Localized strain characterization of cardiomyopathy in Duchenne muscular dystrophy using novel 4D kinematic analysis of cine cardiovascular magnetic resonance

Duchenne muscular dystrophy (DMD) is an X-linked recessive progressive neuromuscular disease with an incidence of approximately 1 in 5000 live male births [1‐3]. DMD is caused by a mutation in the DMD gene resulting in severely reduced or absent functional dystrophin [4, 5]. A lack of dystrophin leads to a loss of sarcolemma integrity, triggering muscle degradation followed by necrosis, fibrosis, and fibro-fatty replacement of normal cardiac muscle tissue [6‐9]. Dystrophin deficiency in the heart leads to myocardial necrosis and fibro-fatty replacement commonly resulting in a lethal cardiomyopathy (CMP), though the onset and progression of this phenotype vary [10‐13]. In the current era, CMP is the most common cause of mortality in DMD, but imaging biomarkers are limited in their ability to predict the early onset or rate of CMP progression [14, 15]. Clinically important CMP typically becomes apparent in the middle of the second decade, affecting one-third of patients by age 14 and nearly all patients over 18 years of age [16, 17]. However, pre-clinical cardiac involvement is thought to be present in up to one-fourth of DMD patients under 6 years old [17].

Early identification of fibro-fatty replacement and myocardial damage allows for prophylactic treatment with cardioprotective medications for DMD CMP [1]. While several non-randomized studies have shown that glucocorticoids, angiotensin-converting enzyme inhibitors, and aldosterone inhibitors may delay CMP progression, [18‐21] more recent trials suggest that these standard heart failure medications may not be as effective in DMD as in other forms of CMP [22]. Novel therapies are needed but cannot be developed without a better understanding of CMP progression and the identification of novel CMP biomarkers.

Over the past few years, we have developed methods for high-sensitivity spatiotemporal mapping of 4D (3D + time) gated cardiac echocardiographic data [23‐26]. These advances have allowed us to identify subtle imaging biomarkers in a variety of cardiac disease animal models of myocardial infarction [27, 28], aortic aneurysm [29, 30], and atherosclerosis [31]. Applying these techniques to 3D + time cardiovascular magnetic resonance (CMR) images offers a promising method for biomechanical characterization of pathologic changes in DMD patients. The objective of this manuscript was to adapt our novel method of spatiotemporal mapping of 4D kinematic data to CMR cine images and to apply this new method to patients with DMD CMP. We hypothesized that this novel method of strain analysis would show significant differences between DMD CMP and healthy controls and that regional strain and strain rate would allow for stronger differentiation compared to global values alone.

This study describes the first use of a novel method for 3D cine CMR feature-tracking (FT) strain analysis using sequentially stacked 2-dimensional cine CMR images. This study also demonstrates the utility of this method in patients with DMD CMP. We showed that localized strain metrics derived from this 4D image analysis are both sensitive and specific for characterizing DMD CMP disease severity. To our knowledge, this is the first report that localized surface area strain and strain rate metrics derived from 3D cine CMR were used to characterize CMP in a DMD cohort. We observed that basal E_a, E_cc, and E_rr peak strain and strain rate were the most sensitive and specific metrics for differentiating DMD CMP from healthy control subjects. In general, we found that for every strain metric assessed, slice-level localized strain rate was better able to differentiate between DMD and control subjects compared to corresponding peak strain alone. Basal E_a systolic strain rate had the best differentiation with an AUC of 0.96. Basal E_rr, basal E_rr systolic strain rate and basal E_cc systolic strain rate magnitude values were significantly decreased in mild cardiomyopathy (LGE-, LVEF > 55%) compared to a healthy control group. Localized E_a and E_cc peak strain and strain rate metrics also had the strongest correlation with LVEF, T1, and ECV values while E_rr and E_ll were less strongly correlated with LVEF, T1, and ECV. These data suggest that localized and kinematic analysis of 3D cine CMR images in DMD patients may provide a more robust analysis for assessing CMP than global or peak strain values alone.

Patients with DMD universally develop cardiomyopathy [45]. However, the age of onset, the time course, and the severity of CMP are highly variable necessitating more refined measures for assessing CMP severity [14, 15]. Although LGE is present as early as 7 years of age it is only apparent when a significant amount of damage has occurred. Traditional functional assessment by LVEF is limited as it is only abnormal in later stages of disease when the process may no longer be reversible. As such, developing early markers of disease can shift the treatment paradigm from rescue to prevention. These early abnormalities provide novel biomarkers and surrogate outcome measures of disease progression.

In this study, we observed the most significant differences, largest AUC values, and strongest correlation to LVEF in basal circumferential and basal E_a when comparing DMD patients to healthy controls. The results demonstrate the value of E_cc as an imaging biomarker in DMD and are consistent with other studies [46, 47]. In a recent study, Siddiqui et al. also showed 3D CMR-derived E_cc was better able to predict the onset of DMD CMP than conventional 2D CMR-derived strain values [48]. Similar to our technique, Siddiqui et al. accomplished 3D FT in DMD cardiomyopathy using 3D interpolation of the endocardial and epicardial boundaries from 2D slices. This 3D FT strain technique has been shown to have superior reproducibility compared to 2D FT in CMR and has been well described by Liu et al. [49].

To our knowledge, no previous studies have explored the role of strain rate derived from 3D cine CMR in a population of patients with DMD-associated CMP. Strain rate has been a predominantly echocardiographic-based measure likely due to its superior temporal resolution (< 5 ms) compared to CMR (~ 20–50 ms) [50]. Additionally, strain rate measurements that use tagging may be less reproducible due to tag fading [51]. In the CMR FT technique we present here, we do not have issues with tag fading which provides an advantage for strain rate estimation. In addition, our method of interpolation between frames allows us to estimate strain rate despite a relatively lower temporal resolution. However, as with all strain and strain rate estimation methods, it should be noted that variability in heartrate will impact the temporal resolution differently for each cardiac cycle analyzed. This variability may affect strain rate estimations. Our strain rate calculation method, like others used for CMR [52] is based on calculating the slope of the strain curve and as such values may be under- or overestimated depending on heartrate. We do note a difference in heartrate between our DMD and healthy control subjects (Table 1), however there is no significant difference between average heartrates between the DMD subgroups we analyzed in this study. One major advantage to our slope calculation and normalization method is that this standardization allows for some added consistency when comparing strain curve slope and shape between patients and groups- even when heartrate is variable within a single patient scan or between multiple scans and patients. These benefits and limitations, however, should be considered when examining the significance of strain rate findings.

In previous studies, strain rate used in assessing myocardial infarction showed good reproducibility using 2D CMR FT [53] and in a healthy control group for both 2D and 3D CMR FT [49]. In this study, we found that for every strain metric assessed, slice-level localized strain rate was better able to differentiate between DMD and healthy control subjects compared to corresponding peak strain alone. These results suggest that valuable information might be missed when only peak strain values are considered. Strain rate differences, particularly those between DMD groups with increasing CMP severity may be an early indication of mechanistic changes in the heart. In myocardial infarction, strain rate has been shown to correlate with regional ischemia and akinetic regions [54, 55]. One explanation for strain rate differences in DMD CMP therefore could be an early manifestation of regional heterogeneity of systolic function that is spatially correlated with regional fibrofatty replacement of healthy myocardium. Further work using animal models or larger clinical datasets may help elucidate a mechanistic explanation for these findings.

Within the group of DMD patients we analyzed (n = 43), we were able to identify three distinct groups- LGE-/LVEF > 55%, LGE + /LVEF > 55%, and LGE + /LVEF < 55%. In our particular cohort of subjects, no patient was observed to be both LGE- and have an LVEF < 55%. Interestingly, we observed that a few of our tested metrics—basal E_rr, basal E_rr systolic strain rate and basal E_cc systolic strain rate magnitude values—were significantly decreased in each DMD group compared to a healthy control group. Other metrics including peak basal Ecc, peak basal Ea, and basal Ea systolic strain rate showed significant differences between LGE-/LVEF > 55% and LGE + /LVEF < 55% groups. These results suggest that regional strain metrics derived from 4D CMR may be able to detect early dysfunction even prior to LGE or overt LV dysfunction and differentiate between more mild and severe disease. A comprehensive longitudinal study describing these changes over time in the same patients would be a valuable extension of this work.

While strain rate measurements improved differentiation for every strain quantity we calculated over peak strain alone, we did not observe strong correlations between radial strain and LVEF, T1, T2, or ECV or longitudinal strain and LVEF, T1, T2, or ECV. This may be due in part to the characteristic pattern of DMD associated CMP which primarily affects myofibers in the subepicardial free wall of the LV, though as the condition progresses, transmural fibrosis becomes increasingly more prevalent [44]. Importantly, we also note that we estimate longitudinal strain using the stack of short-axis images with limited resolution in the longitudinal acquisition plane. This low spatial and contrast resolution may contribute to less reliable feature-tracking and E_ll estimations. Another reason we may not be observing these correlations is the wider variation of E_ll and E_rr strain compared to E_cc, making correlative measures less reliable. A 3D FT CMR analysis in DMD patients done by Siddiqui et al. [48] similar to ours showed insignificant differences in global E_ll between DMD and healthy control subjects but did observe differences in global E_rr and global E_cc. This study also reported similar ranges to those we found for E_ll, E_cc, and E_rr, derived from 3D FT CMR in DMD CMP. A different meta-analysis examining global longitudinal strain measured by 2D speckle tracking echocardiography in eight studies showed that global longitudinal strain and circumferential strain but not radial strain were significantly decreased in DMD vs healthy subjects, though the study did show heterogeneity in results [56]. As with all strain estimation methods, differences in acquisition modality, method, and analysis should all be considered when interpreting results.

In our analysis we observed significant differences in E_rr between healthy and DMD patients, though relatively lower AUC values compared to E_cc and E_a. This may be due in part to the wide range of E_rr values in our method. This variability could be due to dyskinesia resulting in a shift of time of contraction or pathological issues related to radial thickening seen in the DMD patients. It could also be a tracking issue due to through-plane motion from circumferential and longitudinal deformation exacerbated by thinner myocardial walls in more advanced cardiomyopathy. E_rr estimation has historically been a more difficult metric to measure consistently. For example, Cao et al. showed moderate differences in E_cc and E_ll between vendors using CMR FT, but very large differences in E_rr [57]. Despite these considerations, we observed that E_rr may still be a valuable metric and warrants further validation and comparison in DMD populations.

We also explored the use of E_a and E_a strain rate metrics from 3D + time CMR imaging in DMD patients. This metric, while unique to CMR analysis in DMD patients, has been explored with 3D speckle tracking echocardiography (3D-STE) in DMD CMP. For example, Yu et al. demonstrated that E_a derived from 3D-STE had an 85.7% sensitivity and a 71.0% specificity for differentiating DMD patients (n = 56) from controls (n = 31) [58]. E_a is a relatively novel metric, unique to 3D imaging, that takes into account both longitudinal and circumferential shortening [59]. Also, since the myocardium is relatively incompressible, radial thickening during systole influences E_a setting up an inverse relationship between E_a and E_rr. The integration of these effects into a single strain parameter makes E_a potentially useful in examining subclinical dysfunction. Since this is a relatively novel parameter, more studies are needed to determine its full value, especially in CMR imaging. Conventional echocardiographic strain estimation techniques often have a higher temporal resolution (< 5 ms) compared to CMR (20–50 ms), though CMR offers superior contrast resolution [50]. Additionally, methodological differences make a direct comparison of strain values between STE and CMR difficult; depending on technique and study population, these values may not be in agreement [60]. Transthoracic echocardiography (TTE) is used for the initial screening of cardiac function in nearly every patient population, including those at risk for DMD. However, as the disease progresses, limited TTE windows and image artifacts due to scoliosis and fat deposition make cardiac assessment with TTE increasingly difficult [61]. In many DMD patients, only a small number of measures of LV function can be reliably estimated from TTE [61]. Thus, while 3D-STE is a promising characterization technique in some patients, CMR remains the gold standard for evaluation in this patient population.

Many other strain imaging techniques are currently being used to analyze CMR data. The technique described in this work is best characterized as a 3D feature-tracking Lagrangian deformation estimation technique that utilizes image features in CMR scans to estimate strain. Ventricular boundaries, brightness, and homogeneity are all tracked throughout the cardiac cycle to produce deformation. Others have used similar 2D feature-tracking techniques in DMD cohorts to detect morphologic changes in the absence of LGE as well as to detect changes between DMD and healthy subjects not seen using 3D-STE [62]. One major benefit to the feature-tracking approach we use is that it utilizes conventional short-axis cine images and thus does not increase the complexity or length of a typical CMR imaging protocol. Another major advantage of our platform is that it provides additional and more comprehensive 3D imaging strain metrics (i.e. surface area strain, strain rate) compared to conventional metrics typically available through other commercial platforms. The nature of our platform also allows for raw export of image and segmentation data for further, more extensive analysis- one example being the 48-point surface representation of the ventricle which lends itself well to machine and deep learning algorithms. We note as well that this 48-point surface representation does not employ a more traditional basal, mid-ventricular, and apical sections, but rather utilizes a 4-slice length-based analysis (25%, 50%, 75%, 100%). We found that the use of this 4-slice representation allowed for reasonably accurate feature tracking while also permitting a simple and rapid analysis. One drawback to this method is the need to interpolate between slices. A benefit to this approach, however, is that the segmentation framework is adaptable such that more or less slices can easily be added to enhance future analysis. A major drawback to our method is that the post-processing steps for each scan require a moderate amount of training and additional time to complete. Non-expert, non-clinical users of the graphical user interface felt comfortable with its use after 20–30 min of training. After some practice, users were able to complete a full 4D analysis in 30–45 min. While this analysis time is likely not feasible outside of a research setting, efforts are being made to further simplify this approach and incorporate machine learning techniques to automate manual segmentation and analysis [24].

It is important to note that all CMR strain imaging techniques have benefits and drawbacks. HARmonic Phase (HARP) analysis is perhaps the most utilized strain imaging method incorporated into the largest number of post-processing software packages [63‐65]. HARP is a CMR tagging strain analysis technique that isolates one Fourier component of the amplitude modulated data, and tracks pixels with consistent phase [64]. This allows for a relatively rapid and reproducible strain estimation compared to other techniques that involve additional scan time. HARP analysis in conjunction with CMR tagging has been used to reliably estimate strain in DMD patients allowing for robust patient stratification [46, 47]. While this technique is useful in many clinical scenarios, additional scan time, lack of standardization, low spatial resolution, and tag fading are all drawbacks to this method [66, 67].

Displacement encoding with stimulated echoes (DENSE), strain encoded CMR (SENC), and tissue phase mapping (TPM) are additional methods for strain estimation using CMR, but these are less studied in DMD populations [46, 47]. DENSE is generally accepted as an accurate and reproducible method for strain imaging as it relies on the phase information of a stimulated echo and is directly proportional to tissue displacement. By analyzing directional-encoded phase images, the Lagrangian displacement fields can be produced. SENC is similar to HARP in that it utilizes parallel tagged lines with out-of-plane phase encoding gradients to estimate strain. Finally, TPM relies solely on the pixel phase from which it encodes velocity from each image allowing for spatial integration and estimation of deformation and strain. Each of these techniques relies on specialized image sequences and takes additional scan time, but they produce relatively high spatial and temporal resolution needed for strain estimates. In addition, each of these methods is not widely used clinically, though they are being used in research studies [68‐70]. Each of the additional CMR strain imaging techniques mentioned (HARP, DENSE, SENC, and TPM) are most often used on 2D images, though they could be used in 3D reconstruction techniques for strain estimation. Fully 3D CMR acquisition is being explored but the acquisition time is longer than conventional 2D scanning. Additionally, techniques must be robust enough to overcome motion artifacts, and excitation of 3D volume may diminish image contrast between blood and myocardium [71].

The primary purpose and scope of this work was to determine the feasibility and utility of our 3D cine strain analysis technique in DMD CMP. While we see similar differences between DMD and healthy control subjects, the ranges for strain values may differ slightly from other techniques. This is not a unique issue in our proposed method as studies have shown differences in 2D CMR strain where FT techniques and in different cardiac pathologies. For example, Chew et al. demonstrated that CMR FT may overestimate strain when compared to SENC in adult and pediatric congenital heart disease cohorts [72]. Others have shown good agreement between CMR FT and HARP measuring E_cc in a DMD population [73], and CMR FT and DENSE in adults with myocardial infarction [74]. Even when using CMR FT on the same cohort of patients with CMP, inter-vendor differences were found in E_cc and E_ll and E_rr [57]. Small differences have also been shown between 2 and 3D derived CMR FT strain values in DMD [48] and healthy adult populations [49]. As with most sequential cine acquisitions it is also possible that we are very slightly underestimating peak strain because our raw image temporal resolution (20–25 images per cardiac cycle) may not perfectly capture the moment of peak myocardial contraction especially with higher heartrate. Thus, as with any other technique, one should interpret the control and DMD strain value ranges considering these differences between techniques, vendors, and disease characteristics. Future prospective studies will be needed to fully compare and validate the approach described here with 2D and 3D CMR strain imaging data acquired from the same patients. Analysis of a larger longitudinal cohort will also allow us to determine if we can predict outcomes from the novel metrics we derive from our 4D CMR strain method.

In this study, we demonstrate a novel 3D + time CMR imaging analysis platform and describe sensitive and specific strain and strain rate metrics in DMD patients that differentiate mild cardiac disease from healthy subjects and correlate with LVEF and ECV. We also describe for the first time Ea and strain rate metrics derived from 3D cine CMR imaging in a cohort of DMD patients that significantly differentiate these patients from healthy subjects. Identification of sensitive imaging markers for CMP onset and progression could guide prospective therapeutic intervention and refine the power for clinical trials aimed to improve outcomes and therapies in this vulnerable population.