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Comparison of left ventricular strains and torsion derived from feature tracking and DENSE CMR

Journal of Cardiovascular Magnetic Resonance volume 20, Article number: 63 (2018) Cite this article

Abstract

Background

Cardiovascular magnetic resonance (CMR) feature tracking is increasingly used to quantify cardiac mechanics from cine CMR imaging, although validation against reference standard techniques has been limited. Furthermore, studies have suggested that commonly-derived metrics, such as peak global strain (reported in 63% of feature tracking studies), can be quantified using contours from just two frames – end-diastole (ED) and end-systole (ES) – without requiring tracking software. We hypothesized that mechanics derived from feature tracking would not agree with those derived from a reference standard (displacement-encoding with stimulated echoes (DENSE) imaging), and that peak strain from feature tracking would agree with that derived using simple processing of only ED and ES contours.

Methods

We retrospectively identified 88 participants with 186 pairs of DENSE and balanced steady state free precession (bSSFP) image slices acquired at the same locations across two institutions. Left ventricular (LV) strains, torsion, and dyssynchrony were quantified from both feature tracking (TomTec Imaging Systems, Circle Cardiovascular Imaging) and DENSE. Contour-based strains from bSSFP images were derived from ED and ES contours. Agreement was assessed with Bland-Altman analyses and coefficients of variation (CoV). All biases are reported in absolute percentage.

Results

Comparison results were similar for both vendor packages (TomTec and Circle), and thus only TomTec Imaging System data are reported in the abstract for simplicity. Compared to DENSE, mid-ventricular circumferential strain (Ecc) from feature tracking had acceptable agreement (bias: − 0.4%, p = 0.36, CoV: 11%). However, feature tracking significantly overestimated the magnitude of Ecc at the base (bias: − 4.0% absolute, p < 0.001, CoV: 18%) and apex (bias: − 2.4% absolute, p = 0.01, CoV: 15%), underestimated torsion (bias: − 1.4 deg/cm, p < 0.001, CoV: 41%), and overestimated dyssynchrony (bias: 26 ms, p < 0.001, CoV: 76%). Longitudinal strain (Ell) had borderline-acceptable agreement (bias: − 0.2%, p = 0.77, CoV: 19%). Contour-based strains had excellent agreement with feature tracking (biases: − 1.3–0.2%, CoVs: 3–7%).

Conclusion

Compared to DENSE as a reference standard, feature tracking was inaccurate for quantification of apical and basal LV circumferential strains, longitudinal strain, torsion, and dyssynchrony. Feature tracking was only accurate for quantification of mid LV circumferential strain. Moreover, feature tracking is unnecessary for quantification of whole-slice strains (e.g. base, apex), since simplified processing of only ED and ES contours yields very similar results to those derived from feature tracking. Current feature tracking technology therefore has limited utility for quantification of cardiac mechanics.

Background

Cardiac mechanics, such as strain, torsion, and dyssynchrony, are important indicators of cardiac function and independent predictors of serious outcomes, even when accounting for traditional measures such as ejection fraction [1, 2]. Several advanced cardiovascular magnetic resonance (CMR) sequences have been developed to assess cardiac mechanics including tagging [3, 4], displacement encoding with stimulated echoes (DENSE) [5,6,7], strain encoding (SENC) [8], and tissue phase mapping (TPM) [9]. While these techniques can provide reference standard measurements of myocardial motion and deformation, their use is often clinically impractical. Furthermore, because they are specialized non-clinical techniques, there are few large datasets available that could be used to guide the clinical use of these techniques. As such, there has been growing interest in the use of feature tracking software to approximate the mechanics produced by reference standard techniques [10,11,12]. While feature tracking is simple to use and requires only standard anatomical cine sequences that are widely available, it is important to assess how well measures of cardiac mechanics such as left ventricular (LV) strain, torsion, and dyssynchrony derived from feature tracking agree with those derived from reference standard techniques.

While results from feature tracking have been compared to those from tissue tagging [10, 13,14,15,16] and TPM [17], many of these studies have been limited in scope. The largest study [10], with 191 patients with Duchenne’s Muscular Dystrophy and 42 healthy controls, surveyed only mid-ventricular short-axis images, while other studies have had limited sample sizes (n = 18 [16], n = 20 [13]). Such studies have suggested that feature tracking may have poor reproducibility and poor agreement with reference standard techniques for some measures of cardiac mechanics [12, 13] due to the following potential limitations: 1) feature tracking derives displacement fields by propagating myocardial borders from frame to frame, which relies on quantification of local changes in signal intensity and therefore is likely to fail when quantifying motion parallel to or inside the myocardium where there are no features; 2) feature tracking only captures in-plane displacement, and through-plane motion of the myocardium violates the assumptions required to track pixel data.

DENSE, an advanced CMR technique, encodes a component of tissue displacement into the phase of the CMR image [5]. The reconstructed phase image measures displacements directly at the pixel-level with higher spatial resolution (2–3 mm) than myocardial tagging, which is limited by the number of tag lines that can be reliably tracked in the image. Therefore, motion within the myocardium can be accurately captured in all directions. Indeed, data from a deforming phantom demonstrated that DENSE has equal or better performance than tagged CMR, depending on the measured cardiac mechanic [18]. Several advancements in DENSE acquisition since its introduction, such as complementary spatial modulation of magnetization (CSPAMM) artifact suppression [6] and efficient spiral readouts [7], make it an ideal, highly reproducible and validated technique for reference standard measurements of myocardial motion and deformation used by numerous previous studies [18,19,20,21,22,23,24]. However, none of the feature tracking validation studies have been performed with DENSE. Indeed, a recent study [25] included data from both DENSE and feature tracking, but no direct comparisons were made.

Additionally, a literature review including 62 CMR feature tracking studies found that slice-wise strains (i.e. the average strain over an entire image, such as basal, mid-ventricular, or apical short-axis slices) are the most commonly reported measures derived from feature tracking (Table 1 and Additional file 1). In total, 39 studies (63%) reported either circumferential, longitudinal, or radial slice-wise strain, and 13 studies (21%) reported only those strains. However, slice-wise strains, which are reflective of the change in length of an entire contour between just two frames, end-diastole (ED) and end-systole (ES), should not require segmental motion tracking [26]. This suggests that the most commonly reported results from feature tracking could be easily assessed without performing tracking, by simply using the ED and ES contours which are already generated during most clinical CMR scans.

Table 1 Reported mechanics from 62 CMR feature tracking studies
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We hypothesized that LV strains, torsion, and dyssynchrony estimated from feature tracking would not agree well with those measured by DENSE as a reference standard. We also hypothesized that slice-wise strains from measuring the change in length of entire contours between the ED and ES frames (“contour-based” strains) would agree well with strains reported by feature tracking.

Methods

Study population

We reviewed our database of CMR participant datasets that were acquired from 2013 to 2016 at two institutions (University of Kentucky and the Children’s Hospital of Philadelphia) for all instances where both spiral cine DENSE and balanced steady state free precession (bSSFP) were acquired at the same slice location either in basal, mid-ventricular, or apical short-axis image planes or in the four-chamber image plane. The studies were approved by the local IRBs and all participants gave informed consent. During the review, no exclusions for diagnosis or the presence of cardiovascular risk factors were applied.

Image acquisition

All datasets from the University of Kentucky were acquired on a 3 T system (Trio, Siemens Healthineers, Erlangen, Germany) while datasets from the Children’s Hospital of Philadelphia were acquired on a 1.5 T system (Avanto, Siemens Healthineers). Spiral cine DENSE images with displacements encoded in at least the two in-plane dimensions were acquired with an established spiral sequence [7, 18, 21] using the following parameters: 6 spiral interleaves with 2 spiral interleaves acquired per temporal frame, 250 × 250 to 360 × 360 mm2 field of view, 128 × 128 image matrix, 1.95 × 1.95 to 2.81 × 2.81 mm2 pixel size, 8 mm slice thickness, 1.08 ms echo time, 15 to 17 ms repetition time, 17 to 34 ms temporal resolution. Simple or balanced encoding [19] with an encoding frequency between 0.04 and 0.10 cycles/mm [20] was used to measure in-plane displacements, while through-plane dephasing [27] and CSPAMM [6] were used for echo suppression. Cine bSSFP images were acquired at the same locations as the DENSE images using the following parameters: 1.15 × 1.15 to 1.77 × 1.77 mm2 pixel size, 7 to 10 mm slice thickness, 1.15 to 1.51 ms echo time, 2.70 to 3.43 ms repetition time, 8 to 15 k-space segments (true number of frames, 14–30 reconstructed frames), 20.2 to 49.7 ms temporal resolution.

DENSE strain analysis

Cardiac strains were derived from the DENSE images as previously described using DENSEanalysis, an open-source application [28] written in MATLAB (The Mathworks Inc., Natick, Massachusetts, USA) [22]. Examples of image analysis for DENSE as well as feature tracking are shown in Fig. 1. The post-processing steps for each cine DENSE slice included manual segmentation of the LV myocardium and semi-automated phase unwrapping to obtain the 2D displacements within each cardiac frame [22]. Following the unwrapping, spatial smoothing and temporal fitting of displacements (10th order polynomial) were performed as previously described to obtain smooth trajectories for all tissue points beginning at end-diastole and continuing through systole and into mid-diastole [22]. Circumferential (Ecc) and longitudinal (Ell) strains were calculated from short-axis and four-chamber images, respectively, using the Lagrangian Green finite strain tensor. Both circumferential and longitudinal strain were defined as negative for tissue shortening. For participants (n = 38) that had all three short-axis images (basal, mid-ventricular, and apical), cardiac torsion was calculated as the gradient of twist down the long axis of the left ventricle by finding the slope of the linear regression line between twist and longitudinal position. Twist was defined as positive for counter-clockwise rotation relative to the centroid of the LV when viewing a short-axis image from the apex towards the base. Torsion was positive when the apex was twisting more positively than the base. Dyssynchrony was quantified in these same participants (n = 38). To quantify dyssynchrony, cross-correlation delays for each segmental circumferential strain curve from the basal, mid-ventricular, and apical short-axis slices were calculated relative to a patient-specific reference curve [29]. Dyssynchrony was defined as the standard deviation of the segmental delays.

Fig. 1
figure 1

Representative images with contour overlay from feature tracking (TomTec Imaging Systems) and DENSE in mid-ventricular short-axis (top) and four-chamber (bottom) views. End-diastolic images are shown for both DENSE and feature tracking in a representative subject. In feature tracking (TomTec), only endocardial contours were used for longitudinal strain calculation. Contour-based strains were derived from the same end-diastolic/end-systolic contours exported from TomTec

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Feature tracking strain analysis

Strain and twist were derived from bSSFP imaging with Diogenes feature tracking software (2D CPA MR, version 1.1.2.36, TomTec Imaging Systems, Munich, Germany). For short-axis images, both endocardial and epicardial contours were manually drawn at ED and the software automatically propagated the contours through the remaining frames. For the four-chamber image, only an endocardial contour was drawn before propagation, since there is minimal transmural difference in longitudinal strain (Ell) between the endocardium and epicardium as compared to the typical transmural differences seen in circumferential strain (Ecc). In the case of poor tracking, ED contours were redrawn and the propagation repeated until the tracking was visually acceptable. Ecc, Ell, and twist were derived from output files generated by the software. In short-axis slices and for appropriate comparisons to DENSE, which measures strain and twist throughout the myocardial wall, the endocardial and epicardial strains and twist from feature tracking were averaged together to obtain a single transmural value. Additional file 2 contains comparisons between just the endocardial strain from feature tracking and DENSE. Torsion and dyssynchrony were computed using the same calculations as above for DENSE imaging. Studies using feature tracking have stated that strains were derived using the 1D Lagrangian calculation [14, 30, 31], and this was reaffirmed through email correspondence with the vendor.

To assess Ecc and Ell via the change in length of entire contours, the contour position data reported in the output files for only the ED and ES frames from feature tracking using the bSSFP images were used. The frame with the smallest contour circumference was defined as the ES frame. By using these contours, rather than having an observer draw them separately, any intra- and inter-observer variability was removed for the comparison between contour-based strains and feature tracking. However, there should be no fundamental difference in manually-drawn contours and the TomTec propagated contours. This enabled a pure assessment of whether regional tracking information, which would be known to the TomTec strain calculation, is different from just using the lengths of the ED and ES contours. Contour-based strains were derived from the 1D Lagrangian strain calculation.

Finally, it is important to consider the mathematics of the strain calculations if they are different between two techniques. A full derivation of the difference between strains computed from the 2D Lagrangian Green strain tensor of DENSE and the 1D Lagrangian strain from feature tracking and contours is provided in Additional file 3. The relationship can also be found throughout the literature on deformation mechanics (e.g. see chapter 3, page 119, eq. 3.24.12 [32]). From this relationship, we propose that a correction can be applied to the 1D Lagrangian strain results to allow a proper comparison with the DENSE strain results from the 2D Lagrangian Green strain tensor. Specifically, given a 1D Lagrangian strain, ε, we propose to adjust that value by adding (1/2) ε2 to account for differences in the strain calculations per the following equation:

$$ 2\mathrm{D}\ \mathrm{Lagrangian}\ \mathrm{Green}\ \mathrm{Strain}=\upvarepsilon +\left(\frac{1}{2}\right){\left(\upvarepsilon \right)}^2. $$

In order to validate our findings in feature tracking, we also analyzed all data using a separate commercial feature tracking software (cvi42, Circle Cardiovascular Imaging Inc., Calgary, Alberta, Canada). Detailed description of data analyses and results from this comparison are shown in Additional file 4. Since the results were not substantially different between the two vendor platforms, the term “feature tracking” refers to TomTec only in the primary results below for simplification.

Statistics

Agreement of strains and torsion between feature tracking and DENSE was assessed with Bland-Altman analyses and coefficients of variation (CoV). Based on similar analyses in previous studies [24, 33], CoVs less than 20% were interpreted as acceptable. Paired t-tests were utilized to determine whether biases were statistically significant from zero at a significance level of 0.05. Comparisons between feature tracking and DENSE were made both before and after adjusting the feature tracking results to account for the differences in strain calculations. Bland-Altman analyses and CoVs were also used to compare adjusted feature tracking strains to adjusted contour-based strains. Continuous data are presented as mean ± standard deviation.

Results

Study population

From the review of our database, 89 unique participants were identified that had spiral cine DENSE and bSSFP imaging at the same image locations. Of those, 1 participant had poor DENSE image quality due to aberrant prospective electrocardiogram (ECG) triggering and was therefore omitted from analyses. From these 88 participants, we obtained 186 independent image pairs, regionally distributed as follows: 39 basal short-axis, 69 mid-ventricular short-axis, 38 apical short-axis, and 40 four-chamber images. For torsion and dyssynchrony, 38 participants had all 3 of the necessary short-axis images (i.e. all participants that had an apical short-axis image also had the other short-axis images). Characteristics of the participants for each image location are reported in Table 2. Compared to other regions, there was a preponderance of healthy individuals in the four-chamber images due to only acquiring short-axis images in many patient studies.

Table 2 Participant characteristics
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Comparison between feature tracking and DENSE

When using just the endocardial strain from feature tracking, Ecc was significantly overestimated compared to DENSE, which measured strain throughout the myocardial wall (Additional file 2). The remainder of the Ecc, torsion, and dyssynchrony results are based on the average of the endocardial and epicardial values from feature tracking in order to better approximate the DENSE results as described in the Methods.

Before adjusting for differences in the strain calculations, Ecc was significantly overestimated by feature tracking compared to DENSE by between 2.3 and 6.0% (absolute, Table 3). Similarly, feature tracking tended to over-estimate Ell by 1.4%, although the result was not statistically significant (p = 0.08).

Table 3 Summary of strains and torsion from feature tracking and DENSE
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After adjusting the feature tracking results to account for differences in the strain calculations, feature tracking strains all decreased in magnitude – closer to corresponding DENSE values – such that the mid-ventricular Ecc were no longer different (−17.5 vs −17.2%, p = 0.36). However, basal and apical Ecc remained significantly overestimated by feature tracking even after adjustment (by 4.1 and 2.5% absolute, respectively [p < 0.001 for both]). A physiologic gradient of increasing Ecc magnitude from the base to the mid-ventricle to the apex was observed in the DENSE results. This gradient was not present in the feature tracking results before or after adjustment. On Bland-Altman analyses, the 95% limits of agreement and CoVs were lower after the feature tracking results were adjusted (Table 4, Fig. 2). Ecc at the mid-ventricular level had the best agreement between adjusted feature tracking and DENSE (95% limits: ±6.3%, CoV: 10.9%). All other strains demonstrated CoVs above 20% before applying the adjustment. Those same CoVs dropped below 20% after the adjustment. The CoV for Ell was 19.3%.

Table 4 Bland-Altman analyses and coefficients of variation comparing Feature Tracking to the reference (DENSE)
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Fig. 2
figure 2

Bland-Altman analyses for circumferential and longitudinal strains between feature tracking and DENSE. Analyses were performed both before (left column) and after (right column) adjusting the feature tracking results to account for differences in the strain calculation. All differences were calculated by subtracting the DENSE strain from the feature tracking strain. All biases and 95% limits of agreement improved after adjusting the feature tracking strains. The red shaded region highlights the bias. The best agreement was observed in mid-ventricular circumferential strain. CoV, coefficient of variation

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Torsion was significantly underestimated by feature tracking compared to DENSE (2.1 vs 3.5 deg/cm, p < 0.001). Dyssynchrony was significantly overestimated by feature tracking (42 vs 16 ms, p < 0.001). Both torsion and dyssynchrony had poor agreement with DENSE as demonstrated by wide 95% limits and large CoVs (Fig. 3).

Fig. 3
figure 3

Bland-Altman analyses for torsion and dyssynchrony between feature tracking and DENSE. All differences were calculated by subtracting the DENSE measurement from the feature tracking measurement. The red shaded region highlights the bias. Poor agreement between feature tracking and DENSE was observed for both measures as demonstrated by large biases, 95% limits, and CoVs. CoV, coefficient of variation

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Comparison between feature tracking and contour-based strain

Excellent agreement was observed between all Ecc and Ell from feature tracking and contour-based strains (Table 5, Fig. 4) with CoVs between 3.2 and 7.0%. Bland-Altman 95% limits (between ±2.2 and ± 3.8%) were substantially lower than those observed during the comparisons between feature tracking and DENSE.

Table 5 Bland-Altman analyses and coefficients of variation for feature tracking compared to contour-based strains
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Fig. 4
figure 4

Bland-Altman analyses for circumferential and longitudinal strains between feature tracking and contour-based strains. All differences were calculated by subtracting the feature tracking strain from the contour-based strain. The red shaded region highlights the bias. Excellent agreement (small biases and tight 95% limits) was observed for all circumferential and longitudinal strains. CoV, coefficient of variation

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While the agreement between feature tracking and contour-based strain was excellent, we investigated why it was not perfect. Specifically, we found discrepancies between the appearance of the propagated contours and the strains that feature tracking reported for them. For example, Fig. 5 shows propagated endocardial contours for frame 1 and frame 30 (the last frame) for a short-axis image from a representative subject. There are noticeable differences in the contour lengths between those two frames, and the contour-based strain calculation would quantify a small strain for frame 30 relative to frame 1. However, the feature tracking software reported exactly zero strain for all segments in both frame 1 and frame 30, which is inconsistent with the noticeable differences between the contours.

Fig. 5
figure 5

Propagated contours and reported feature tracking strains are inconsistent (representative subject). The propagated endocardial contours for frame 1 and frame 30 (the last frame) of a representative subject are shown along with the strains reported by feature tracking. Despite the differences in contour length, which would be measured as strain by the contour-based calculation, the feature tracking software reported zero strain in all segments and, thus, zero slice-wise strain. When deriving strains, the feature tracking software may employ curve-fitting after propagating the contours, which would lead to differences between the reported feature tracking strains and the contour-based strains

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Validation of feature tracking using circle

In summary, the results from Circle are similar to the findings from TomTec. Consistent with TomTec, Circle significantly overestimated basal and apical Ecc, Ell, and dyssynchrony compared to DENSE (all p < 0.001,Additional file 4 Table S3), and the amount of overestimation was in general larger than that of TomTec (Additional file 4 Table S4, Figure S4). Only mid-ventricular Ecc from Circle showed good agreement with DENSE (bias = 0.7%, CoV = 11%). Opposite to TomTec, torsion was overestimated in Circle compared to DENSE (bias = 1.5 deg/cm, CoV = 36%). The agreements of torsion and dyssynchrony between Circle and TomTec, as well as between Circle and DENSE, were very poor with unacceptable limits and CoVs (Fig. S5). Detailed results are included in Additional file 4.

Discussion

This study evaluated the utility of commercially available feature tracking software for quantifying measures of cardiac mechanics, including LV strains, torsion and dyssynchrony. Our primary findings included: 1) the only truly acceptable agreement between feature tracking and the reference standard (DENSE) was observed for mid-ventricular Ecc, 2) feature tracking significantly overestimated the magnitude of Ecc in basal and apical images, 3) feature tracking overestimated Ell in four-chamber images, 4) feature tracking significantly overestimated dyssynchrony and under- or over- estimated torsion depending on the vendor with unacceptable CoVs, and 5) slice-wise strains from the change in length of entire contours (contour-based strains) had excellent agreement with slice-wise strains reported by feature tracking.

Slice-wise strains from feature tracking and contour-based strains

Feature tracking has emerged as a simple and convenient tool for estimating cardiac mechanics from standard CMR imaging. However, we found that the most commonly-reported mechanics from feature tracking (slice-wise Ecc and Ell) can be reproduced by contour-based strains. Such agreement between feature tracking and contour-based strains has been previously reported along with the suggestion that manual border delineation could be a low-cost alternative to purchasing feature tracking software [26]. Because of the excellent agreement between feature tracking and contour-based strain, regional tracking capabilities and the cost of the feature tracking software are not required to assess these metrics. Many of the insights from previous feature tracking studies could have been easily produced without the software by the manual delineation of borders at two time points, ED and ES, which is already routine for most clinical examinations in which LV volumes are reported.

However, the use of feature tracking or manual delineation to assess slice-wise strains is not beyond reproach. Only mid-ventricular Ecc had good agreement between feature tracking and DENSE with 95% limits of agreement of ±6.3% and a CoV of 10.9%. Previous studies that assessed the agreement between feature tracking and myocardial tagging have shown 95% limits ranging from ±3.3% [10] to ±9.1% [16], with several other studies in between [13, 14, 31]. However, for Ecc in basal and apical images, we found significant biases and larger CoVs, which indicates that feature tracking and DENSE do not agree as well in those regions. In particular, apical Ecc had the largest 95% limits of agreement (±10.8%), which is consistent with a previous study that also observed that the apical region had the largest 95% limits (±12.8% at 1.5 T and ±9.2% at 3 T) [16]. The largest bias (−4.0%) was observed in basal Ecc. This bias was large enough to disrupt the physiologic gradient in Ecc from base to apex that was observed in the DENSE results and has been documented extensively [21, 34,35,36]. These inconsistencies between feature tracking and DENSE at the basal and apical levels are likely due to both through-plane motion, which is most prominent at the base and invalidates the fundamental assumption that a segment of tissue can be observed and tracked through the entire cardiac cycle in a single 2D image plane, and the difficulty in tracking the true endocardial contour, which may be more prominent at the apex due to papillary musculature and trabeculations. Inter-test variability in both techniques, while larger in feature tracking [12, 20], also contributes to imperfect agreement between them. Among the slice-wise strains quantified in this study, Ell had the highest CoV (19.3%) along with high 95% limits of agreement compared to DENSE (±9.3%). This is consistent with a previous comparison between feature tracking and myocardial tagging which found 95% limits of agreement to be ±9.5% [13].

To reiterate, for slice-wise strains in general, manual contour delineation at just ED and ES can replace results from feature tracking. However, neither agree strongly with a reference standard technique like DENSE with the exception of mid-ventricular Ecc.

Torsion, Dyssynchrony, and other mechanics

Less commonly-reported measures of mechanics from feature tracking include segmental strains, strain rates, torsion, and dyssynchrony. However, these are precisely the measurements for which accurate feature tracking would be most useful since none of these can be quantified by the manual delineation of contours at only ED and ES. Unfortunately, feature tracking has limited success in accurately and reproducibly quantifying these mechanics.

Previous studies of segmental strains and strain rates from feature tracking have demonstrated poor reproducibility and poor agreement with reference standards [12, 16]. Similar results were observed for torsion in the present study where torsion from feature tracking (TomTec) significantly underestimated DENSE by 1.4 deg/cm on average, while torsion from Circle overestimated DENSE by 1.5 deg./cm. This large bias is consistent with the literature as the torsion found by DENSE (3.5 ± 0.9 deg/cm) is similar to previous results from DENSE (3.1 to 3.9 deg./cm) [21] and myocardial tagging (3.4 to 3.7 deg/cm) [37] while the torsion result from TomTec (2.1 ± 1.2 deg/cm) is similar to previous feature tracking studies (2.3 ± 0.8 deg./cm) [33]. Furthermore, the CoV and 95% limits of agreement for comparing DENSE and feature tracking were high (41.1% and ± 2.4 deg/cm, respectively). Another previous study also found poor agreement and correlation between torsion derived from feature tracking and myocardial tagging as well as poor reproducibility from feature tracking [15]. The poor results from feature tracking using bSSFP images are likely due to the difficulty in tracking myocardial motion in the circumferential direction. While a strong gradient between the blood pool and the myocardium exists for accurately tracking the location of the endocardial contour in bSSFP imaging, the gradients in the orthogonal direction, which are necessary for tracking twist along that contour, are much weaker. Therefore, it is nearly impossible to track motion parallel to the myocardial wall unless additional features (such as papillary muscles) are present for tracking.

In the present study, dyssynchrony was significantly overestimated compared to DENSE (42 ± 22 vs 16 ± 20 ms) while demonstrating large 95% limits of agreement (±56 ms) and CoV (76.3%). A previous study has also demonstrated poor reproducibility for the quantification of dyssynchrony from segmental Ecc (CoV: 37.5%) [38]. The poor agreement between feature tracking and DENSE is largely due to the need for accurate segmental strains within the dyssynchrony calculation. Poor reproducibility of segmental strains, which has been demonstrated for feature tracking [12], likely resulted in both erroneously high measured dyssynchrony within volunteers (i.e. a bias) and greater variability in general.

Limitations

While this study evaluated the agreement between measurements derived from feature tracking and those same measures derived from a reference standard DENSE sequence, we could not evaluate the prognostic utility of the measures. While we observed imperfect agreement between the techniques, it is still possible that feature tracking (or manual contour delineation) produces useful results. However, careful consideration is required before generalizing results from reference standard techniques to feature tracking. There may be cases where only a reference standard technique is sufficient (e.g., identifying a gradient in Ecc from base to apex). Measures of radial strain were not included in this study due to well-known poor reproducibility [12, 13]. In addition, the current study did not evaluate all patient populations. Different populations will likely show different levels of agreement. In particular, populations with poor function and reduced through-plane motion would be expected to have better agreement between feature tracking and reference standard techniques. However, since changes in strains may precede changes in other functional measures, quantification of cardiac strains is likely most important in populations with healthy or nearly healthy function (e.g. pediatric obesity [23]). While it is common to assess the agreement between feature tracking and reference standard techniques with only healthy participants [13], we note that there was a preponderance of healthy subjects in the assessment of Ell compared to other strains in this study.

Conclusion

Compared to DENSE as a reference standard, feature tracking was inaccurate for quantification of apical and basal LV Ecc, Ell, torsion, and dyssynchrony. Feature tracking was only accurate for quantification of mid LV Ecc. Moreover, feature tracking is unnecessary for quantification of global peak strains, since simplified processing of only ED and ES contours yields very similar results to those derived from feature tracking. Current commercial feature tracking technology therefore has limited utility for quantification of cardiac mechanics.

Abbreviations

bSSFP:

Balanced steady state free precession

CMR:

Cardiovascular magnetic resonance

CoV:

Coefficient of variation

CSPAMM:

Complementary spatial modulation of magnetization

DENSE:

Displacement encoding with stimulated echoes

Ecc:

Circumferential strain

ED:

End-diastole

Ell:

Longitudinal strain

ES:

End-systole

LV:

Left ventricle/left ventricular

SENC:

Strain encoding

TPM:

Tissue phase mapping

References

  1. Stanton T, Leano R, Marwick TH. Prediction of all-cause mortality from global longitudinal speckle strain: comparison with ejection fraction and wall motion scoring. Circ Cardiovasc Imaging. 2009;2:356–64.

    Article  PubMed  Google Scholar 

  2. Choi E-Y, Rosen BD, Fernandes VRS, Yan RT, Yoneyama K, Donekal S, Opdahl A, Almeida ALC, Wu CO, Gomes AS, D a B, Lima J a C. Prognostic value of myocardial circumferential strain for incident heart failure and cardiovascular events in asymptomatic individuals: the multi-ethnic study of atherosclerosis. Eur Heart J. 2013;34:2354–61.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Axel L, Dougherty L. MR imaging of motion with spatial modulation of magnetization. Radiology. 1989;171:841–5.

    Article  PubMed  CAS  Google Scholar 

  4. Ibrahim el SH. Myocardial tagging by cardiovascular magnetic resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications. J Cardiovasc Magn Reson. 2011;13:36.

    Article  Google Scholar 

  5. Aletras AH, Ding S, Balaban RS, Wen H. DENSE: displacement encoding with stimulated echoes in cardiac functional MRI. J Magn Reson. 1999;137:247–52.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Kim D, Gilson WD, Kramer CM, Epstein FH. Myocardial tissue tracking with two-dimensional cine displacement-encoded MR imaging: development and initial evaluation. Radiology. 2004;230:862–71.

    Article  PubMed  Google Scholar 

  7. Zhong X, Spottiswoode BS, Meyer CH, Kramer CM, Epstein FH. Imaging three-dimensional myocardial mechanics using navigator-gated volumetric spiral cine DENSE MRI. Magn Reson Med. 2010;64:1089–97.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Osman NF, Sampath S, Atalar E, Prince JL. Imaging longitudinal cardiac strain on short-axis images using strain-encoded MRI. Magn Reson Med. 2001;46:324–34.

    Article  PubMed  CAS  Google Scholar 

  9. Simpson R, Keegan J, Firmin D. Efficient and reproducible high resolution spiral myocardial phase velocity mapping of the entire cardiac cycle. J Cardiovasc Magn Reson. 2013;15:34.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hor KN, Gottliebson WM, Carson C, Wash E, Cnota J, Fleck R, Wansapura J, Klimeczek P, Al-Khalidi HR, Chung ES, Benson DW, Mazur W. Comparison of magnetic resonance feature tracking for strain calculation with harmonic phase imaging analysis. JACC Cardiovasc Imaging. 2010;3:144–51.

    Article  PubMed  Google Scholar 

  11. Claus P, Omar AMS, Pedrizzetti G, Sengupta PP, Nagel E. Tissue tracking Technology for Assessing Cardiac MechanicsPrinciples, normal values, and Clinical Applications. JACC Cardiovasc Imaging. 2015;8:1444–60.

    Article  PubMed  Google Scholar 

  12. Morton G, Schuster A, Jogiya R, Kutty S, Beerbaum P, Nagel E. Inter-study reproducibility of cardiovascular magnetic resonance myocardial feature tracking. J Cardiovasc Magn Reson. 2012;14:43.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Augustine D, Lewandowski AJ, Lazdam M, Rai A, Francis J, Myerson S, Noble A, Becher H, Neubauer S, Petersen SE, Leeson P. Global and regional left ventricular myocardial deformation measures by magnetic resonance feature tracking in healthy volunteers: comparison with tagging and relevance of gender. J Cardiovasc Magn Reson. 2013;15:8.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Harrild DM, Han Y, Geva T, Zhou J, Marcus E, Powell AJ. Comparison of cardiac MRI tissue tracking and myocardial tagging for assessment of regional ventricular strain. Int J Cardiovasc Imaging. 2012;28:2009–18.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kuetting D, Sprinkart AM, Doerner J, Schild H, Thomas D. Comparison of magnetic resonance feature tracking with harmonic phase imaging analysis (CSPAMM) for assessment of global and regional diastolic function. Eur J Radiol. 2015;84:100–7.

    Article  PubMed  CAS  Google Scholar 

  16. Singh A, Steadman CD, Khan JN, Horsfield MA, Bekele S, Nazir SA, Kanagala P, Masca NGD, Clarysse P, McCann GP. Intertechnique agreement and interstudy reproducibility of strain and diastolic strain rate at 1.5 and 3 tesla: a comparison of feature-tracking and tagging in patients with aortic stenosis. J Magn Reson Imaging. 2015;41:1129–37.

    Article  PubMed  Google Scholar 

  17. Kuetting DLR, Sprinkart AM, Dabir D, Schild HH, Thomas DK. Assessment of cardiac dyssynchrony by cardiac MR: a comparison of velocity encoding and feature tracking analysis. J Magn Reson Imaging. 2015;43:940–46.

    Article  PubMed  Google Scholar 

  18. Young AA, Li B, Kirton RS, Cowan BR. Generalized spatiotemporal myocardial strain analysis for DENSE and SPAMM imaging. Magn Reson Med. 2012;67:1590–9.

    Article  PubMed  Google Scholar 

  19. Zhong X, Helm PA, Epstein FH. Balanced multipoint displacement encoding for DENSE MRI. Magn Reson Med. 2009;61:981–8.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wehner GJ, Grabau JD, Suever JD, Haggerty CM, Jing L, Powell DK, Hamlet SM, Vandsburger MH, Zhong X, Fornwalt BK. 2D cine DENSE with low encoding frequencies accurately quantifies cardiac mechanics with improved image characteristics. J Cardiovasc Magn Reson. 2015;17:93.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wehner GJ, Suever JD, Haggerty CM, Jing L, Powell DK, Hamlet SM, Grabau JD, Mojsejenko WD, Zhong X, Epstein FH, Fornwalt BK. Validation of in vivo 2D displacements from spiral cine DENSE at 3T. J Cardiovasc Magn Reson. 2015;17:5.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Spottiswoode BS, Zhong X, Hess a T, Kramer CM, Meintjes EM, Mayosi BM, Epstein FH. Tracking myocardial motion from cine DENSE images using spatiotemporal phase unwrapping and temporal fitting. IEEE Trans Med Imaging. 2007;26:15–30.

    Article  PubMed  CAS  Google Scholar 

  23. Jing L, Binkley CM, Suever JD, Umasankar N, Haggerty CM, Rich J, Nevius CD, Wehner GJ, Hamlet SM, Powell DK, Radulescu A, Kirchner HL, Epstein FH, Fornwalt BK. Cardiac remodeling and dysfunction in childhood obesity: a cardiovascular magnetic resonance study. J Cardiovasc Magn Reson. 2016;18:28.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Haggerty CM, Kramer SP, Binkley CM, Powell DK, Mattingly AC, Charnigo R, Epstein FH, Fornwalt BK. Reproducibility of cine displacement encoding with stimulated echoes (DENSE) cardiovascular magnetic resonance for measuring left ventricular strains, torsion, and synchrony in mice. J Cardiovasc Magn Reson. 2013;15:71.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cao JJ, Ngai N, Duncanson L, Cheng J, Gliganic K, Chen Q. A comparison of both DENSE and feature tracking techniques with tagging for the cardiovascular magnetic resonance assessment of myocardial strain. J Cardiovasc Magn Reson. 2018;20:26.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kempny A, Fernández-Jiménez R, Orwat S, Schuler P, Bunck AC, Maintz D, Baumgartner H, Diller G-P. Quantification of biventricular myocardial function using cardiac magnetic resonance feature tracking, endocardial border delineation and echocardiographic speckle tracking in patients with repaired tetralogy of Fallot and healthy controls. J Cardiovasc Magn Reson. 2012;14:32.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhong X, Spottiswoode BS, E a C, Gilson WD, Epstein FH. Selective suppression of artifact-generating echoes in cine DENSE using through-plane dephasing. Magn Reson Med. 2006;56:1126–31.

    Article  PubMed  Google Scholar 

  28. Gilliam AD, Suever JD: DENSEanalysis. 2016.

    Google Scholar 

  29. Jing L, Haggerty CM, Suever JD, Alhadad S, Prakash A, Cecchin F, Skrinjar O, Geva T, Powell AJ, Fornwalt BK. Patients with repaired tetralogy of Fallot suffer from intra- and inter-ventricular cardiac dyssynchrony: a cardiac magnetic resonance study. Eur Heart J Cardiovasc Imaging. 2014;15:1333–43.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wu L, Germans T, Güçlü A, Heymans MW, Allaart CP, van Rossum AC. Feature tracking compared with tissue tagging measurements of segmental strain by cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2014;16:10.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Moody WE, Taylor RJ, Edwards NC, Chue CD, Umar F, Taylor TJ, Ferro CJ, Young AA, Townend JN, Leyva F, Steeds RP. Comparison of magnetic resonance feature tracking for systolic and diastolic strain and strain rate calculation with spatial modulation of magnetization imaging analysis. J Magn Reson Imaging. 2015;41:1000–12.

    Article  PubMed  Google Scholar 

  32. Lai WM, Rubin D, Krempl E. Chapter 3 – kinematics of a continuum. In: Introduction to continuum mechanics. Volume 3; 2010. p. 69–153.

    Chapter  Google Scholar 

  33. Kowallick JT, Morton G, Lamata P, Jogiya R, Kutty S, Lotz J, Hasenfuß G, Nagel E, Chiribiri A, Schuster A. Inter-study reproducibility of left ventricular torsion and torsion rate quantification using MR myocardial feature tracking. J Magn Reson Imaging. 2015;43:128–37.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Moore CC, Lugo-Olivieri CH, McVeigh ER, E a Z. Three-dimensional systolic strain patterns in the normal human left ventricle: characterization with tagged MR imaging. Radiology. 2000;214:453–66.

    Article  PubMed  CAS  Google Scholar 

  35. Feng L, Donnino R, Babb J, Axel L, Kim D. Numerical and in vivo validation of fast cine displacement-encoded with stimulated echoes (DENSE) MRI for quantification of regional cardiac function. Magn Reson Med. 2009;62:682–90.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Leitman M, Lysiansky M, Lysyansky P, Friedman Z, Tyomkin V, Fuchs T, Adam D, Krakover R, Vered Z. Circumferential and longitudinal strain in 3 myocardial layers in normal subjects and in patients with regional left ventricular dysfunction. J Am Soc Echocardiogr. 2010;23:64–70.

    Article  PubMed  Google Scholar 

  37. Donekal S, Ambale-Venkatesh B, Berkowitz S, Wu CO, Choi EY, Fernandes V, Yan R, A a H, D a B, J a L. Inter-study reproducibility of cardiovascular magnetic resonance tagging. J Cardiovasc Magn Reson. 2013;15:37.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kowallick JT, Morton G, Lamata P, Jogiya R, Kutty S, Hasenfuss G, Lotz J, Chiribiri A, Nagel E, Schuster A. Quantitative assessment of left ventricular mechanical dyssynchrony using cine cardiovascular magnetic resonance imaging: inter-study reproducibility. J Cardiovasc Magn Reson. 2016;18(Suppl 1):P40.

    Article  PubMed Central  Google Scholar 

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Funding

This work was supported by a National Institutes of Health (NIH) Director’s Early Independence Award (DP5 OD-012132), NIH T32 HL-072743, NIH P20 GM103527, and NIH grant number UL1TR000117 from the National Center for Research Resources and the National Center for Advancing Translational Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available on reasonable request with approval of the corresponding author.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Kentucky, Lexington, KY, USA

    Gregory J. Wehner & Brandon K. Fornwalt

  2. Department of Imaging Science and Innovation, Geisinger, 100 North Academy Avenue, Danville, PA, 17822-4400, USA

    Linyuan Jing, Christopher M. Haggerty, Jonathan D. Suever, Jing Chen, Jared A. Feindt & Brandon K. Fornwalt

  3. Department of Pediatrics, University of Kentucky, Lexington, KY, USA

    Linyuan Jing, Christopher M. Haggerty, Jonathan D. Suever, W. Dimitri Mojsejenko & Brandon K. Fornwalt

  4. Department of Electrical Engineering, University of Kentucky, Lexington, KY, USA

    Sean M. Hamlet & Brandon K. Fornwalt

  5. Division of Cardiology, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Mark A. Fogel

  6. Department of Radiology, Geisinger, Danville, PA, USA

    Brandon K. Fornwalt

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  1. Gregory J. Wehner
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Contributions

GJW analyzed and collected data, assisted with study design and implementation, and drafted the manuscript. LJ and CMH analyzed and collected data, assisted with study design and implementation, and critical revision of the manuscript. JDS assisted with study design and implementation, and critical revision of the manuscript. JC, SMH, JAF and WDM analyzed and collected data, and assisted with critical revision of the manuscript. MAF collected data, assisted with study design and implementation, and critical revision of the manuscript. BKF conceived the study, assisted in study design and coordination, and assisted with drafting and critical revision of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Brandon K. Fornwalt.

Ethics declarations

Ethics approval and consent to participate

The study was approved by Institutional Review Boards at both the University of Kentucky and Children’s Hospital of Philadelphia. All participants provided assent and parents/legal guardians provided written and informed consent for minors.

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Not applicable.

Competing interests

The authors declare that they have no competing interests

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Additional files

Additional file 1:

Literature Review. (DOCX 22 kb)

Additional file 2:

Comparison between Endocardial Strain from Feature Tracking and DENSE. (DOCX 143 kb)

Additional file 3:

Comparison of Strain Calculations. (DOCX 190 kb)

Additional file 4:

Validation using a Second Feature Tracking Software. (DOCX 302 kb)

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Wehner, G.J., Jing, L., Haggerty, C.M. et al. Comparison of left ventricular strains and torsion derived from feature tracking and DENSE CMR. J Cardiovasc Magn Reson 20, 63 (2018). https://doi.org/10.1186/s12968-018-0485-4

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  • DOI: https://doi.org/10.1186/s12968-018-0485-4

Keywords

Journal of Cardiovascular Magnetic Resonance

ISSN: 1532-429X

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