002, 21(9):1059-1068. 10.1109/TMI.2002.804435View ArticleGoogle Scholar
- Lin J-W, Laine AF, Bergmann SR: Improving PET-based physiological quantification through methods of wavelet denoising. IEEE Transactions on Biomedical Engineering 2001, 48(2):202-212. 10.1109/10.909641View ArticleGoogle Scholar
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