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Technical Developments |
1 From the Departments of Radiology (R.W.v.d.M., J.D., A.d.R., H.J.L.), Cardiology (J.J.B.), and Endocrinology (S.H., J.W.A.S., J.A.R.), Leiden University Medical Center, Albinusdreef 2, C2-S, 2333 ZA Leiden, the Netherlands; Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology, Zurich, Switzerland (S.K.); Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M.S.); Philips Medical Systems, Cleveland, Ohio (M.S.); and Department of Endocrinology, Free University Medical Center, Amsterdam, the Netherlands (M.D., L.J.R.). Received November 7, 2006; revision requested January 11, 2007; revision received January 25; final version accepted March 7. Address correspondence to H.J.L. (e-mail: h.j.lamb{at}lumc.nl).
| ABSTRACT |
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© RSNA, 2007
| INTRODUCTION |
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Motion artifacts from cardiac and respiratory motion have a negative effect on the reliability of myocardial 1H MR spectroscopy. Motion of the heart relative to the volume of interest may lead to reduced spectral resolution and contamination of the 1H MR spectrum by, for example, epicardial fat. In addition, respiratory motion may negatively influence 1H MR spectral resolution by preventing optimal shimming and water suppression.
Several methods for respiratory gating have been proposed to improve repeatability and spectral resolution at 1H MR spectroscopy (2,3,9). Recently, respiratory navigator gating and volume tracking for double-triggered cardiac 1H MR spectroscopy became available (10). However, the influence of respiratory navigator gating on spectral resolution and on the reproducibility of myocardial TG measurements is unknown. Therefore, the purpose of our study was to prospectively compare spectral resolution and reproducibility of 1H MR spectroscopy, with and without respiratory motion compensation based on navigator echoes, to assess myocardial TG in the human heart.
| MATERIALS AND METHODS |
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In 20 volunteers (14 men, six women; mean age ± standard error, 31 years ± 2.8 [range, 19–60 years]; body mass index, 19–30 kg/m2) without a history of cardiovascular disease, 1H MR spectroscopy of the myocardium was performed at rest. Furthermore, one healthy male subject (age, 22 years; body mass index, 23 kg/m2) underwent 1H MR spectroscopy before and after a very low calorie diet. This healthy subject had no history or clinical evidence of cardiovascular disease, diabetes, or any other chronic disease (screening visit consisted of a medical history, physical examination, electrocardiography [ECG], and screening laboratory tests such as fasting plasma glucose and lipid levels and an oral glucose tolerance test). The medical ethical committee of our institution (Leiden University Medical Center, Leiden, the Netherlands) approved our study protocol, and all participants gave informed consent.
Study Design
ECG-triggered 1H MR spectroscopy was performed twice during one session with the same parameters, without changing the position of the voxel, both with and without the use of respiratory navigator gating and volume tracking. Thereafter, the volunteer was removed from the imager. After 5 minutes, the volunteer was repositioned in the magnet bore, and ECG-triggered 1H MR spectroscopy was repeated with and without respiratory navigator gating and volume tracking after completing all preparation phases (see below). No marking of coil position on the chest wall of subjects or other efforts to minimize variability were performed.
To test the ability of respiratory navigator–gated 1H MR spectroscopy to demonstrate changes in myocardial TG content after metabolic interventions, 1H MR spectroscopy was performed in one volunteer before and after a 3-day very low calorie diet. The low-calorie diet consisted of 471 kilocalories, 50.2 g of carbohydrates, and 6.9 g of fat (0.94 g saturated fat, Modifast Intensive; Nutrition & Santé Benelux, Breda, the Netherlands) per day. The volunteer was instructed to remain fasted for 4 hours prior to both 1H MR spectroscopy examinations.
MR Technique
Cardiac MR examinations were performed at 1.5 T (Gyroscan ACS/NT15; Philips, Best, the Netherlands). A 17-cm diameter circular surface coil, with a vitamin A capsule in the center for visualization of the coil center on survey images, was positioned on the chest wall. Gradient-echo survey images were acquired to verify location of the 1H MR spectroscopy surface coil. When necessary, the coil was repositioned to place the coil center just below the mitral valve level of the heart (Fig 1). Once the coil was at the correct position, ECG-triggered MR imaging was performed to acquire multiphase gradient-echo images (repetition time msec/echo time msec, 3.5/1.75; 35–40 heart phases) in the four-chamber and short-axis views to image the interventricular septum and to determine the time point of end systole (Fig 1).
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Spectral Quantification
All 1H MR spectroscopic data were fitted in the time domain, directly on free-induction decays by using Java-based MR user-interface software (jMRUI version 2.2; A. van den Boogaart, Katholieke Universiteit Leuven, Leuven, Belgium) (14) in consensus by two authors (R.W.v.d.M. and H.J.L., with 2 and 15 years of experience in myocardial MR imaging, respectively). The Hankel-Lanczos filter (single-variable decomposition method) was used to remove residual water signal from spectra acquired with water suppression. Myocardial TG signal amplitudes were analyzed automatically by using the Advanced Magnetic Resonance, or AMARES, fitting algorithm within the jMRUI software (15). The AMARES fitting algorithm within jMRUI also provides the standard deviation of the amplitude (one time the Cramer-Rao standard deviation [CRSD]), which can be used as a measure of the accuracy of the fitted signal amplitude, reflecting the signal-to-noise ratio (16). The CRSD of the lipid signal was divided by the lipid signal amplitude, yielding a relative CRSD, which is inversely related to the signal-to-noise ratio. Resonance frequency estimates for intramyocardial lipids were described with the assumption of Gaussian line shapes at 0.9, 1.3, and 2.1 ppm. (In keeping with the approach of Torriani et al [17], we summed the amplitudes of lipid resonances at 0.9 and 1.3 ppm for TG quantification for statistical analysis). Prior knowledge was incorporated into the fitting algorithm by using previously published criteria (18–20). Fixed frequencies for TG peaks were used, and linewidths and amplitudes were unconstrained. The zero-order phase correction was estimated by using the AMARES algorithm, and the first-order phase correction was fixed to 0.13 msec. The water signal from spectra without water suppression obtained from the same voxel was used as internal reference for relative quantification of lipid resonances. The water signal peak at 4.7 ppm was quantified and the linewidth (full width at half maximum) was calculated by using a Lorentzian line shape in the AMARES algorithm. The percentage of myocardial TG content relative to water was calculated as the signal amplitude of TG divided by the signal amplitude of water, and multiplied by 100.
Statistical Analysis
To compare reproducibility of percentage of myocardial TG content with and without respiratory navigator gating and volume tracking, the intraclass correlation coefficients were calculated by using a mixed-effects analysis of variance (with patients as random factor) for both conditions separately. Furthermore, the coefficients of variance were calculated for both conditions separately. Moreover, Bland-Altman plots were constructed. Statistical significance of differences was assessed by using two-tailed paired t tests, and P < .05 was considered to indicate a significant difference. Mean values ± standard errors are given. Statistical analyses were performed by using statistical software (SPSS, version 12.01; SPSS, Chicago, Ill).
| RESULTS |
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The very low calorie diet induced an 83% increase in myocardial TG content compared with baseline percentage of TG (1.1% and 0.6% TG content, respectively) (Fig 4) in one volunteer.
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| DISCUSSION |
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Respiratory motion causes a relative displacement of the acquisition volume in relation to the position of the human heart. Thereby, respiratory motion may hamper shimming and water suppression. In our study, the full width at half maximum values of the unsuppressed water signal decreased significantly with use of the respiratory navigator compared with acquisitions without respiratory navigator gating and tracking. The observed values in our study of full width at half maximum with use of the respiratory navigator technique correspond to values reported for the tibialis anterior muscle (17) and are lower than previously published values for myocardial 1H MR spectroscopy (1). Therefore, application of respiratory navigator gating and tracking improves spectral resolution for metabolic imaging of myocardial TGs of the human heart.
The mean percentages of myocardial TG, assessed with and without respiratory motion compensation, were in accordance with previously published data from other studies (9), but with respiratory navigator, the percentage myocardial TG was lower than the acquired values in our study without use of respiratory motion compensation. The observed percentages of TG are scattered over a relatively large range for all acquisition conditions. In all acquisition conditions, the CRSDs were less than 1% of the signal amplitude, and thus spectral noise was considered to have a negligible contribution to the uncertainty of our measurements. Therefore we assume that the observed range in myocardial TG percentages reflects differences in measurement conditions (ie, presence or absence of navigator gating).
The observed higher percentage of myocardial TG without application of respiratory motion compensation is probably caused by contamination of epicardial fat. The contamination is most likely caused by the relative displacement of the acquisition volume in myocardial tissue, due to respiratory motion causing contamination from outside the selected voxel and thereby to an increase in the apparent percentage of myocardial TG.
Bland-Altman analysis showed improved agreement in myocardial TG assessment with use of respiratory navigator gating and tracking. No comparable data could be found in previous reports. In addition, with use of the respiratory navigator, reproducibility of myocardial TG assessment expressed as the intraclass correlation coefficient and the coefficient of variation improved significantly. In our study, use of respiratory navigator gating and tracking improved the intraclass correlation coefficient from 0.32 to 0.81 and decreased the coefficient of variation from 32.4% to 17.9% for assessment of myocardial TG. A coefficient of variation of 17.9% for the assessment of myocardial TG with use of respiratory motion compensation is in concordance with results of previous studies in which various other methods were used for cardiac and respiratory motion correction to increase spectroscopic quality (2,3). Szczepaniak et al (3) showed a coefficient of variation for MR spectroscopic determination of myocardial TGs of 17%, with use of a pressure belt for respiratory gating, while others reported a coefficient of variation of 13% for TG determination by using double triggering based on the ECG signal (2).
In our study, an increase in myocardial TG content was found after a short-term very low calorie diet in a healthy subject. Although this test was performed in only one volunteer, and thus is not representative of a proved finding, the result corresponds to the findings of Reingold et al (9). The clinical interpretation of the above-mentioned finding needs to be established in a larger cohort study. This clinical example suggests that metabolic imaging of myocardial TG content may be a useful new tool for monitoring effects of dietary and/or medical interventions in metabolic and cardiac disorders, such as metabolic syndrome, diabetes, and myocardial lipotoxicity. Furthermore, metabolic imaging of myocardial TG content may provide new (patho-)physiologic insights of myocardial TG handling, also in relation to global and regional cardiac function.
Our study has some limitations: First, 1H MR spectroscopy was performed in healthy volunteers only. A patient who is experiencing any sort of stress due to a medical condition is possibly less cooperative with a longer acquisition time caused by the respiratory motion compensation. We think, however, that a clinical cardiac MR imaging examination time of approximately 25 minutes to acquire a cardiac spectrum is not any different from other clinical cardiac MR imaging applications. The more reliable results of respiratory motion–compensated spectroscopy compared with non-respiratory motion–compensated spectroscopy warrants the extra time investment.
Second, the use of cardiac 1H MR spectroscopy currently has only limited clinical relevance. However, it is potentially a very useful tool in cardiac metabolic studies—for example, in the evaluation of diet and therapy effects.
Third, 1H MR spectra were obtained in the myocardial septum only. The use of 1H MR spectroscopy in other regions of the heart was not demonstrated. We think that the myocardial septum is the most favorable region for acquiring cardiac spectra in generalized disorders that affect the myocardium, such as diabetes mellitus. Motion in the myocardial septum is limited, and the myocardial septum is far from the free walls and from the pericardial fat, which could contaminate the spectra. However, more work needs to be done to develop a reliable MR spectroscopy technique for the lateral walls of the heart, which could be of interest in assessing myocardial lipid accumulation in localized disorders such as myocardial infarction.
Respiratory navigator–gated and ECG-triggered 1H MR spectroscopy of the human heart to assess myocardial TG content showed substantially better spectral resolution and reproducibility than ECG-triggered 1H-MR spectroscopy without respiratory motion correction. Therefore, we believe that respiratory motion correction is essential for reproducible metabolic imaging of myocardial TG content of the human heart.
| ADVANCES IN KNOWLEDGE |
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| IMPLICATION FOR PATIENT CARE |
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| ACKNOWLEDGMENTS |
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| FOOTNOTES |
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Abbreviations: CRSD = Cramer-Rao standard deviation ECG = electrocardiography TG = triglyceride
See Materials and Methods for pertinent disclosures.
Author contributions: Guarantors of integrity of entire study, R.W.v.d.M., H.J.L.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, R.W.v.d.M., J.D., S.K., S.H., J.W.A.S., J.A.R., M.D., L.J.R., A.d.R., H.J.L.; clinical studies, R.W.v.d.M., J.D., S.K., M.S., S.H., A.d.R., H.J.L.; statistical analysis, R.W.v.d.M., J.D., S.K., H.J.L.; and manuscript editing, all authors
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