Diagnostic capability of feature-tracking cardiovascular magnetic resonance to detect infarcted segments: a comparison with tagged magnetic resonance and wall thickening analysis

Segmental strain for scar detection in acute myocardial infarcts and in follow-up exams using non-contrast CMR cine sequences

Background

The purpose of the study was to investigate feasibility of infarct detection in segmental strain derived from non-contrast cardiac magnetic resonance (CMR) cine sequences in patients with acute myocardial infarction (AMI) and in follow-up (FU) exams.

Methods

57 patients with AMI (mean age 61 ± 12 years, CMR 2.8 ± 2 days after infarction) were retrospectively included, FU exams were available in 32 patients (35 ± 14 days after first CMR). 43 patients with normal CMR (54 ± 11 years) served as controls. Dedicated software (Segment CMR, Medviso) was used to calculate global and segmental strain derived from cine sequences. Cine short axis stacks and segmental circumferential strain calculations of every patient and control were presented to two blinded readers in random order, who were advised to identify potentially infarcted segments, blinded to LGE and clinical information.

Results

Impaired global strain was measured in AMI patients compared to controls (global peak circumferential strain [GPCS] p = 0.01; global peak longitudinal strain [GPLS] p = 0.04; global peak radial strain [GPRS] p = 0.01). In both imaging time points, mean segmental peak circumferential strain [SPCS] was impaired in infarcted tissue compared to remote segments (AMI: p = 0.03, FU: p = 0.02). SPCS values in infarcted segments were similar between AMI and FU (p = 0.8). In SPCS calculations, 141 from 189 acutely infarcted segments were accurately detected (74.6%), visual evaluation of correlating cine images detected 43.4% infarcts. In FU, 80% infarcted segments (91/114 segments) were detected in SPCS and 51.8% by visual evaluation of correlating short axis cine images (p = 0.01).

Conclusion

Segmental circumferential strain derived from routinely acquired native cine sequences detects nearly 75% of acute infarcts and 80% of infarcts in subacute follow-up CMR, significantly more than visual evaluation of correlating cine images alone. Acute infarcts may display only subtle impairment of wall motion and no obvious wall thinning, thus SPCS calculation might be helpful for scar detection in patients with acute infarcts, when LGE images are not available.

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.

Segmental strain analysis for the detection of chronic ischemic scars in non-contrast cardiac MRI cine images

Cardiac magnetic resonance imaging (MRI) with late gadolinium enhancement (LGE) is considered the gold standard for scar detection after myocardial infarction. In times of increasing skepticism about gadolinium depositions in brain tissue and contraindications of gadolinium administration in some patient groups, tissue strain-based techniques for detecting ischemic scars should be further developed as part of clinical protocols. Therefore, the objective of the present work was to investigate whether segmental strain is noticeably affected in chronic infarcts and thus can be potentially used for infarct detection based on routinely acquired non-contrast cine images in patients with known coronary artery disease (CAD). Forty-six patients with known CAD and chronic scars in LGE images (5 female, mean age 52 ± 19 years) and 24 gender- and age-matched controls with normal cardiac MRI (2 female, mean age 47 ± 13 years) were retrospectively enrolled. Global (global peak circumferential [GPCS], global peak longitudinal [GPLS], global peak radial strain [GPRS]) and segmental (segmental peak circumferential [SPCS], segmental peak longitudinal [SPLS], segmental peak radial strain [SPRS]) strain parameters were calculated from standard non-contrast balanced SSFP cine sequences using commercially available software (Segment CMR, Medviso, Sweden). Visual wall motion assessment of short axis cine images as well as segmental circumferential strain calculations (endo-/epicardially contoured short axis cine and resulting polar plot strain map) of every patient and control were presented in random order to two independent blinded readers, which should localize potentially infarcted segments in those datasets blinded to LGE images and patient information. Global strain values were impaired in patients compared to controls (GPCS p = 0.02; GPLS p = 0.04; GPRS p = 0.01). Patients with preserved ejection fraction showed also impeded GPCS compared to healthy individuals (p = 0.04). In patients, mean SPCS was significantly impaired in subendocardially (-  5.4% ± 2) and in transmurally infarcted segments (- 1.2% ± 3) compared to remote myocardium (- 12.9% ± 3, p = 0.02 and 0.03, respectively). ROC analysis revealed an optimal cut-off value for SPCS for discriminating infarcted from remote myocardium of - 7.2% with a sensitivity of 89.4% and specificity of 85.7%. Mean SPRS was impeded in transmurally infarcted segments (15.9% ± 6) compared to SPRS of remote myocardium (31.4% ± 5; p = 0.02). The optimal cut-off value for SPRS for discriminating scar tissue from remote myocardium was 16.6% with a sensitivity of 83.3% and specificity of 76.5%. 80.3% of all in LGE infarcted segments (118/147) were correctly localized in segmental circumferential strain calculations based on non-contrast cine images compared to 53.7% (79/147) of infarcted segments detected by visual wall motion assessment (p > 0.01). Global strain parameters are impaired in patients with chronic infarcts compared to controls. Mean SPCS and SPRS in scar tissue is impeded compared to remote myocardium in infarcts patients. Blinded to LGE images, two readers correctly localized 80% of infarcted segments in segmental circumferential strain calculations based on non-contrast cine images, in contrast to only 54% of infarcted segments detected due to wall motion abnormalities in visual wall motion assessment. Analysis of segmental circumferential strain shows a promising method for detection of chronic scars in routinely acquired, non-contrast cine images for patients who cannot receive or decline gadolinium.

Comparison of Left Ventricular Global Strain in Anterior and Non-anterior Wall Myocardial Infarction With CMR Tissue Tracking

Left ventricular (LV) myocardial dysfunction occurs after myocardial infarction (MI) is associated with the location, infarct size, and transmurality degrees of MI. The myocardial strain is a sensitive index used for the quantification of myocardium dysfunction. This study used Tissue-Tracking to evaluate whether the different location of MI would result in different myocardial dysfunction. One hundred patients diagnosed with MI who underwent cardiovascular magnetic resonance examination were included. The tissue-tracking indices, LV global radial strain (GRS), global circumferential strain (GCS), global longitudinal strain (GLS), and the infarct size (IS,% of LV mass) were quantified. There were 42 cases of anterior wall MI (AWMI) and 58 cases of non-anterior wall MI (NAWMI). The GCS of AWMI was significantly lower than that of NAWMI (P = 0.036). In the same level of infarct size, the myocardial strain of AWMI was not significantly different from NAWMI group (P > 0.05). The GRS and GCS were significantly different between transmurality > 50% group with transmurality ≤ 50% group (P < 0.05). The present study demonstrated that LV MI is associated with reduced myocardial strain, and the infarct size and degrees of transmurality were both related to the decline of myocardial strain in patients with MI.

Tissue-tracking in the assessment of late gadolinium enhancement in myocarditis and myocardial infarction

PURPOSE

To test the diagnostic performance of cardiovascular magnetic resonance (CMR) tissue-tracking (TT) to detect the presence of late gadolinium enhancement (LGE) in patients with a diagnosis of myocardial infarction (MI) or myocarditis (MYO), preserved left ventricular ejection fraction (LVEF) and no visual regional wall motion abnormalities (RWMA).

METHODS

We selected consecutive CMR studies of 50 MI, 50 MYO and 96 controls. Receiving operating characteristic (ROC) curve and net reclassification index (NRI) analyses were used to assess the predictive ability and the incremental diagnostic yield of 2D and 3D TT-derived strain parameters for the detection of LGE and to measure the best cut-off values of strain parameters.

RESULTS

Overall, cases showed significantly reduced 2D global longitudinal strain (2D-GLS) values compared with controls (-20.1 ± 3.1% vs -21.6 ± 2.7%; p = 0.0008). 2D-GLS was also significantly reduced in MYO patients compared with healthy controls (-19.7 ± 2.9% vs -21.9 ± 2.4%; p = 0.0001). 3D global radial strain (3D-GRS) was significantly reduced in MI patients compared with controls with risk factors (34.3 ± 11.8% vs 40.3 ± 12.5%, p = 0.024) Overall, 2D-GLS yielded good diagnostic accuracy for the detection of LGE in the MYO subgroup (AUROC 0.79; NRI (95% CI) = 0.6 (0.3, 1.02) p = 0.0004), with incremental predictive value beyond risk factors and LV function parameters (p for AUROC difference = 0.048). In the MI subgroup, 2D-GRS (AUROC 0.81; NRI (95% CI) = 0.56 (0.17, 0.95) p = 0.004), 3D-GRS (AUROC 0.82; NRI (95% CI) = 0.57 (0.17, 0.97) p = 0.006) and 3D global circumferential strain (3D-GCS) (AUROC 0.81; NRI (95% CI) = 0.62 (0.22, 1.01) p = 0.002) emerged as potential markers of disease. The best cut-off for 2D-GLS was -21.1%, for 2D- and 3D-GRS were 39.1% and 37.7%, respectively, and for 3D-GCS was -16.4%.

CONCLUSIONS

At CMR-tissue tracking analysis, 2D-GLS was a significant predictor of LGE in patients with myocarditis but preserved LVEF and no visual RWMA. Both 2D- and 3D-GRS and 2D-GCS yielded good diagnostic accuracy for LGE detection in patients with previous MI but preserved LVEF and no visual RWMA.

3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging

Many cardiac catheter interventions require accurate discrimination between healthy and infarcted myocardia. The gold standard for infarct imaging is late gadolinium-enhanced MRI (LGE-MRI), but during cardiac procedures electroanatomical or electromechanical mapping (EAM or EMM, respectively) is usually employed. We aimed to improve the ability of EMM to identify myocardial infarction by combining multiple EMM parameters in a statistical model. From a porcine infarction model, 3D electromechanical maps were 3D registered to LGE-MRI. A multivariable mixed-effects logistic regression model was fitted to predict the presence of infarct based on EMM parameters. Furthermore, we correlated feature-tracking strain parameters to EMM measures of local mechanical deformation. We registered 787 EMM points from 13 animals to the corresponding MRI locations. The mean registration error was 2.5 ± 1.16 mm. Our model showed a strong ability to predict the presence of infarction (C-statistic = 0.85). Strain parameters were only weakly correlated to EMM measures. The model is accurate in discriminating infarcted from healthy myocardium. Unipolar and bipolar voltages were the strongest predictors.

Recovery and prognostic value of myocardial strain in ST-segment elevation myocardial infarction patients with a concurrent chronic total occlusion

Objectives

Global left ventricular (LV) function is routinely used to assess cardiac function; however, myocardial strain is able to identify more subtle dysfunction. We aimed to determine the recovery and prognostic value of featuring tracking (FT) cardiovascular magnetic resonance (CMR) strain in ST-segment elevation myocardial infarction (STEMI) patients with a concurrent chronic total occlusion (CTO).

Methods

In the randomized EXPLORE trial, there was no significant difference in global LV function after percutaneous coronary intervention (PCI) of the CTO, compared with no-CTO PCI, post-STEMI. In the current study, we included 200 of the 302 EXPLORE patients with a baseline CMR, of which 180 also had 4-month follow-up (serial) CMR. Global longitudinal strain (GLS) was calculated from 3 long-axis views. Global circumferential strain (GCS) and segmental strain were calculated from 3 short-axis views (basal, mid, and apical).

Results

Global strain significantly improved at 4 months (GLS ∆ − 1.8 ± 4.3%, p < 0.001; GCS ∆ − 1.7 ± 4.7%, p < 0.001); however, there was no treatment effect of CTO-PCI on strain recovery. GLS was a significant predictor for 4 months of LV ejection fraction (p = 0.006), incremental to other CMR parameters including infarct size. For mortality, infarct size remained the strongest predictor. On regional level, segmental strain independently predicted recovery in the dysfunctional segments (p < 0.001).

Conclusions

Global and segmental myocardial strains significantly improved over time, with no effect of CTO-PCI. Global strain was associated with outcome and segmental strain was an independent predictor for regional LV recovery in the dysfunctional CTO territory. Further research is needed to determine the additional prognostic value of strain beyond routine CMR parameters.

Key Points

• In STEMI patients with a concurrent CTO, strain significantly improves over time, regardless of CTO-PCI.

• Global strain is an independent predictor for functional recovery, incremental to infarct size, LVEF, and clinical parameters.

• Segmental strain was able to predict the recovery of wall thickening, incremental to transmural extent of infarction.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06338-x) contains supplementary material, which is available to authorized users.

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI

Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for chronic MI delineation via deep learning on non-contrast material-enhanced cardiac cine MRI. Materials and Methods In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis. Results Study participants included 212 patients with chronic MI (men, 171; age, 57.2 years ± 12.5) and 87 healthy control patients (men, 42; age, 43.3 years ± 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm² ± 2.8 vs 5.5 cm² ± 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% ± 17.3 vs 18.5% ± 15.4; P = .17; correlation coefficient, r = 0.89). Conclusion The proposed deep learning framework on nonenhanced cardiac cine MRI enables the confirmation (presence), detection (position), and delineation (transmurality and size) of chronic myocardial infarction. However, future larger-scale multicenter studies are required for a full validation. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Leiner in this issue.

Recent Advances in Cardiac Magnetic Resonance Imaging

Cardiac magnetic resonance (CMR) imaging provides accurate anatomic information and advanced soft contrast, making it the reference standard for assessing cardiac volumes and systolic function. In this review, we summarize the recent advances in CMR sequences. New technical development has widened the use of CMR imaging beyond the simple characterization of myocardial scars and assessment of contractility. These novel CMR sequences offer comprehensive assessments of coronary plaque characterization, myocardial fiber orientation, and even metabolic activity, and they can be readily applied in clinical settings. CMR imaging is able to provide new insights into understanding the pathophysiologic process of underlying cardiac disease, and it can help physicians choose the best treatment strategies. Although several limitations, including the high cost and time-consuming process, have limited the widespread clinical use of CMR imaging so far, recent advances in software and hardware technologies have made the future more promising.