- Top of page
- DISCUSSION AND CONCLUSIONS
- Supporting Information
The aim of this study was to implement a quantitative in vivo cardiac diffusion tensor imaging (DTI) technique that was robust, reproducible, and feasible to perform in patients with cardiovascular disease. A stimulated-echo single-shot echo-planar imaging (EPI) sequence with zonal excitation and parallel imaging was implemented, together with a novel modification of the prospective navigator (NAV) technique combined with a biofeedback mechanism. Ten volunteers were scanned on two different days, each time with both multiple breath-hold (MBH) and NAV multislice protocols. Fractional anisotropy (FA), mean diffusivity (MD), and helix angle (HA) fiber maps were created. Comparison of initial and repeat scans showed good reproducibility for both MBH and NAV techniques for FA (P > 0.22), MD (P > 0.15), and HA (P > 0.28). Comparison of MBH and NAV FA (FAMBHday1 = 0.60 ± 0.04, FANAVday1 = 0.60 ± 0.03, P = 0.57) and MD (MDMBHday1 = 0.8 ± 0.2 × 10−3 mm2/s, MDNAVday1 = 0.9 ± 0.2 × 10−3 mm2/s, P = 0.07) values showed no significant differences, while HA values (HAMBHday1Endo = 22 ± 10°, HAMBHday1Mid-Endo = 20 ± 6°, HAMBHday1Mid-Epi = −1 ± 6°, HAMBHday1Epi = −17 ± 6°, HANAVday1Endo = 7 ± 7°, HANAVday1Mid-Endo = 13 ± 8°, HANAVday1Mid-Epi = −2 ± 7°, HANAVday1Epi = −14 ± 6°) were significantly different. The scan duration was 20% longer with the NAV approach. Currently, the MBH approach is the more robust in normal volunteers. While the NAV technique still requires resolution of some bulk motion sensitivity issues, these preliminary experiments show its potential for in vivo clinical cardiac diffusion tensor imaging and for delivering high-resolution in vivo 3D DTI tractography of the heart. Magn Reson Med 70:454–465, 2013. © 2012 Wiley Periodicals, Inc.
Diffusion MRI has the unique ability to characterize the microstructure of tissues noninvasively. Histological studies have shown that the myocardium consists of an array of crossing helical fiber tracts [1, 2], which evolve smoothly from a left-handed helix in the subepicardium to a right-handed helix in the subendocardium. This structure contributes significantly to efficient ventricular function and is subject to remodeling and disarray in the presence of disease, such as myocardial infarction and cardiomyopathies. In humans, in vivo cardiac diffusion tensor imaging (DTI) has been used to noninvasively depict the fiber structure in the healthy human heart [3-10], in hypertrophic cardiomyopathy , and in patients with myocardial infarction [12, 13].
Microstructural changes in the myocardium can be quantified by measuring mean diffusivity (MD), fractional anisotropy (FA), and the orientation (helix angle, HA) of the myofibers. These indices have been widely used in ex vivo cardiac DTI studies of both healthy [14-29] and diseased myocardium [30-35]. Moreover, these indices have been used in humans in vivo to characterize the microstructural integrity of the myocardium after infarction [12, 13]. However, the reproducibility of these metrics (MD, FA, and HA) in the human heart in vivo is unknown.
Achieving reproducible DTI measurements in the heart in vivo is particularly challenging because the bulk motion of the myocardium is four orders of magnitude greater than the diffusion coefficient. The biggest challenge for in vivo cardiac DTI is motion both within and between cardiac cycles. Two sequence designs have been reported to perform DTI in vivo. A diffusion-encoded stimulated echo (STEAM) approach, which runs over two heart beats, and makes the assumption that the heart is in the same position at both diffusion-encoding times on consecutive cardiac cycles [3, 4, 6, 7], and a spin-echo single cardiac cycle approach with bipolar (velocity compensated) diffusion-encoding gradients . Several techniques have been used to compensate for respiratory motion including breath-hold , synchronized breathing , and retrospective navigators (NAV) using an intensity-based correlation postprocessing method to select the images to be used for modulus averaging . These implementations required acquisition times of 7–10 min per slice. In this form, DTI remains practical only in relatively healthy patients and well-motivated volunteers. A prospective NAV-based approach combined with biofeedback mechanisms to increase scan efficiency, however, has not previously been implemented. This may facilitate clinical implementation and enable higher resolution DTI acquisitions with more complete coverage of the heart within a clinically acceptable acquisition time.
The purpose of the current study was thus to implement and perform DTI of the human heart in vivo with both a multiple breath-hold (MBH) and a NAV-based version of a diffusion-weighted STEAM sequence. For that purpose, a novel modification of the prospective diaphragmatic NAV technique was implemented and combined with a biofeedback mechanism to increase scanning efficiency. Furthermore, we aimed to compare the accuracy of the NAV-based approach with the MBH approach, and to assess the reproducibility of the two techniques. To the best of our knowledge, this is the first study to systematically assess the reproducibility of DTI in the human heart in vivo.
DISCUSSION AND CONCLUSIONS
- Top of page
- DISCUSSION AND CONCLUSIONS
- Supporting Information
In vivo DTI of the heart is challenging to perform due to cardiac motion superimposed on intrinsically low SNR data acquisition. Although studies from several groups show that in vivo DTI in humans is possible [9-13], its accuracy and reproducibility remain unknown. We show here for the first time that DTI of the human heart in vivo can be performed with a high degree of reproducibility. Moreover, we show for the first time that cardiac DTI can be performed with a biofeedback NAV-based approach, which also provides highly reproducible measures of FA and MD. The reproducibility of the techniques described here lay the foundations for future use of cardiac DTI in the diagnosis, risk stratification, and evaluation of the treatment of human cardiac diseases.
Several techniques can theoretically enable DTI of the entire heart to be performed within a clinically feasible scan duration. In the current study, three contiguous slices at the midventricular level were acquired in 12–20 min using both the MBH and NAV approaches. All the scans were successful and reproducible. The EPI images in our study did not suffer from distortion artifacts due to the short EPI readout achieved with parallel imaging, zonal excitation, and a modest matrix size. The b value of 350 s/mm2 was selected based on previous literature suggesting that b values of 300–350 s/mm2 are sufficient to reveal the diffusion anisotropy in the myocardium [4, 9, 12, 13]. Larger b values require a longer TE and decrease SNR. Because of this low b value, the signal decrease is not apparent in the DW images. It is however sufficient to extract the diffusion information needed to calculate FA, MD, and HA, and even generate the glyphs and tractograms. However, a systematic study to define the optimum b value and number of averages needed to provide robust in vivo DTI data is required and will be the subject of future work. Comparison of the FA and MD values acquired in the initial and repeat scans showed good reproducibility for both MBH and NAV techniques. Furthermore, both MBH and NAV techniques showed no statistically significant differences in the FA and MD values. The MD maps in Figure 3 were scaled from the minimum to the maximum MD value found in the map. This scaling will make any heterogeneity appear more pronounced. The actual differences between the minimum and maximum MD values, however, show that the variation is not large. While it is possible to observe lower FA values in the left ventricle (LV)–right ventricle (RV) junction, the FA maps appear homogeneous. In this study, the transmural variations of FA and MD were not investigated. Several indices of diffusivity [trace apparent diffusion coefficient, principal eigenvalue λ1, MD] have been reported in the literature in humans in vivo, together with a range of values. In healthy volunteers, Reese et al. reported an MD = 0.9 ± 0.3 × 10−3 mm2/s , Dou et al. reported a λ1 = 0.9 × 10−3 mm2/s , Gamper et al. reported values of λ1 ranging from 1.8 × 10−3 to 2.3 × 10−3 mm2/s , Wu et al. reported trace apparent diffusion coefficient = 0.63 ± 0.02 × 10−3 mm2/s , and Rapacchi et al. reported trace apparent diffusion coefficient ∼ 7 × 10−3 mm2/s . The quantitative MD values measured in this article (MDMBHday1 = 0.8 ± 0.2 × 10−3 mm2/s, MDNAVday1 = 0.9 ± 0.2 × 10−3 mm2/s) are in good agreement with the same measures by Reese et al. . However, further studies will be needed to compare measures, standardize and optimize the method. It should be noted that the quantitative results in this study are related to acquisitions at end-systolic phase. We may therefore expect differences between in vivo and ex vivo data, between fresh and fixed ex vivo data, and data acquired at different cardiac phases.
Statistically significant differences were found between MBH and NAV techniques for HA values. Further interrogation of the data suggested that the inconsistent HA patterns were found in NAV datasets in which a small fraction (up to 10%) of the diffusion-weighted frames had signal voids in some part of the LV, as shown in Supporting Information Figure 13. This result correlates with the findings of the SNR calculation, which demonstrated that the MBH and NAV techniques produce b0 images with similar SNR, while significant differences between MBH and NAV were observed for the DW images. Inspection of the NAV plots showed that the frames with signal voids were correlated with those frames that were accepted immediately after deep inspiration (Supporting Information Fig. 13). To overcome this, it may be possible to modify the acceptance algorithm to reject a certain amount of data acquired immediately after a deep inspiration. Alternatively, as this portion always represents <10% of the data, a postprocessing step could edit out these frames before further DTI analysis. Future investigations into the effect of different breathing patterns and potential accept/reject algorithms are currently ongoing but are beyond the scope of the current paper. Nonetheless, these issues are highly addressable and our preliminary NAV results demonstrate the potential of this technique to be as robust as the MBH approach. We consider therefore that this approach establishes the right paradigm for routine clinical cardiac DTI.
Nonetheless, several limitations of the approaches used here merit discussion. First, the STEAM technique works well in healthy volunteers with stable heart rates but would perform less well in patients with highly variable heart rates. To overcome this, arrhythmia rejection algorithms will need to be implemented, which in turn will extend the acquisition time. The NAV approach is better suited to the incorporation of arrhythmia rejection algorithms because it can cope with extended acquisition times much better than the MBH technique. Alternatively, robustness against arrhythmia might be addressed using a spin-echo approach , which runs over only one HB, or the recently described PCATMIP method . Secondly, the HA range seen in our study was narrower (approximately 40° to −32°) compared with prior ex vivo and histological studies [16, 23, 24, 31, 54]. The HA range can be influenced by many factors, including in vivo versus ex vivo imaging, motion artifacts, spatial resolution and the segmentation technique used. The study by Lombaert et al. of healthy ex vivo human hearts showed ranges of HA variations from −41° ± 26° on the epicardium to +66° ± 15° on the endocardium . The histological study of postmortem human hearts by Greenbaum at al. showed a distribution of HAs from −40° on the epicardium to about +40° on the endocardium . The narrower HA range of this study may be accounted for by the modest in-plane resolution of 2.7 mm, which rendered the transmural HA values susceptible to volume averaging. The segmentation algorithm used to extract the HA values imposes further averaging, and might also account for the narrower range seen in HA. This is particularly likely in the subepicardial layer, which was thicker than the other layers after segmentation. The papillary muscles, where HA is highly variable, were included in the subendocardial layer and might account for the lower endocardial HA values. However, since the same segmentation algorithm was used in all cases, these issues do not impact the MBH versus NAV method comparison and reproducibility study. The segmentation method used was an operator independent, automated algorithm, which behaved identically in both breath-hold and NAV datasets. Although this approach was adequate for the purposes of this study, which were to test the reproducibility of breath-hold and NAV cardiac DTI techniques and also to compare them to each other, the limitations mentioned before and the fact that fast marching method algorithms have rarely been used to segment the myocardium , make it unsuitable for immediate application to clinical studies. Further work will be required to compare this segmentation method to other techniques, such as manual/expert segmentation, in terms of reproducibility and diagnostically meaningful results. Designing, implementing, optimizing, testing, and comparing segmentation algorithms to extract robust and meaningful quantitative HA data will be a very important task for the translation of cardiac DTI into clinical routine, and is the subject of ongoing research in our group. The image reconstruction interpolation step used to increase the spatial resolution of the b0 and DW images was based on zero-filling and should not influence the ROI-based quantitative analysis performed. For the quantitative analysis, the segmentation mask created to extract quantitative data from each ROI had the same resolution as the DWI images. Third, an average myocardial blood volume of 5–15% [56-59] has been reported in dogs. Therefore, blood pseudo diffusion might interfere with the quantitation of true diffusion [53, 60]. However, since intramyocardial capillaries are closely aligned with the muscle fibers, the directions derived from these data should still be valid. Moreover, these measurements were performed at end systole, when intramyocardial blood flow is significantly reduced. Finally, it should be noted that not all patients are able to adjust to a biofeedback based respiratory NAV, and some may prefer more conventional NAV techniques. These limitations, however, are all highly addressable, and the subject of ongoing work.
At present, the current MBH approach appears most suitable in normal volunteers. However, while the NAV technique still needs some bulk motion sensitivity issues to be tackled, these preliminary experiments show its feasibility and potential. The ability to perform navigator-gated DTI will be useful in normal volunteers but critical if the use of DTI is to be extended to patients with cardiovascular disease and limited breath-hold capacity. Furthermore, NAV DTI will be key not only in terms of patient comfort and compliance but also as a necessary step toward high-resolution in vivo 3D DTI tractography of the heart [10, 61, 62]. Inspection of the superquadric glyph continuum in our study (Fig. 7a,b) showed that with both the MBH and NAV acquisitions, the principal glyph orientation in all three slices remained consistent with a coherent helical myofiber pattern. These results encouraged us to pursue application of postprocessing methods to provide preliminary in vivo cardiac 3D tractograms (Fig. 7c,d). Although only three slices were acquired, Figure 7c,d demonstrate that it is possible to create 3D fiber tractography maps with data acquired using both MBH and NAV techniques, with clear depiction of the HA structure of the LV. A detailed description of these tractography algorithms and quantitative validation of the tractograms together with an analysis of the robustness/error level of the tractograms and the relationship between the number of slices and the error level will be important to determine the amount of data needed for robust in vivo cardiac tractography data, but it is beyond the scope of this paper and the subject of future work, which will also focus on extending coverage and shortening scan duration.
In conclusion, we have implemented a robust reproducible quantitative in vivo cardiac DTI technique that has the potential to enhance our understanding of in vivo structure–function relationships of the normal heart and furthermore be applied to the study of human cardiovascular disease. We show here for the first time that DTI of the human heart in vivo can be performed with a high degree of reproducibility. In addition, the feasibility of biofeedback NAV-based DTI of the heart is shown. While further optimization will be needed to improve resolution, extend coverage, and reduce scan duration, the foundation for high-resolution in vivo DTI of the human heart has been laid. This constitutes an important step forward towards the clinical use of cardiac DTI.