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Left Ventricular Mass and Volume: Fast Calculation with Guide-Point Modeling on MR Images¹

¹ From the Departments of Anatomy with Radiology (A.A.Y.), Medicine (B.R.C., S.F.T.), and Engineering Science (W.J.H.), University of Auckland, 85 Park Road, Auckland, New Zealand; and the Department of Medicine, University of Alabama at Birmingham, Ala (L.J.D.). Received August 13, 1999; revision requested October 5; revision received November 12; accepted December 20. Supported in part by the Auckland Medical Research Foundation and the Health Research Council of New Zealand. Address correspondence to A.A.Y. (e-mail: a.young@auckland.ac.nz).

	ABSTRACT

TOP ABSTRACT INTRODUCTION Materials and Methods Results Discussion APPENDIX REFERENCES

 
The authors describe a fast method for calculating left ventricle (LV) mass and volumes from multiplanar magnetic resonance (MR) images. Mathematic models were fitted to a small number of user-selected guide points in 15 healthy volunteers, 13 patients after myocardial infarction, and a canine model of mitral regurgitation in eight dogs. Errors between model and manual contours were small (LV mass, 1.8 g ± 4.9 [mean ± SD]; end-diastolic volume, 2.2 mL ± 4.6; end-systolic volume, 2.3 mL ± 3.8). Estimates of global function could be obtained in 6 minutes, a time saving of 5–10 times over estimates with manual contouring.

Index terms: Heart, volume, 51.92 • Magnetic resonance (MR), phase imaging, 51.12144 • Magnetic resonance (MR), rapid imaging, 51.121416 • Magnetic resonance (MR), volume measurement, 51.12144

	INTRODUCTION

TOP ABSTRACT INTRODUCTION Materials and Methods Results Discussion APPENDIX REFERENCES

 
Left ventricle (LV) mass and volumes at end-diastole (ED) and end-systole (ES) are essential clinical parameters for the diagnosis and management of cardiac disease. Magnetic resonance (MR) imaging is able to provide accurate and precise estimations of LV mass and volume since it is a true three-dimensional (3D) method that is not dependent on geometric assumptions and is not limited in the position or orientation of the image sections (unlike echocardiography or computed tomography [CT]) (1,2). Current imaging techniques allow the acquisition of 10–20 sections in short- and long-axis orientations, each with 10–20 frames through the cardiac cycle, in clinically acceptable times (<15 minutes) (3,4). Typically, LV boundaries (contours) are drawn on each section of a series of short-axis images and the outlined areas converted to volumes and summed to produce estimates of volume and mass. Previous studies have shown that this technique gives more accurate and reproducible estimates of volume and mass than do other imaging modalities (2,5–7).

A major limitation of the MR imaging section summation method is the prohibitive time necessary to outline the inner and outer boundaries of the LV in each section at ED and ES. This image contouring bottleneck precludes the use of multiplanar MR imaging in routine clinical care and limits its application to small research trials. Many semiautomated image segmentation algorithms have been applied to this problem (8–10), but these algorithms are not sufficiently robust for routine clinical use. Image pixel intensities are insufficient to adequately constrain the segmentation problem, owing to the limited temporal and spatial resolution, presence of image artifacts, and lack of contrast between blood and muscle. The endocardial trabeculae and papillary muscles also make the inner boundary difficult to define. At present, the amount of time spent on manual editing and correction renders automated methods almost as slow as manual contouring in clinical practice.

In this study, we evaluated a fast, accurate method of calculating LV mass and volumes from multiple MR sections, without the need for image contouring. The method relies on an accurate mathematic model of the LV that is fitted to a relatively small number of data points (guide points) provided by the user. No image processing is required and the method is therefore independent of image quality.

	Materials and Methods

TOP ABSTRACT INTRODUCTION Materials and Methods Results Discussion APPENDIX REFERENCES

 
Data Acquisition
To determine the accuracy and efficiency of this method, we analyzed results in 15 healthy volunteers and 13 patients with regional abnormalities in wall motion due to myocardial infarction. Informed consent was obtained for all human studies, which were performed with the approval of the institutional ethical committees.

Volunteers.—A group of 15 healthy subjects (10 men and five women; mean age, 23 years; age range, 18–32 years) underwent MR imaging with a 1.5-T imager (Vision; Siemens, Erlangen, Germany). Prospectively gated cardiac cine images were acquired in eight to nine short-axis sections and three long-axis sections with use of a segmented k-space pulse sequence (repetition time msec/echo time msec of 8/5, flip angle of 10°, field of view of 280–350 mm) with view sharing (11–19 frames per section) (3,4). Each section was acquired during a breath hold of 15–19 cardiac cycles. The short-axis sections spanned the heart from apex to base with a section thickness of 8.0 mm and section gap of 0.0–3.0 mm.

Phase-contrast velocity images (11) were acquired in a section positioned perpendicular to the ascending aorta approximately 20 mm above the aortic valve. Since the entire imaging protocol performed with the volunteers consisted of several hours of imaging (including tagging and many flow-quantification sequences), all subjects had a break of 1–2 hours between cine anatomic imaging (including the short-axis cine sections used in the volume calculations) and the phase-contrast flow imaging. At the latter, prospectively gated cine gradient-echo imaging was performed (24/6, flip angle of 30°, field of view of 250 mm, section thickness of 8.0 mm, velocity encoding of 150 or 250 cm/sec, and 25–45 frames through the cardiac cycle). Velocity sensitization was encoded in the through-plane (section-select) direction.

Patients.—MR imaging data from 13 patients, who underwent MR imaging as part of a previous study (12) at 2 weeks (n = 6) and 3 months (n = 7) after their first acute Q wave myocardial infarction, were selected for analysis of global function. The 13 studies were selected consecutively in alphabetical order from a database of 70 studies, details of which are given by Johnson et al (12). All MR studies were performed with a 1.5-T imager (Gyroscan; Philips, Shelton, Conn). Respiration-compensated gradient-echo cine images (30/14, flip angle of 40°, field of view of 300–400 mm) were acquired in nine to 10 short-axis sections, each 8.0-mm thick with a 1.0-mm section gap.

Animal studies.—MR data from eight dogs imaged 5–6 months after induction of mitral regurgitation, as part of a previous study (13) approved by the institutional animal care committee, were analyzed by means of guide-point modeling. Mitral regurgitation was induced by means of percutaneous chordal rupture of the mitral valvular apparatus, as described in reference 13. MR imaging was performed with a 1.5-T imager (Gyroscan) with the same gradient-echo cine imaging sequences as were used with the patients (six to eight short-axis sections, 8-mm-thick sections, 1.6–4.0 section gap, field of view of 350 mm). After imaging, the hearts were arrested with KCl and removed from the chest. After the atria and right ventricular free wall were removed from the interventricular septum and LV, the portions were weighed.

Image Analysis
Manual contouring and section summation.—The inner and outer boundaries of the LV were defined on each image as follows. First, a semiautomatic region-growing method was used to locate preliminary boundaries (10). These boundaries were then manually corrected for each image. To the best ability of the observer (S.F.T.), papillary muscles were excluded from the myocardium and included in the blood pool. Great care was taken to identify the exact boundary of the myocardium. The contours were then reviewed by another observer (B.R.C.), and a consensus was achieved in all cases where there was a difference in opinion. Figure 1 shows short-axis sections at ED and ES together with the final boundaries (called "manual contours" in this article since extensive manual correction of the segmentation result was required). The areas outlined in each image were then multiplied by the section thickness plus the section gap and the resultant volumes summed to produce estimates of ED and ES volumes. LV mass was calculated from the mean of the ED and ES myocardial volumes (as the difference between the volumes enclosed by the epicardial and endocardial contours) multiplied by 1.05 g/mL.

View larger version (148K): [in this window] [in a new window] [Download PPT slide]   Figure 1. ED (left) and ES (right) gradient-echo short-axis images with manual contours shown as lines. Top: Volunteer images (8/5 with 10° flip angle). Bottom: Patient images (30/14 with 40° flip angle). Note the wall motion abnormality (lack of motion and wall thickening) in the basal lateral wall on the patient image (arrow).

Model Definition
A finite element model was used to represent the geometry of the LV. This model was similar to those used previously to describe LV deformation (14,15). The model consisted of 16 elements, each with cubic interpolation in the circumferential and longitudinal directions. Linear interpolation was used to couple the inner and outer surfaces into a coherent 3D model. The model was defined in a polar coordinate system (Appendix). This simplified the solution process, allowing the radial coordinate () of the model to be fitted as a function of the two angular coordinates ( and µ in the circumferential and longitudinal directions, respectively). Use of a polar coordinate system imposes two constraints on the shape of the model. First, there must be a straight "central axis" passing within the LV cavity from apex to base. Second, the location of the LV inner and outer surfaces must be uniquely specified on the basis of a radial distance from the central axis at all points. These constraints are not as severe as those typically applied in the case of echocardiography (16), and the piecewise cubic interpolation allows a variety of normal and deformed LV shapes to be accurately modeled, including LV aneurysms and severe hypertrophy (as in reference 14).

Initially, the model was scaled to each heart according to the distance between the base and the apex of the LV. The initial shape of the LV model was a regular ellipsoid, which was obtained by setting the inner and outer surfaces to a constant radial value. The extent of the model in the longitudinal (µ) direction was set to correspond to the most basal extent of the LV in the long-axis images at each time point.

Guide-Point Modeling
Guide points were interactively placed on the images by the user and the two-dimensional image coordinates converted to 3D coordinates with the section position information encoded in the image header. The model shape was fitted to the 3D guide-point positions by minimizing an error function consisting of the sum of a smoothing term and a term penalizing the distance between each guide point and the corresponding model position (Appendix). The smoothing term penalized changes in slope and curvature around the LV, allowing the model to realistically interpolate the very sparse guide-point data. The error function was quadratic in the model parameters, resulting in a simple linear least squares solution process. As the user placed or modified guide points, the model fit was updated in real time. The intersections of the model with the image planes were recalculated after each fit by means of a subdivision algorithm (17) and redisplayed on each image. The user continued to interactively add or modify guide points until the model-image intersections provided a good representation of the boundaries of the LV. Figure 2 shows the intersections of the model with the images shown in Figure 1.

View larger version (153K): [in this window] [in a new window] [Download PPT slide]   Figure 2. Same images as in Figure 1 with model contours shown as light lines to indicate results of guide-point (•) fit.

Model Mass and Volume Comparisons
LV mass and volume could be estimated from the model in a number of ways. To assess the accuracy of the technique, we needed a consistent method of volume calculation that could be used for both manual and guide-point modeling methods; therefore, we used the intersections of the model with each image section plane as model contours at ED and ES (Fig 2). The model contours were then input to the same custom-written software used to calculate the volumes from the manual contours. In this way, the ability of the modeling process to reproduce the manual contours for the purposes of volume calculation could be directly assessed.

Statistical Tests
Volumes and mass were compared between the two methods by means of paired t tests. Differences with a P value of .05 were considered statistically significant.

	Results

TOP ABSTRACT INTRODUCTION Materials and Methods Results Discussion APPENDIX REFERENCES

 
Figure 2 shows the result of the fitting process on typical short-axis images obtained in a volunteer and a patient at 3 months after myocardial infarction. Figure 3 shows the corresponding 3D LV models and illustrates the ability of the model to describe distorted LV shapes. This patient’s infarct was located in the basal lateral LV wall, leading to a wall motion abnormality in this region, which is clearly seen in Figure 3. For the 15 volunteers, a mean 66 guide points (range, 49–81) were needed for each study to fit the original images (approximately six per section). For the 13 patients, a mean 32 guide points (range, 22–51) were needed for each study (approximately three per section). Approximately 6 minutes were required by each operator to generate the guide-point models at ED and ES and estimate LV mass and volumes, ejection fraction, and stroke volume for each study. This represents an improvement of five to 10 times over the times required for manual contouring (Table 1).

View larger version (61K): [in this window] [in a new window] [Download PPT slide]   Figure 3. Three-dimensional displays of the model fits in Figure 2. + = endocardial guide points. Endocardial surface is shaded, and element boundaries are drawn as lines. Interventricular septum is to the left. Note the distorted LV shape at ES on the patient image owing to the basal lateral infarct (arrow).

View larger version (37K): [in this window] [in a new window] [Download PPT slide]   Figure 4. Bland-Altman plots show differences in global function parameters (model minus manual) versus the mean of the two methods. A, ED volume (EDV); B, ES volume (ESV); C, stroke volume (SV); D, ejection fraction (EF); and E, LV mass. and  = volunteers, and  = patients; and  = observer 1, and  = observer 2.

View larger version (13K): [in this window] [in a new window] [Download PPT slide]   Figure 5. A, Bland-Altman plot shows differences in stroke volume (SV) between phase-contrast flow measurements (corrected for heart rate) and manual method () and modeling method ( = observer 1,  = observer 2) in volunteers. B, Bland-Altman plot shows differences in mass between postmortem measurements and modeling method (single observer) in eight dogs.

	Discussion

TOP ABSTRACT INTRODUCTION Materials and Methods Results Discussion APPENDIX REFERENCES

 
The guide-point modeling method can be used with any mathematic model and 3D image data set (eg, 3D ultrasonography or CT). Use of smoothing constraints in conjunction with guide points provided an intuitive and efficient user interface with which to manipulate the model. Since the position of the guide points influenced the model shape in a consistent 3D manner, relatively few guide points were required for adequate fits to the images (three to six points per section), which reduced the amount of user interaction required. In our experiments, more guide points were needed for the volunteers than for the patients with wall motion abnormalities. The breath-hold volunteer images were of much higher quality than were the non–breath-hold patient images, with minimal motion artifact, better signal-to-noise ratio and greater contrast between blood and muscle, particularly about the papillary muscles and endocardial trabeculae. This higher quality may have contributed to the increased number of guide points required for good correspondence between the model and the image in these cases.

The guide-point modeling method relies on the user to successively refine placement of the model on the images. No image processing is required. Current methods that rely on image processing lack the robustness and accuracy necessary for successful clinical application; however, local image information can be included in the model optimization (18).

LV absolute volumes tended to be overestimated with the model contours of operator 1 relative to operator 2 and the manual contours in the healthy group. These differences were spread evenly over all sections and could be equated with a small bias in the perceived position of the boundary. It is vital in any institution that a consensus be achieved among all operators in the placement of ventricular boundaries on the images. Small biases in contour placement can easily lead to large errors in volumes and mass. In this study, the SDs of the differences between manual and modeling methods were small, and the bias is unlikely to be clinically important.

Results of analysis of covariance comparing phase-contrast aortic flow measurements with manual and model estimates of stroke volume showed that there was no effect (other than heart rate) due to the measurement method. The comparison of LV mass with postmortem measurements showed an apparent overestimation of mass with the guide-point method, since papillary muscles were included in the postmortem weighing but were excluded from the guide-point model. The eight dogs were studied after 5–6 months of mitral regurgitation caused by rupture of the chordae and thus exhibited severe LV remodeling due to chronic volume overload (13). The contribution of the papillary muscles to LV mass was therefore likely to be smaller than usual, and some papillary muscle may have been included in the guide-point model owing to failure to distinguish it from LV wall. Also, errors due to trabeculation of the inner surface of the LV, possible inclusion of epicardial fat (together with the specific gravity factor of 1.05 g/mL used for comparison), and the slight overestimation of model mass relative to manual contours (Tables 2, 3) may have combined to produce the apparent overestimation.

The guide-point modeling method was found to reduce the time required to estimate LV mass, ED volume, ES volume, stroke volume, and ejection fraction by a factor of 5–10 without loss of accuracy. In conjunction with recent advances in fast cardiac imaging (3,4), a comprehensive cardiac examination including imaging (scout images plus eight to 12 cine sections in short- and long-axis orientations) and analysis (global and possibly regional function) would typically require less than half an hour to complete. This time saving enables high-spatial-resolution multiplanar MR studies of cardiac function to be efficiently used in routine clinical practice.

	APPENDIX

TOP ABSTRACT INTRODUCTION Materials and Methods Results Discussion APPENDIX REFERENCES

 
A finite element model was constructed as described previously (14,15,19). Within each element, the geometry of the LV was given by a weighted mean of values at the nodes:

The model was fitted to guide-point data by minimizing the following error function:

S()

u =  - *,

The error function (Eq [A3]) was quadratic in the unknown nodal parameters (ⁿ), allowing direct solution by means of the standard least squares methods.

	ACKNOWLEDGMENTS

 
We thank Dr Tom Gentles, MBChB, (Department of Paediatrics) and Dr Chris Occleshaw, MBChB, (Department of Radiology) of Green Lane Hospital, Auckland, New Zealand, for their assistance with the recruitment and imaging of the volunteers. We also thank Andrew Cantell, BE, for his technical expertise.

	FOOTNOTES

 
Abbreviations: ED = end-diastolic, ES = end-systolic, LV = left ventricle, 3D = three-dimensional

Author contributions: Guarantor of integrity of entire study, A.A.Y.; study concepts, A.A.Y., B.R.C.; study design, A.A.Y.; definition of intellectual content, A.A.Y., B.R.C.; literature research, A.A.Y.; clinical studies, B.R.C., L.J.D.; experimental studies, A.A.Y., S.F.T., B.R.C., L.J.D.; data acquisition, A.A.Y., S.F.T., B.R.C., L.J.D.; data analysis, A.A.Y., S.F.T., W.J.H.; statistical analysis, A.A.Y.; manuscript preparation, A.A.Y.; manuscript editing and review, A.A.Y., S.F.T., W.J.H., B.R.C.

	REFERENCES

TOP ABSTRACT INTRODUCTION Materials and Methods Results Discussion APPENDIX REFERENCES

 

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This Article Abstract Figures Only Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Young, A. A. Articles by Dell’Italia, L. J. Search for Related Content PubMed PubMed Citation Articles by Young, A. A. Articles by Dell’Italia, L. J.