Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking—a cardiovascular MR study in health and disease

Myocardial strain allows for a quantitative measurement of myocardial deformation. Analysis of strain using cardiovascular magnetic resonance (CMR) can be obtained either by tissue tagging or by direct feature tracking (FT) on standard cine images. While CMR tagging has been validated and has further advanced into various different techniques (e.g., (fast) Strain Encoding Magnetic Resonance imaging (SENC) or displacement encoding with stimulated echoes (DENSE)) [1‐4], it still has the drawback of requiring special sequences and scan time. In contrast to this, FT is a promising tool as it allows for the assessment of segmental and global strains in longitudinal, circumferential, and radial directions (LS, CS, RS, GLS, GCS, and GRS, respectively) from standard cine images which are usually acquired during clinical routine [5]. Left ventricular (LV) strain analysis applying CMR has been implied in a wide array of clinical diseases ranging from chemotherapy-induced cardiotoxicity [6, 7] to ischemic heart disease [8‐10] and even non-ischemic heart diseases like hypertrophic cardiomyopathies (HCMs) [11, 12] or cases of acute myocarditis [13, 14]. Despite its wide utility and power to detect myocardial changes even in states with preserved function, FT still lacks standardization and consensus about the methodological process. In a previous study, different factors, like post-processing software used, slice selection, and 2D or 3D analysis, which all have the potential to influence strain values, were analyzed [15]. Additionally, one must take the time-consuming manual contouring process as well as the reader’s level of expertise and training into consideration, which also impact strain evaluation [16]. One potential approach to reduce influence of manually derived contours is the use of artificial intelligence (AI)–derived contours. AI-based segmentation and strain evaluation have been previously applied and validated in a large cohort with commercially available software [17] as well as commercially unavailable software [18]. These advances may further streamline strain assessment and help to reach a consensus about a standardized approach to feature tracking in clinical routine and “big data” studies.

Our study aimed at evaluating and comparing manual and AI-based approaches regarding quantitative strain metrics used in clinical routine as well as on a contour level for strain assessment by FT in healthy volunteers and patients with different cardiac diseases in order to identify strengths and weaknesses of these methods.

The ethics review board approved all studies and all participants gave written informed consent.

The main results of our studies are as follows: strain analysis by FT on cine images based on AI-derived contours is feasible and results in equivalent global and segmental strain values with the exception of lateral segments in hypertrophied ventricles. The difference however is attributable to tracking errors as the spatial overlap metric shows good agreement of the methods.

CMR has become the gold standard for LV and right ventricular volume and mass quantification [28] with a standardized approach for analysis and post-processing of images [29]. Yet, there are no consensus recommendations on how to quantify LV myocardial tissue dynamics and deformation by applying CMR. Therefore, we explored how strain assessment by FT can potentially become more standardized involving AI-powered approaches. In our study, we could demonstrate that AI-generated contours for strain assessment by FT are reliable and result in equivalent global and segmental values. In a previous study by Ruijsink et al, DSC between manual and AI-based segmentation was 93% for the endocardial segmentation and 84% for the epicardial segmentation [18]. We found a similar DSC for the myocardial class in our study. Other segmentation algorithms which were tested on the “Automatic Cardiac Diagnosis Challenge” (ACDC) dataset, achieved DSC scores up to 96% for the LV [30]. These scores are higher than the one presented here; however, the underlying dataset in the challenge plays an important factor. The ACDC dataset included similar pathologies as in this study, such as HCM, CMI, and dilated cardiomyopathies; however, whether scans were carried out after contrast administration was not clearly depicted. Images obtained after contrast media application pose an additional challenge as myocardial fibrosis might be mistaken for the blood pool by AI algorithms as well as human readers. In this study, we found the overall lowest segmentation overlap, indicated by DSC and HD metrics, in the CMI group. As all scans in the CMI group were carried out after contrast administration and each case included at least one focal fibrosis; these scans posed a challenge for the algorithm. This was also evidenced by the lower DSC and HD in the comparison of pre-CM and post-CM images. In the subset of LVH, we found higher DSC scores in comparison to the healthy and CMI cohorts. We believe that this paradoxon can be explained by the larger LVM in the LVH cohort as the overall differences in segmentations are divided by a larger area. When considering the HD metric, the healthy cohort showed the lowest value regarding the SAX segmentations.

When segmental values, which are defined according to the AHA model, are compared, the insertion point has to be taken into consideration. In order to verify the proper insertion, visual analysis can be carried out; however, for large data, this is tedious. We propose therefore a comparison based on angular differences as outlined in the methods. The highest angular differences (8.7°) were seen in the CMI-F cohort. Comparing this to the general division of the AHA segments (60°), we feel that these are neglectable; however, the relevance of angular differences and their impact on AHA segments ought to be investigated further.

Interestingly, the study by Ruijsink et al used 3 SAX slices as well as 2cv and 4cv for strain assessment with diastolic contours. This might potentially impact the strain analysis of CS and RS values [15]. Previous studies reported normal values for FT on the full SAX coverage [5, 31]. To additionally achieve a more streamlined post-processing of CMR images, AI algorithms, with the placement of contours in ED and ES for functional assessment, can “recycle” these contours for strain assessment by FT. Further studies employing this approach, potentially in clinical routine scenarios, are needed to verify whether this approach is feasible in a real-world setting. We analyzed not only a healthy population but also one with different clinical entities, which were chosen as either to be a challenge regarding the segmentation (post-contrast cines with fibrosis) or for the FT algorithm (wall motion abnormalities) or both (LVH subgroup, CMI-EWF). In addition, we compared all cases 1:1 and not only selected cases, which minimizes the possibility of errors. The LVH group, especially AS and HCM cases, was burdensome for the AI on a contour and tracking level. Studies about AS and CMR FT have been previously published but none covered AI-derived contours or analysis of tracking errors [32‐37]. As the majority of tracking issues in the LVH group was related to the anterolateral and inferolateral segments, papillary muscle hypertrophy could have played a role [38, 39]. As the FT algorithm relies on recognition of voxel-based features, which are in general sparse or even absent in the normal myocardium [40], hypertrophy of the myocardial tissue can further deteriorate tracking. We found the highest CS and LS as well as the lowest RS value in the abovementioned segments in the healthy cohort. Andre et al presented similar results, with the highest variance in these segments. Potentially, the movement of the papillary muscle can have an impact on the values in these segments even in non-pathologic states, which would be further exacerbated with hypertrophy of the myocardium and its appendages. A previous study found statistically significant differences between healthy male and female volunteers in AHA segment 5 of LS analysis [15]. One other study reported a significantly larger papillary muscle mass in males compared to females [41], which might possibly explain the previous findings and the ones presented here. In concordance with the identified segments that pose a challenge for the FT algorithm, the AI-derived strain values show the only significant differences compared to manual contours in these segments. Interestingly, the differences between the methods were due to strain values for AS and HCM cases (Supplementary Material 2). These differences however are not based on a contour level as evidenced by the DSC. Potential influencing factors might relate to the LAX extent or the acquisition itself. In comparison to the other pathologies, we found the 3cv slice location in this group frequently at a narrower angle. This finding might be related to a prominent and dilated ascending aortic root in AS patients impairing proper 3cv acquisitions [42, 43].

The intersegmental differences are a drawback for FT-derived strain values. In a head-to-head comparison between fast-SENC, tagging, and FT, all techniques had good reproducibility; however, in a segmental inter-study comparison, FT showed the lowest agreement [3]. This was confirmed by other studies reporting a rather large variation across segments rendering comparisons rather unfeasible and additionally demonstrating that segmental analysis with FT is complex and clinical implications uncertain [31, 44]. A potential solution might be the use of regional instead of segmental values [45]. In contrast to FT, other techniques such as DENSE have a higher reproducibility of segmental strain values [46, 47]. In addition, segmental strain values provided by DENSE have been shown to carry a prognostic implication in patients after an acute myocardial infarction [48]. Regarding the segmental approach, SENC-derived strain values similarly show a better intersegmental agreement [3, 49]. The fast SENC technique seems highly reproducible, even across different sites [50, 51]. Clinical application of this technique has shown clinical merit; however, more research is needed regarding segmental values [52]. In general, all strain values derived from commercially available software are potentially limited in their comparability as new versions provide new values; hence, providing the software version applied is of great importance.

Lastly, we want to comment on the effect of contrast media application on FT-derived strain values. On the one hand, we noticed that the segmentation becomes more challenging for the AI algorithm; on the other hand, post-CM strain values are lower. This is in line with previous literature [53]. As SAX acquisition are now most of the time acquired after contrast media application, challenges and AI segmentation networks have to take this into consideration.

Our study shows that application of AI-derived contours for feature tracking and strain analysis in CMR yields results comparable to manual segmentations. Attention should be taken while evaluating left ventricle hypertrophy cases especially in patients with aortic stenosis independent of the use of manual or AI-derived contours.