Quantitative myocardial infarction on delayed enhancement MRI. Part I: Animal validation of an automated feature analysis and combined thresholding infarct sizing algorithm

Impact of late gadolinium enhancement image acquisition resolution on neural network based automatic scar segmentation

BACKGROUND

Automatic myocardial scar segmentation from late gadolinium enhancement (LGE) images using neural networks promises an alternative to time-consuming and observer-dependent semi-automatic approaches. However, alterations in data acquisition, reconstruction as well as post-processing may compromise network performance. The objective of the present work was to systematically assess network performance degradation due to a mismatch of point-spread function between training and testing data.

METHODS

Thirty-six high-resolution (0.7×0.7×2.0 mm3) LGE k-space datasets were acquired post-mortem in porcine models of myocardial infarction. The in-plane point-spread function and hence in-plane resolution Δx was retrospectively degraded using k-space lowpass filtering, while field-of-view and matrix size were kept constant. Manual segmentation of the left ventricle (LV) and healthy remote myocardium was performed to quantify location and area (% of myocardium) of scar by thresholding (≥ SD5 above remote). Three standard U-Nets were trained on training resolutions Δxtrain = 0.7, 1.2 and 1.7 mm to predict endo- and epicardial borders of LV myocardium and scar. The scar prediction of the three networks for varying test resolutions (Δxtest = 0.7 to 1.7 mm) was compared against the reference SD5 thresholding at 0.7 mm. Finally, a fourth network trained on a combination of resolutions (Δxtrain = 0.7 to 1.7 mm) was tested.

RESULTS

The prediction of relative scar areas showed the highest precision when the resolution of the test data was identical to or close to the resolution used during training. The median fractional scar errors and precisions (IQR) from networks trained and tested on the same resolution were 0.0 percentage points (p.p.) (1.24 - 1.45), and - 0.5 - 0.0 p.p. (2.00 - 3.25) for networks trained and tested on the most differing resolutions, respectively. Deploying the network trained on multiple resolutions resulted in reduced resolution dependency with median scar errors and IQRs of 0.0 p.p. (1.24 - 1.69) for all investigated test resolutions.

CONCLUSION

A mismatch of the imaging point-spread function between training and test data can lead to degradation of scar segmentation when using current U-Net architectures as demonstrated on LGE porcine myocardial infarction data. Training networks on multi-resolution data can alleviate the resolution dependency.

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

Myocardial infarction (MI) occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is cardiovascular magnetic resonance imaging (MRI) with intravenously administered gadolinium-based contrast (with damaged areas apparent as late gadolinium enhancement [LGE]). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. It has the potential to reduce uncertainty due to technical variability across labs and the inherent problems of data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by atrous spatial pyramid pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: (i) background, (ii) heart muscle, (iii) blood and (iv) LGE areas. Our experiments show that the model named MI-ResNet50-AC provides the best global accuracy (97.38%), mean accuracy (86.01%), weighted intersection over union (IoU) of 96.47%, and bfscore of 64.46% for the global segmentation. However, in detecting only LGE tissue, a smaller model, MI-ResNet18-AC, exhibited higher accuracy (74.41%) than MI-ResNet50-AC (64.29%). New models were compared with state-of-the-art models and manual quantification. Our models demonstrated favorable performance in global segmentation and LGE detection relative to the state-of-the-art, including a four-fold better performance in matching LGE pixels to contours produced by clinicians.

Infarct quantification with cardiovascular magnetic resonance using "standard deviation from remote" is unreliable: validation in multi-centre multi-vendor data

BACKGROUND

The objective of the study was to investigate variability and agreement of the commonly used image processing method "n-SD from remote" and in particular for quantifying myocardial infarction by late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR). LGE-CMR in tandem with the analysis method "n-SD from remote" represents the current reference standard for infarct quantification. This analytic method utilizes regions of interest (ROIs) and defines infarct as the tissue with a set number of standard deviations (SD) above the signal intensity of remote nulled myocardium. There is no consensus on what the set number of SD is supposed to be. Little is known about how size and location of ROIs and underlying signal properties in the LGE images affect results. Furthermore, the method is frequently used elsewhere in medical imaging often without careful validation. Therefore, the usage of the "n-SD" method warrants a thorough validation.

METHODS

Data from 214 patients from two multi-center cardioprotection trials were included. Infarct size from different remote ROI positions, ROI size, and number of standard deviations ("n-SD") were compared with reference core lab delineations.

RESULTS

Variability in infarct size caused by varying ROI position, ROI size, and "n-SD" was 47%, 48%, and 40%, respectively. The agreement between the "n-SD from remote" method and the reference infarct size by core lab delineations was low. Optimal "n-SD" threshold computed on a slice-by-slice basis showed high variability, n = 5.3 ± 2.2.

CONCLUSION

The "n-SD from remote" method is unreliable for infarct quantification due to high variability which depends on different placement and size of remote ROI, number "n-SD", and image signal properties related to the CMR-scanner and sequence used. Therefore, the "n-SD from remote" method should not be used, instead methods validated against an independent standard are recommended.

CMRSegTools: An open-source software enabling reproducible research in segmentation of acute myocardial infarct in CMR images

In the last decade, a large number of clinical trials have been deployed using Cardiac Magnetic Resonance (CMR) to evaluate cardioprotective strategies aiming at reducing the irreversible myocardial damage at the time of reperfusion. In these studies, segmentation and quantification of myocardial infarct lesion are often performed with a commercial software or an in-house closed-source code development thus creating a barrier for reproducible research. This paper introduces CMRSegTools: an open-source application software designed for the segmentation and quantification of myocardial infarct lesion enabling full access to state-of-the-art segmentation methods and parameters, easy integration of new algorithms and standardised results sharing. This post-processing tool has been implemented as a plug-in for the OsiriX/Horos DICOM viewer leveraging its database management functionalities and user interaction features to provide a bespoke tool for the analysis of cardiac MR images on large clinical cohorts. CMRSegTools includes, among others, user-assisted segmentation of the left-ventricle, semi- and automatic lesion segmentation methods, advanced statistical analysis and visualisation based on the American Heart Association 17-segment model. New segmentation methods can be integrated into the plug-in by developing components based on image processing and visualisation libraries such as ITK and VTK in C++ programming language. CMRSegTools allows the creation of training and testing data sets (labeled features such as lesion, microvascular obstruction and remote ROI) for supervised Machine Learning methods, and enables the comparative assessment of lesion segmentation methods via a single and integrated platform. The plug-in has been successfully used by several CMR imaging studies.

Histopathological validation of semi-automated myocardial scar quantification techniques for dark-blood late gadolinium enhancement magnetic resonance imaging

AIMS

To evaluate the performance of various semi-automated techniques for quantification of myocardial infarct size on both conventional bright-blood and novel dark-blood late gadolinium enhancement (LGE) images using histopathology as reference standard.

METHODS AND RESULTS

In 13 Yorkshire pigs, reperfused myocardial infarction was experimentally induced. At 7 weeks post-infarction, both bright-blood and dark-blood LGE imaging were performed on a 1.5 T magnetic resonance scanner. Following magnetic resonance imaging (MRI), the animals were sacrificed, and histopathology was obtained. The percentage of infarcted myocardium was assessed per slice using various semi-automated scar quantification techniques, including the signal threshold vs. reference mean (STRM, using 3 to 8 SDs as threshold) and full-width at half-maximum (FWHM) methods, as well as manual contouring, for both LGE methods. Infarct size obtained by histopathology was used as reference. In total, 24 paired LGE MRI slices and histopathology samples were available for analysis. For both bright-blood and dark-blood LGE, the STRM method with a threshold of 5 SDs led to the best agreement to histopathology without significant bias (-0.23%, 95% CI [-2.99, 2.52%], P = 0.862 and -0.20%, 95% CI [-2.12, 1.72%], P = 0.831, respectively). Manual contouring significantly underestimated infarct size on bright-blood LGE (-1.57%, 95% CI [-2.96, -0.18%], P = 0.029), while manual contouring on dark-blood LGE outperformed semi-automated quantification and demonstrated the most accurate quantification in this study (-0.03%, 95% CI [-0.22, 0.16%], P = 0.760).

CONCLUSION

The signal threshold vs. reference mean method with a threshold of 5 SDs demonstrated the most accurate semi-automated quantification of infarcted myocardium, without significant bias compared to histopathology, for both conventional bright-blood and novel dark-blood LGE.

Study protocol: MyoFit46-the cardiac sub-study of the MRC National Survey of Health and Development

BACKGROUND

The life course accumulation of overt and subclinical myocardial dysfunction contributes to older age mortality, frailty, disability and loss of independence. The Medical Research Council National Survey of Health and Development (NSHD) is the world's longest running continued surveillance birth cohort providing a unique opportunity to understand life course determinants of myocardial dysfunction as part of MyoFit46-the cardiac sub-study of the NSHD.

METHODS

We aim to recruit 550 NSHD participants of approximately 75 years+ to undertake high-density surface electrocardiographic imaging (ECGI) and stress perfusion cardiovascular magnetic resonance (CMR). Through comprehensive myocardial tissue characterization and 4-dimensional flow we hope to better understand the burden of clinical and subclinical cardiovascular disease. Supercomputers will be used to combine the multi-scale ECGI and CMR datasets per participant. Rarely available, prospectively collected whole-of-life data on exposures, traditional risk factors and multimorbidity will be studied to identify risk trajectories, critical change periods, mediators and cumulative impacts on the myocardium.

DISCUSSION

By combining well curated, prospectively acquired longitudinal data of the NSHD with novel CMR-ECGI data and sharing these results and associated pipelines with the CMR community, MyoFit46 seeks to transform our understanding of how early, mid and later-life risk factor trajectories interact to determine the state of cardiovascular health in older age.

TRIAL REGISTRATION

Prospectively registered on ClinicalTrials.gov with trial ID: 19/LO/1774 Multimorbidity Life-Course Approach to Myocardial Health- A Cardiac Sub-Study of the MCRC National Survey of Health and Development (NSHD).

Texture-based probability mapping for automatic scar assessment in late gadolinium-enhanced cardiovascular magnetic resonance images

Purpose

To evaluate a novel texture-based probability mapping (TPM) method for scar size estimation in LGE-CMRI.

Methods

This retrospective proof-of-concept study included chronic myocardial scars from 52 patients. The TPM was compared with three signal intensity-based methods: manual segmentation, full-width-half-maximum (FWHM), and 5-standard deviation (5-SD). TPM is generated using machine learning techniques, expressing the probability of scarring in pixels. The probability is derived by comparing the texture of the 3 × 3 pixel matrix surrounding each pixel with reference dictionaries from patients with established myocardial scars. The Sørensen-Dice coefficient was used to find the optimal TPM range. A non-parametric test was used to test the correlation between infarct size and remodeling parameters. Bland-Altman plots were performed to assess agreement among the methods.

Results

The study included 52 patients (76.9% male; median age 64.5 years (54, 72.5)). A TPM range of 0.328–1.0 was found to be the optimal probability interval to predict scar size compared to manual segmentation, median dice (25th and 75th percentiles)): 0.69(0.42–0.81). There was no significant difference in the scar size between TPM and 5-SD. However, both 5-SD and TPM yielded larger scar sizes compared with FWHM (p < 0.001 and p = 0.002). There were strong correlations between scar size measured by TPM, and left ventricular ejection fraction (LVEF, r = −0.76, p < 0.001), left ventricular end-diastolic volume index (r = 0.73, p < 0.001), and left ventricular end-systolic volume index (r = 0.75, p < 0.001).

Conclusion

The TPM method is comparable with current SI-based methods, both for the scar size assessment and the relationship with left ventricular remodeling when applied on LGE-CMRI.

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Texture based probability mapping can be used to evaluate myocardial scar size.

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The method can assess myocardial fibrosis independent of signal intensity.

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The TPM method shows strong correlations between scar size and left ventricular ejection fraction.

High-resolution Free-breathing late gadolinium enhancement Cardiovascular magnetic resonance to diagnose myocardial injuries following COVID-19 infection

PURPOSE

High-resolution free-breathing late gadolinium enhancement (HR-LGE) was shown valuable for the diagnosis of acute coronary syndromes with non-obstructed coronary arteries. The method may be useful to detect COVID-related myocardial injuries but is hampered by prolonged acquisition times. We aimed to introduce an accelerated HR-LGE technique for the diagnosis of COVID-related myocardial injuries.

METHOD

An undersampled navigator-gated HR-LGE (acquired resolution of 1.25 mm3) sequence combined with advanced patch-based low-rank reconstruction was developed and validated in a phantom and in 23 patients with structural heart disease (test cohort; 15 men; 55 ± 16 years). Twenty patients with laboratory-confirmed COVID-19 infection associated with troponin rise (COVID cohort; 15 men; 46 ± 24 years) prospectively underwent cardiovascular magnetic resonance (CMR) with the proposed sequence in our center. Image sharpness, quality, signal intensity differences and diagnostic value of free-breathing HR-LGE were compared against conventional breath-held low-resolution LGE (LR-LGE, voxel size 1.8x1.4x6mm).

RESULTS

Structures sharpness in the phantom showed no differences with the fully sampled image up to an undersampling factor of x3.8 (P > 0.5). In patients (N = 43), this acceleration allowed for acquisition times of 7min21s ± 1min12s at 1.25 mm3 resolution. Compared with LR-LGE, HR-LGE showed higher image quality (P = 0.03) and comparable signal intensity differences (P > 0.5). In patients with structural heart disease, all LGE-positive segments on LR-LGE were also detected on HR-LGE (80/391) with 21 additional enhanced segments visible only on HR-LGE (101/391, P < 0.001). In 4 patients with COVID-19 history, HR-LGE was definitely positive while LR-LGE was either definitely negative (1 microinfarction and 1 myocarditis) or inconclusive (2 myocarditis).

CONCLUSIONS

Undersampled free-breathing isotropic HR-LGE can detect additional areas of late enhancement as compared to conventional breath-held LR-LGE. In patients with history of COVID-19 infection associated with troponin rise, the method allows for detailed characterization of myocardial injuries in acceptable scan times and without the need for repeated breath holds.

Endogenous assessment of myocardial injury with single-shot model-based non-rigid motion-corrected T1 rho mapping

BACKGROUND

Cardiovascular magnetic resonance T1ρ mapping may detect myocardial injuries without exogenous contrast agent. However, multiple co-registered acquisitions are required, and the lack of robust motion correction limits its clinical translation. We introduce a single breath-hold myocardial T1ρ mapping method that includes model-based non-rigid motion correction.

METHODS

A single-shot electrocardiogram (ECG)-triggered balanced steady state free precession (bSSFP) 2D adiabatic T1ρ mapping sequence that collects five T1ρ-weighted (T1ρw) images with different spin lock times within a single breath-hold is proposed. To address the problem of residual respiratory motion, a unified optimization framework consisting of a joint T1ρ fitting and model-based non-rigid motion correction algorithm, insensitive to contrast change, was implemented inline for fast (~ 30 s) and direct visualization of T1ρ maps. The proposed reconstruction was optimized on an ex vivo human heart placed on a motion-controlled platform. The technique was then tested in 8 healthy subjects and validated in 30 patients with suspected myocardial injury on a 1.5T CMR scanner. The Dice similarity coefficient (DSC) and maximum perpendicular distance (MPD) were used to quantify motion and evaluate motion correction. The quality of T1ρ maps was scored. In patients, T1ρ mapping was compared to cine imaging, T2 mapping and conventional post-contrast 2D late gadolinium enhancement (LGE). T1ρ values were assessed in remote and injured areas, using LGE as reference.

RESULTS

Despite breath holds, respiratory motion throughout T1ρw images was much larger in patients than in healthy subjects (5.1 ± 2.7 mm vs. 0.5 ± 0.4 mm, P < 0.01). In patients, the model-based non-rigid motion correction improved the alignment of T1ρw images, with higher DSC (87.7 ± 5.3% vs. 82.2 ± 7.5%, P < 0.01), and lower MPD (3.5 ± 1.9 mm vs. 5.1 ± 2.7 mm, P < 0.01). This resulted in significantly improved quality of the T1ρ maps (3.6 ± 0.6 vs. 2.1 ± 0.9, P < 0.01). Using this approach, T1ρ mapping could be used to identify LGE in patients with 93% sensitivity and 89% specificity. T1ρ values in injured (LGE positive) areas were significantly higher than in the remote myocardium (68.4 ± 7.9 ms vs. 48.8 ± 6.5 ms, P < 0.01).

CONCLUSIONS

The proposed motion-corrected T1ρ mapping framework enables a quantitative characterization of myocardial injuries with relatively low sensitivity to respiratory motion. This technique may be a robust and contrast-free adjunct to LGE for gaining new insight into myocardial structural disorders.

Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks

Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p< 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.

Role of free-breathing motion-corrected late gadolinium enhancement technique for image quality assessment and LGE quantification

OBJECTIVE

To compare the image quality and late gadolinium enhancement (LGE) quantification between free-breathing motion-corrected and conventional breath-hold LGE method in a variety of cardiovascular diseases.

MATERIALS AND METHODS

149 consecutive patients underwent contrast-enhanced cardiac magnetic resonance examination employing both free-breathing motion-corrected LGE and conventional breath-hold LGE method. Scan time, contrast-to-noise ratio, overall image quality score and LGE mass were measured and analyzed statistically.

RESULTS

Free-breathing motion-corrected LGE method had a shorter scan time and higher overall image quality score in comparison with conventional breath-hold LGE method (p < 0.001). Univariate/multivariate logistic regression analysis showed that breath-holding difficulty, high heart rate and arrhythmia could be predictive factors possibly for an inferior image quality score (p < 0.05 for all). The contrast-to-noise ratios of free-breathing motion-corrected LGE images were higher than those of conventional breath-hold LGE images (p < 0.001). In the cases with subepicardial and/or transmural myocardial enhancement, the measured LGE masses were larger on free-breathing motion-corrected LGE images in comparison with those on conventional breath-hold LGE images (p < 0.05).

CONCLUSION

Free-breathing motion-corrected LGE could be a better choice for patients who need contrast-enhanced cardiac MRI and have one or more of the risk factors for an inferior image quality score, including breath-holding difficulty, high heart rate and arrhythmia. However, an overestimation of LGE mass on free-breathing motion-corrected LGE image should be taken into consideration when LGE pattern involves subepicardial and/or transmural myocardium.

Emidec: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI

One crucial parameter to evaluate the state of the heart after myocardial infarction (MI) is the viability of the myocardial segment, i.e., if the segment recovers its functionality upon revascularization. MRI performed several minutes after the injection of a contrast agent (delayed enhancement-MRI or DE-MRI) is a method of choice to evaluate the extent of MI, and by extension, to assess viable tissues after an injury. The Emidec dataset is composed of a series of exams with DE-MR images in short axis orientation covering the left ventricle from normal cases or patients with myocardial infarction, with the contouring of the myocardium and diseased areas (if present) from experts in the domains. Moreover, classical available clinical parameters when the patient is managed by an emergency department are provided for each case. To the best of our knowledge, the Emidec dataset is the first one where annotated DE-MRI are combined with clinical characteristics of the patient, allowing the development of methodologies for exam classification as for exam quantification.

Standardized image interpretation and post-processing in cardiovascular magnetic resonance - 2020 update : Society for Cardiovascular Magnetic Resonance (SCMR): Board of Trustees Task Force on Standardized Post-Processing

With mounting data on its accuracy and prognostic value, cardiovascular magnetic resonance (CMR) is becoming an increasingly important diagnostic tool with growing utility in clinical routine. Given its versatility and wide range of quantitative parameters, however, agreement on specific standards for the interpretation and post-processing of CMR studies is required to ensure consistent quality and reproducibility of CMR reports. This document addresses this need by providing consensus recommendations developed by the Task Force for Post-Processing of the Society for Cardiovascular Magnetic Resonance (SCMR). The aim of the Task Force is to recommend requirements and standards for image interpretation and post-processing enabling qualitative and quantitative evaluation of CMR images. Furthermore, pitfalls of CMR image analysis are discussed where appropriate. It is an update of the original recommendations published 2013.

Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study

Background Cardiac MRI late gadolinium enhancement (LGE) scar volume is an important marker for outcome prediction in patients with hypertrophic cardiomyopathy (HCM); however, its clinical application is hindered by a lack of measurement standardization. Purpose To develop and evaluate a three-dimensional (3D) convolutional neural network (CNN)-based method for automated LGE scar quantification in patients with HCM. Materials and Methods We retrospectively identified LGE MRI data in a multicenter (n = 7) and multivendor (n = 3) HCM study obtained between November 2001 and November 2011. A deep 3D CNN based on U-Net architecture was used for LGE scar quantification. Independent CNN training and testing data sets were maintained with a 4:1 ratio. Stacks of short-axis MRI slices were split into overlapping substacks that were segmented and then merged into one volume. The 3D CNN per-site and per-vendor performances were evaluated with respect to manual scar quantification performed in a core laboratory setting using Dice similarity coefficient (DSC), Pearson correlation, and Bland-Altman analyses. Furthermore, the performance of 3D CNN was compared with that of two-dimensional (2D) CNN. Results This study included 1073 patients with HCM (733 men; mean age, 49 years ± 17 [standard deviation]). The 3D CNN-based quantification was fast (0.15 second per image) and demonstrated excellent correlation with manual scar volume quantification (r = 0.88, P < .001) and ratio of scar volume to total left ventricle myocardial volume (%LGE) (r = 0.91, P < .001). The 3D CNN-based quantification strongly correlated with manual quantification of scar volume (r = 0.82-0.99, P < .001) and %LGE (r = 0.90-0.97, P < .001) for all sites and vendors. The 3D CNN identified patients with a large scar burden (>15%) with 98% accuracy (202 of 207) (95% confidence interval [CI]: 95%, 99%). When compared with 3D CNN, 2D CNN underestimated scar volume (r = 0.85, P < .001) and %LGE (r = 0.83, P < .001). The DSC of 3D CNN segmentation was comparable among different vendors (P = .07) and higher than that of 2D CNN (DSC, 0.54 ± 0.26 vs 0.48 ± 0.29; P = .02). Conclusion In the hypertrophic cardiomyopathy population, a three-dimensional convolutional neural network enables fast and accurate quantification of myocardial scar volume, outperforms a two-dimensional convolutional neural network, and demonstrates comparable performance across different vendors. © RSNA, 2019 Online supplemental material is available for this article.

The Role of Cardiac MRI in the Management of Ventricular Arrhythmias in Ischaemic and Non-ischaemic Dilated Cardiomyopathy

Ventricular tachycardia (VT) and VF account for the majority of sudden cardiac deaths worldwide. Treatments for VT/VF include anti-arrhythmic drugs, ICDs and catheter ablation, but these treatments vary in effectiveness and carry substantial risks and/or expense. Current methods of selecting patients for ICD implantation are imprecise and fail to identify some at-risk patients, while leading to others being overtreated. In this article, the authors discuss the current role and future direction of cardiac MRI (CMRI) in refining diagnosis and personalising ventricular arrhythmia management. The capability of CMRI with gadolinium contrast delayed-enhancement patterns and, more recently, T1 mapping to determine the aetiology of patients presenting with heart failure is well established. Although CMRI imaging in patients with ICDs can be challenging, recent technical developments have started to overcome this. CMRI can contribute to risk stratification, with precise and reproducible assessment of ejection fraction, quantification of scar and ‘border zone’ volumes, and other indices. Detailed tissue characterisation has begun to enable creation of personalised computer models to predict an individual patient’s arrhythmia risk. When patients require VT ablation, a substrate-based approach is frequently employed as haemodynamic instability may limit electrophysiological activation mapping. Beyond accurate localisation of substrate, CMRI could be used to predict the location of re-entrant circuits within the scar to guide ablation.

Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia

In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.

Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images

OBJECTIVE

The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images.

METHODS

A successful FCNN in the literature (the ENet) was modified and trained to provide scar-tissue segmentation. Two segmentation protocols (Protocol 1 and Protocol 2) were investigated, the latter limiting the scar-segmentation search area to the left ventricular myocardial tissue region. CMR-LGE from 30 patients with ischemic-heart disease were retrospectively analyzed, for a total of 250 images, presenting high variability in terms of scar dimension and location. Segmentation results were assessed against manual scar-tissue tracing using one-patient-out cross validation.

RESULTS

Protocol 2 outperformed Protocol 1 significantly (p value < 0.05), with median sensitivity and Dice similarity coefficient equal to 88.07% [inter-quartile range (IQR) 18.84%] and 71.25% (IQR 31.82%), respectively.

DISCUSSION

Both segmentation protocols were able to detect scar tissues in the CMR-LGE images but higher performance was achieved when limiting the search area to the myocardial region. The findings of this paper represent an encouraging starting point for the use of FCNNs for the segmentation of nonviable scar tissue from CMR-LGE images.

A systematic comparison of cardiovascular magnetic resonance and high resolution histological fibrosis quantification in a chronic porcine infarct model

The noninvasive reference standard for myocardial fibrosis detection on cardiovascular magnetic resonance imaging (CMR) is late gadolinium enhancement (LGE). Currently there is no consensus on the preferred method for LGE quantification. Moreover myocardial wall thickening (WT) and strain are measures of regional deformation and function. The aim of this research was to systematically compare in vivo CMR parameters, such as LGE, WT and strain, with histological fibrosis quantification. Eight weeks after 90 min ischemia/reperfusion of the LAD artery, 16 pigs underwent in vivo Cine and LGE CMR. Histological sections from transverse heart slices were digitally analysed for fibrosis quantification. Mean fibrosis percentage of analysed sections was related to the different CMR techniques (using segmentation or feature tracking software) for each slice using a linear mixed model analysis. The full width at half maximum (FWHM) technique for quantification of LGE yielded the highest R2 of 60%. Cine derived myocardial WT explained 16-36% of the histological myocardial fibrosis. The peak circumferential and radial strain measured by feature tracking could explain 15 and 10% of the variance of myocardial fibrosis, respectively. The used method to systematically compare CMR image data with digital histological images is novel and feasible. Myocardial WT and strain were only modestly related with the amount of fibrosis. The fully automatic FWHM analysis technique is the preferred method to detect myocardial fibrosis.

Cardiac MR Imaging in Acute Coronary Syndrome: Application and Image Interpretation

Acute coronary syndrome (ACS) is a frequent cause of hospitalization and coronary interventions. Cardiac magnetic resonance (MR) imaging is an increasingly used technique for initial work-up of chest pain and early post-reperfusion and follow-up evaluation of ACS to identify patients at high risk of further cardiac events. Cardiac MR imaging can evaluate with accuracy a variety of prognostic indicators of myocardial damage, including regional myocardial dysfunction, infarct distribution, infarct size, myocardium at risk, microvascular obstruction, and intramyocardial hemorrhage in both acute setting and later follow-up examinations. In addition, MR imaging is useful to rule out other causes of acute chest pain in patients admitted to the emergency department. In this article, a brief explanation of the pathophysiology, classification, and treatment options for patients with ACS will be introduced. Indications of cardiac MR imaging in ACS patients will be reviewed and specific cardiac MR protocol, image interpretation, and potential diagnostic pitfalls will be discussed. ^© RSNA, 2017 Online supplemental material is available for this article.

Early Gadolinium Enhancement for Determination of Area at Risk: A Preclinical Validation Study

OBJECTIVES

The aim of this study was to determine whether early gadolinium enhancement (EGE) by cardiac magnetic resonance (CMR) in a canine model of reperfused myocardial infarction depicts the area at risk (AAR) as determined by microsphere blood flow analysis.

BACKGROUND

It remains controversial whether only the irreversibly injured myocardium enhances when CMR is performed in the setting of acute myocardial infarction. Recently, EGE has been proposed as a measure of the AAR in acute myocardial infarction because it correlates well with T2-weighted imaging of the AAR, but this still requires pathological validation.

METHODS

Eleven dogs underwent 2 h of coronary artery occlusion and 48 h of reperfusion before imaging at 1.5-T. EGE imaging was performed 3 min after contrast administration with coverage of the entire left ventricle. Late gadolinium enhancement imaging was performed between 10 and 15 min after contrast injection. AAR was defined as myocardium with blood flow <2 SD from remote myocardium determined by microspheres during occlusion. The size of infarction was determined with triphenyltetrazolium chloride.

RESULTS

There was no significant difference in the size of enhancement by EGE compared with the size of AAR by microspheres (44.1 ± 15.8% vs. 42.7 ± 9.2%; p = 0.61), with good correlation (r = 0.88; p < 0.001) and good agreement by Bland-Altman analysis (mean bias 1.4 ± 17.4%). There was no difference in the size of enhancement by EGE compared with enhancement on native T1 and T2 maps. The size of EGE was significantly greater than the infarct by triphenyltetrazolium chloride (44.1 ± 15.8% vs. 20.7 ± 14.4%; p < 0.001) and late gadolinium enhancement (44.1 ± 15.8% vs. 23.5 ± 12.7%; p < 0.001).

CONCLUSIONS

At 3 min post-contrast, EGE correlated well with the AAR by microspheres and CMR and was greater than infarct size. Thus, EGE enhances both reversibly and irreversibly injured myocardium.

Scar characteristics derived from two- and three-dimensional reconstructions of cardiac contrast-enhanced magnetic resonance images: Relationship to ventricular tachycardia inducibility and ablation success

Background

The relationship between cardiac contrast-enhanced magnetic resonance imaging (CE-MRI)-derived scar characteristics and substrate for ventricular tachycardia (VT) in patients with structural heart disease (SHD) has not been fully investigated.

Methods

This study included 51 patients (mean age, 63.3±15.1 years) who underwent CE-MRI with SHD and VT induction testing before ablation. Late gadolinium-enhanced (LGE) regions on MRI slices were quantified by thresholding techniques. Signal intensities (SIs) 2–6 SDs above the mean SI of the remote left ventricular (LV) myocardium were considered as scar border zones, and SI>6 SDs, as scar zone, and the scar characteristics related to VT inducibility and successful ablation via endocardial approaches were evaluated.

Results

The proportion of the total CE-MRI-derived scar border zone in the inducible VT group was significantly greater than that in the non-inducible VT group (26.3±9.9% vs. 19.2±7.8%, respectively, P=0.0323). The LV endocardial scar zone to total LV myocardial scar zone ratio in patients whose ablation was successful was significantly greater than that in those whose ablation was unsuccessful (0.61±0.11 vs. 0.48±0.12, respectively, P=0.0042). Most successful ablation sites were located adjacent to CE-MRI-derived scar border zones.

Conclusions

By CE-MRI, we were able to characterize not only the scar, but also its location and heterogeneity, and those features seemed to be related to VT inducibility and successful ablation from an endocardial site.

Effect of Omega-3 Acid Ethyl Esters on Left Ventricular Remodeling After Acute Myocardial Infarction: The OMEGA-REMODEL Randomized Clinical Trial

BACKGROUND

Omega-3 fatty acids from fish oil have been associated with beneficial cardiovascular effects, but their role in modifying cardiac structures and tissue characteristics in patients who have had an acute myocardial infarction while receiving current guideline-based therapy remains unknown.

METHODS

In a multicenter, double-blind, placebo-controlled trial, participants presenting with an acute myocardial infarction were randomly assigned 1:1 to 6 months of high-dose omega-3 fatty acids (n=180) or placebo (n=178). Cardiac magnetic resonance imaging was used to assess cardiac structure and tissue characteristics at baseline and after study therapy. The primary study endpoint was change in left ventricular systolic volume index. Secondary endpoints included change in noninfarct myocardial fibrosis, left ventricular ejection fraction, and infarct size.

RESULTS

By intention-to-treat analysis, patients randomly assigned to omega-3 fatty acids experienced a significant reduction of left ventricular systolic volume index (-5.8%, P=0.017), and noninfarct myocardial fibrosis (-5.6%, P=0.026) in comparison with placebo. Per-protocol analysis revealed that those patients who achieved the highest quartile increase in red blood cell omega-3 index experienced a 13% reduction in left ventricular systolic volume index in comparison with the lowest quartile. In addition, patients in the omega-3 fatty acid arm underwent significant reductions in serum biomarkers of systemic and vascular inflammation and myocardial fibrosis. There were no adverse events associated with high-dose omega-3 fatty acid therapy.

CONCLUSIONS

Treatment of patients with acute myocardial infarction with high-dose omega-3 fatty acids was associated with reduction of adverse left ventricular remodeling, noninfarct myocardial fibrosis, and serum biomarkers of systemic inflammation beyond current guideline-based standard of care.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00729430.

Impact of microvascular obstruction on semiautomated techniques for quantifying acute and chronic myocardial infarction by cardiovascular magnetic resonance

Aims

The four most promising semiautomated techniques (5-SD, 6-SD, Otsu and the full width half maximum (FWHM)) were compared in paired acute and follow-up cardiovascular magnetic resonance (CMR), taking into account the impact of microvascular obstruction (MVO) and using automated extracellular volume fraction (ECV) maps for reference. Furthermore, their performances on the acute scan were compared against manual myocardial infarct (MI) size to predict adverse left ventricular (LV) remodelling (≥20% increase in end-diastolic volume).

Methods

40 patients with reperfused ST segment elevation myocardial infarction (STEMI) with a paired acute (4±2 days) and follow-up CMR scan (5±2 months) were recruited prospectively. All CMR analysis was performed on CVI42.

Results

Using manual MI size as the reference standard, 6-SD accurately quantified acute (24.9±14.0%LV, p=0.81, no bias) and chronic MI size (17.2±9.7%LV, p=0.88, no bias). The performance of FWHM for acute MI size was affected by the acquisition sequence used. Furthermore, FWHM underestimated chronic MI size in those with previous MVO due to the significantly higher ECV in the MI core on the follow-up scans previously occupied by MVO (82 (75–88)% vs 62 (51–68)%, p<0.001). 5-SD and Otsu were precise but overestimated acute and chronic MI size. All techniques were performed with high diagnostic accuracy and equally well to predict adverse LV remodelling.

Conclusions

6-SD was the most accurate for acute and chronic MI size and should be the preferred semiautomatic technique in randomised controlled trials. However, 5-SD, FWHM and Otsu could also be used when precise MI size quantification may be adequate (eg, observational studies).

A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data

BACKGROUND

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data.

METHODS

The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images).

RESULTS

Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in PSIR images (n = 49).

CONCLUSIONS

The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images.

CLINICAL TRIAL REGISTRATION

CHILL-MI: NCT01379261 .

MITOCARE

NCT01374321 .

Myocardial infarct sizing by late gadolinium-enhanced MRI: Comparison of manual, full-width at half-maximum, and n-standard deviation methods

PURPOSE

To compare three widely used methods for myocardial infarct (MI) sizing on late gadolinium-enhanced (LGE) magnetic resonance (MR) images: manual delineation and two semiautomated techniques (full-width at half-maximum [FWHM] and n-standard deviation [SD]).

MATERIALS AND METHODS

3T phase-sensitive inversion-recovery (PSIR) LGE images of 114 patients after an acute MI (2-4 days and 6 months) were analyzed by two independent observers to determine both total and core infarct sizes (TIS/CIS). Manual delineation served as the reference for determination of optimal thresholds for semiautomated methods after thresholding at multiple values. Reproducibility and accuracy were expressed as overall bias ± 95% limits of agreement.

RESULTS

Mean infarct sizes by manual methods were 39.0%/24.4% for the acute MI group (TIS/CIS) and 29.7%/17.3% for the chronic MI group. The optimal thresholds (ie, providing the closest mean value to the manual method) were FWHM30% and 3SD for the TIS measurement and FWHM45% and 6SD for the CIS measurement (paired t-test; all P > 0.05). The best reproducibility was obtained using FWHM. For TIS measurement in the acute MI group, intra-/interobserver agreements, from Bland-Altman analysis, with FWHM30%, 3SD, and manual were -0.02 ± 7.74%/-0.74 ± 5.52%, 0.31 ± 9.78%/2.96 ± 16.62% and -2.12 ± 8.86%/0.18 ± 16.12, respectively; in the chronic MI group, the corresponding values were 0.23 ± 3.5%/-2.28 ± 15.06, -0.29 ± 10.46%/3.12 ± 13.06% and 1.68 ± 6.52%/-2.88 ± 9.62%, respectively. A similar trend for reproducibility was obtained for CIS measurement. However, semiautomated methods produced inconsistent results (variabilities of 24-46%) compared to manual delineation.

CONCLUSION

The FWHM technique was the most reproducible method for infarct sizing both in acute and chronic MI. However, both FWHM and n-SD methods showed limited accuracy compared to manual delineation. J. Magn. Reson. Imaging 2016;44:1206-1217.

Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT

BACKGROUND

Efficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI) by determining the size of myocardium at risk (MaR) and myocardial infarction (MI), (MSI = 1-MI/MaR). Cardiovascular magnetic resonance (CMR) can be used to assess MI by late gadolinium enhancement (LGE) and MaR by either T2-weighted imaging or contrast enhanced SSFP (CE-SSFP). Automatic segmentation algorithms have been developed and validated for MI by LGE as well as for MaR by T2-weighted imaging. There are, however, no algorithms available for CE-SSFP. Therefore, the aim of this study was to develop and validate automatic segmentation of MaR in CE-SSFP.

METHODS

The automatic algorithm applies surface coil intensity correction and classifies myocardial intensities by Expectation Maximization to define a MaR region based on a priori regional criteria, and infarct region from LGE. Automatic segmentation was validated against manual delineation by expert readers in 183 patients with reperfused acute MI from two multi-center randomized clinical trials (RCT) (CHILL-MI and MITOCARE) and against myocardial perfusion SPECT in an additional set (n = 16). Endocardial and epicardial borders were manually delineated at end-diastole and end-systole. Manual delineation of MaR was used as reference and inter-observer variability was assessed for both manual delineation and automatic segmentation of MaR in a subset of patients (n = 15). MaR was expressed as percent of left ventricular mass (%LVM) and analyzed by bias (mean ± standard deviation). Regional agreement was analyzed by Dice Similarity Coefficient (DSC) (mean ± standard deviation).

RESULTS

MaR assessed by manual and automatic segmentation were 36 ± 10% and 37 ± 11%LVM respectively with bias 1 ± 6%LVM and regional agreement DSC 0.85 ± 0.08 (n = 183). MaR assessed by SPECT and CE-SSFP automatic segmentation were 27 ± 10%LVM and 29 ± 7%LVM respectively with bias 2 ± 7%LVM. Inter-observer variability was 0 ± 3%LVM for manual delineation and -1 ± 2%LVM for automatic segmentation.

CONCLUSIONS

Automatic segmentation of MaR in CE-SSFP was validated against manual delineation in multi-center, multi-vendor studies with low bias and high regional agreement. Bias and variability was similar to inter-observer variability of manual delineation and inter-observer variability was decreased by automatic segmentation. Thus, the proposed automatic segmentation can be used to reduce subjectivity in quantification of MaR in RCT.

CLINICAL TRIAL REGISTRATION

NCT01379261. NCT01374321.

Midlife Cardiovascular Risk Factors and Late-Life Unrecognized and Recognized Myocardial Infarction Detect by Cardiac Magnetic Resonance: ICELAND-MI, the AGES-Reykjavik Study

Background

Associations of atherosclerosis risk factors with unrecognized myocardial infarction (UMI) are unclear. We investigated associations of midlife risk factors with UMI and recognized MI (RMI) detected 31 years later by cardiac magnetic resonance.

Methods and Results

The Reykjavik Study (1967–1991) collected serial risk factors in subjects, mean (SD) age 48 (7) years. In ICELAND‐MI (2004–2007), 936 survivors (76 (5) years) were evaluated by cardiac magnetic resonance. Analysis included logistic regression and random effects modeling. Comparisons are relative to subjects without MI. At baseline midlife evaluation, a modified Framingham risk score was significantly higher in RMI and in UMI versus no MI (7.4 (6.3)%; 7.1 (6.2)% versus 5.4 (5.8)%, P<0.001). RMI and UMI were more frequent in men (65%, 64% versus 43%; P<0.0001). Baseline systolic and diastolic blood pressure were significantly higher in UMI (138 (17) mm Hg versus 133 (17) mm Hg; P<0.006; 87 (10) mm Hg versus 84 (10) mm Hg; P<0.02). Diastolic BP was significantly higher in RMI (88 (10) mm Hg versus 84 (10) mm Hg; P<0.02). Cholesterol and triglycerides were significantly higher in RMI (6.7 (1.1) mmol/L versus 6.2 (1.1) mmol/L; P=0.0005; and 1.4 (0.7) mmol/L versus 1.1 (0.7) mmol/L; P<0.003). Cholesterol trended higher in UMI (P=0.08). Serial midlife systolic BP was significantly higher in UMI versus no MI (β [SE] = 2.69 [1.28] mm Hg, P=0.04). Serial systolic and diastolic BP were significantly higher in RMI versus no MI (4.12 [1.60] mm Hg, P=0.01 and 2.05 [0.91] mm Hg, P=0.03) as were cholesterol (0.43 [0.11] mmol/L, P=0.0001) and triglycerides (0.3 [0.06] mmol/L, P<0.0001).

Conclusions

Midlife vascular risk factors are associated with UMI and RMI detected by cardiac magnetic resonance 31 years later. Systolic blood pressure was the most significant modifiable risk factor associated with later UMI.

Quantitative assessment of myocardial fibrosis in an age-related rat model by ex vivo late gadolinium enhancement magnetic resonance imaging with histopathological correlation

Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) imaging can detect the presence of myocardial infarction from ischemic cardiomyopathies (ICM). However, it is more challenging to detect diffuse myocardial fibrosis from non-ischemic cardiomyopathy (NICM) with this technique due to more subtle and heterogeneous enhancement of the myocardium. This study investigates whether high-resolution LGE CMR can detect age-related myocardial fibrosis using quantitative texture analysis with histological validation. LGE CMR of twenty-four rat hearts (twelve 6-week-old and twelve 2-year-old) was performed using a 7T MRI scanner. Picrosirius red was used as the histopathology reference for collagen staining. Fibrosis in the myocardium was quantified with standard deviation (SD) threshold methods from the LGE CMR images and 3D contrast texture maps that were computed from gray level co-occurrence matrix of the CMR images. There was a significant increase of collagen fibers in the aged compared to the young rat histology slices (2.60±0.27 %LV vs. 1.24±0.29 %LV, p<0.01). Both LGE CMR and texture images showed a significant increase of myocardial fibrosis in the elderly compared to the young rats. Fibrosis in the LGE CMR images correlated strongly with histology with the 3 SD threshold (r=0.84, y=0.99x+0.00). Similarly, fibrosis in the contrast texture maps correlated with the histology using the 4 SD threshold (r=0.89, y=1.01x+0.00). High resolution ex-vivo LGE CMR can detect the presence of diffuse fibrosis that naturally developed in elderly rat hearts. Our results suggest that texture analysis may improve the assessment of myocardial fibrosis in LGE CMR images.

Time elapsed after contrast injection is crucial to determine infarct transmurality and myocardial functional recovery after an acute myocardial infarction

BACKGROUND

In acute myocardial infarction (MI), late Gadolinium enhancement (LGE) has been proposed to include the infarcted myocardium and area at risk. However, little information is available on the optimal timing after contrast injection to differentiate these 2 areas. Our aim was to determine in acute and chronic MI whether imaging time after contrast injection influences the LGE size that better predicts infarct size and functional recovery.

METHODS

Subjects were evaluated by cardiovascular magnetic resonance (CMR) the first week (n = 60) and 3 months (n = 47) after a percutaneously revascularized STEMI. Inversion-recovery single-shot (ss-IR) imaging was acquired at multiple time points following contrast administration and compared to segmented inversion-recovery (seg-IR) sequences. Inversion time was properly adjusted and images were blinded, randomized and measured for LGE volumes.

RESULTS

In acute MI, LGE volume decreased over several minutes (p = 0.005) with the greatest volume occurring at 3 minutes and the smallest at 25 minutes post-contrast injection; however, LGE volume remained constant over time in chronic MI (p = 0.886). Depending on the imaging time, in acute phase, a change in the transmurality index was also observed. A transmural infarction (>75%) at 25 minutes better predicted the absence of improvement in the wall motion score index (WMSI), a higher increase in left ventricular volumes and a lower ejection fraction compared to 10 minutes.

CONCLUSIONS

A change was observed in LGE volume in the minutes following contrast administration in acute but not in chronic MI. Infarct transmurality 25 minutes post-contrast injection better predicted infarct size and functional recovery at follow-up.

Mechanisms for overestimating acute myocardial infarct size with gadolinium-enhanced cardiovascular magnetic resonance imaging in humans: a quantitative and kinetic study

Aims

It remains controversial whether cardiovascular magnetic resonance imaging with gadolinium only enhances acutely infarcted or also salvaged myocardium. We hypothesized that enhancement of salvaged myocardium may be due to altered extracellular volume (ECV) and contrast kinetics compared with normal and infarcted myocardium. If so, these mechanisms could contribute to overestimation of acute myocardial infarction (AMI) size.

Methods and results

Imaging was performed at 1.5T ≤ 7 days after AMI with serial T₁ mapping and volumetric early (5 min post-contrast) and late (20 min post-contrast) gadolinium enhancement imaging. Infarcts were classified as transmural (>75% transmural extent) or non-transmural. Patients with non-transmural infarctions (n = 15) had shorter duration of symptoms before reperfusion (P = 0.02), lower peak troponin (P = 0.008), and less microvascular obstruction (P < 0.001) than patients with transmural infarcts (n = 22). The size of enhancement at 5 min was greater than at 20 min (18.7 ± 12.7 vs. 12.1 ± 7.0%, P = 0.003) in non-transmural infarctions, but similar in transmural infarctions (23.0 ± 10.0 vs. 21.9 ± 9.9%, P = 0.21). ECV of salvaged myocardium was greater than normal (39.5 ± 5.8 vs. 24.1 ± 3.1%) but less than infarcted myocardium (50.5 ± 6.0%, both P < 0.001). In kinetic studies of non-transmural infarctions, salvaged and infarcted myocardium had similar T₁ at 4 min but different T₁ at 8–20 min post-contrast.

Conclusion

The extent of gadolinium enhancement in AMI is modulated by ECV and contrast kinetics. Image acquisition too early after contrast administration resulted in overestimation of infarct size in non-transmural infarctions due to enhancement of salvaged myocardium.

Quantification of infarct size and myocardium at risk: evaluation of different techniques and its implications

AIMS

The aim of this study was to evaluate seven methods for quantifying myocardial oedema [2 standard deviation (SD), 3 SD, 5 SD, full width at half maximum (FWHM), Otsu method, manual thresholding, and manual contouring] from T2-weighted short tau inversion recovery (T2w STIR) and also to reassess these same seven methods for quantifying acute infarct size following ST-segment myocardial infarction (STEMI). This study focuses on test-retest repeatability while assessing inter- and intraobserver variability. T2w STIR and late gadolinium enhancement (LGE) are the most widely used cardiovascular magnetic resonance (CMR) techniques to image oedema and infarction, respectively. However, no consensus exists on the best quantification method to be used to analyse these images. This has potential important implications in the research setting where both myocardial oedema and infarct size are increasingly used and measured as surrogate endpoints in clinical trials.

METHODS AND RESULTS

Forty patients day 2 following acute reperfused STEMI were scanned for myocardial oedema and infarction (LGE). All patients had a second CMR scan on the same day >6 h apart from the first one. Images were analysed offline by two independent observers using the semi-automated software. Both oedema and LGE were quantified using seven techniques (2 SD, 3 SD, 5 SD, Otsu, FWHM, manual threshold, and manual contouring). Interobserver, intraobserver and test-retest agreement and variability for both infarct size and oedema quantification were assessed. Infarct size and myocardial quantification vary depending on the quantification method used. Overall, manual contouring provided the lowest inter-, intraobserver, and interscan variability for both infarct size and oedema quantification. The FWHM method for infarct size quantification and the Otsu method for myocardial oedema quantification are acceptable alternatives.

CONCLUSIONS

This study determines that, in acute myocardial infarction (MI), manual contouring has the lowest overall variability for quantification of both myocardial oedema and MI when analysed by experienced observers.

Comparison of semi-automated methods to quantify infarct size and area at risk by cardiovascular magnetic resonance imaging at 1.5T and 3.0T field strengths

BACKGROUND

There is currently no gold standard technique for quantifying infarct size (IS) and ischaemic area-at-risk (AAR [oedema]) on late gadolinium enhancement imaging (LGE) and T2-weighted short tau inversion recovery imaging (T2w-STIR) respectively. This study aimed to compare the accuracy and reproducibility of IS and AAR quantification on LGE and T2w-STIR imaging using Otsu's Automated Technique (OAT) with currently used methods at 1.5T and 3.0T post acute ST-segment elevation myocardial infarction (STEMI).

METHODS

Ten patients were assessed at 1.5T and 10 at 3.0T. IS was assessed on LGE using 5-8 standard-deviation thresholding (5-8SD), full-width half-maximum (FWHM) quantification and OAT. AAR was assessed on T2w-STIR using 2SD and OAT. Accuracy was assessed by comparison with manual quantification. Interobserver and intraobserver variabilities were assessed using Intraclass Correlation Coefficients and Bland-Altman analysis. IS using each technique was correlated with left ventricular ejection fraction (LVEF).

RESULTS

FWHM and 8SD-derived IS closely correlated with manual assessment at both field strengths (1.5T: 18.3 ± 10.7% LV Mass [LVM] with FWHM, 17.7 ± 14.4% LVM with 8SD, 16.5 ± 10.3% LVM with manual quantification; 3.0T: 10.8 ± 8.2% LVM with FWHM, 11.4 ± 9.0% LVM with 8SD, 11.5 ± 9.0% LVM with manual quantification). 5SD and OAT overestimated IS at both field strengths. OAT, 2SD and manually quantified AAR closely correlated at 1.5T, but OAT overestimated AAR compared with manual assessment at 3.0T. IS and AAR derived by FWHM and OAT respectively had better reproducibility compared with manual and SD-based quantification. FWHM IS correlated strongest with LVEF.

CONCLUSIONS

FWHM quantification of IS is accurate, reproducible and correlates strongly with LVEF, whereas 5SD and OAT overestimate IS. OAT accurately assesses AAR at 1.5T and with excellent reproducibility. OAT overestimated AAR at 3.0T and thus cannot be recommended as the preferred method for AAR quantification at 3.0T.

Endogenous assessment of chronic myocardial infarction with T(1ρ)-mapping in patients

BACKGROUND

Detection of cardiac fibrosis based on endogenous magnetic resonance (MR) characteristics of the myocardium would yield a measurement that can provide quantitative information, is independent of contrast agent concentration, renal function and timing. In ex vivo myocardial infarction (MI) tissue, it has been shown that a significantly higher T(1ρ) is found in the MI region, and studies in animal models of chronic MI showed the first in vivo evidence for the ability to detect myocardial fibrosis with native T(1ρ)-mapping. In this study we aimed to translate and validate T(1ρ)-mapping for endogenous detection of chronic MI in patients.

METHODS

We first performed a study in a porcine animal model of chronic MI to validate the implementation of T(1ρ)-mapping on a clinical cardiovascular MR scanner and studied the correlation with histology. Subsequently a clinical protocol was developed, to assess the feasibility of scar tissue detection with native T(1ρ)-mapping in patients (n = 21) with chronic MI, and correlated with gold standard late gadolinium enhancement (LGE) CMR. Four T1ρ-weighted images were acquired using a spin-lock preparation pulse with varying duration (0, 13, 27, 45 ms) and an amplitude of 750 Hz, and a T(1ρ)-map was calculated. The resulting T(1ρ)-maps and LGE images were scored qualitatively for the presence and extent of myocardial scarring using the 17-segment AHA model.

RESULTS

In the animal model (n = 9) a significantly higher T(1ρ) relaxation time was found in the infarct region (61 ± 11 ms), compared to healthy remote myocardium (36 ± 4 ms) . In patients a higher T(1ρ) relaxation time (79 ± 11 ms) was found in the infarct region than in remote myocardium (54 ± 6 ms). Overlap in the scoring of scar tissue on LGE images and T(1ρ)-maps was 74%.

CONCLUSION

We have shown the feasibility of native T(1ρ)-mapping for detection of infarct area in patients with a chronic myocardial infarction. In the near future, improvements on the T(1ρ)-mapping sequence could provide a higher sensitivity and specificity. This endogenous method could be an alternative for LGE imaging, and provide additional quantitative information on myocardial tissue characteristics.

Differences in quantitative assessment of myocardial scar and gray zone by LGE-CMR imaging using established gray zone protocols

Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging is the gold standard for myocardial scar evaluation. Heterogeneous areas of scar ('gray zone'), may serve as arrhythmogenic substrate. Various gray zone protocols have been correlated to clinical outcomes and ventricular tachycardia channels. This study assessed the quantitative differences in gray zone and scar core sizes as defined by previously validated signal intensity (SI) threshold algorithms. High quality LGE-CMR images performed in 41 cardiomyopathy patients [ischemic (33) or non-ischemic (8)] were analyzed using previously validated SI threshold methods [Full Width at Half Maximum (FWHM), n-standard deviation (NSD) and modified-FWHM]. Myocardial scar was defined as scar core and gray zone using SI thresholds based on these methods. Scar core, gray zone and total scar sizes were then computed and compared among these models. The median gray zone mass was 2-3 times larger with FWHM (15 g, IQR: 8-26 g) compared to NSD or modified-FWHM (5 g, IQR: 3-9 g; and 8 g. IQR: 6-12 g respectively, p < 0.001). Conversely, infarct core mass was 2.3 times larger with NSD (30 g, IQR: 17-53 g) versus FWHM and modified-FWHM (13 g, IQR: 7-23 g, p < 0.001). The gray zone extent (percentage of total scar that was gray zone) also varied significantly among the three methods, 51 % (IQR: 42-61 %), 17 % (IQR: 11-21 %) versus 38 % (IQR: 33-43 %) for FWHM, NSD and modified-FWHM respectively (p < 0.001). Considerable variability exists among the current methods for MRI defined gray zone and scar core. Infarct core and total myocardial scar mass also differ using these methods. Further evaluation of the most accurate quantification method is needed.

Reproducibility of manual and semi-automated late enhancement quantification in patients with Fabry disease

BACKGROUND

Late enhancement (LE) imaging is increasingly used for diagnosis of non-ischemic cardiomyopathy. However, the mostly patchy appearance of LE in this context may reduce the reproducibility of LE measurement.

PURPOSE

To report intra- and inter-observer variabilities of LE measurements in Fabry disease using manual and semi-automated quantification.

MATERIAL AND METHODS

Twenty MRI data-sets of male patients aged 44 ± 7 years were analyzed twice (interval 12 months) by one observer and additionally once by a second observer. Left ventricular (LV) parameters were determined using cine MRI. Gradient-echo LE images were analyzed by manual planimetry and by a semi-automatic prototype software. Variabilities were determined by Bland-Altman analyses and additionally intra-class correlation coefficient (ICC) values were calculated to survey intra- and inter-observer reproducibility.

RESULTS

The amount of LE was 5.2 ± 5.1 mL or 2.8 ± 2.6 % of LV mass (observer 2). LE was detected predominantly intramurally in a patchy pattern. All patients had LE restricted to the basal infero-lateral parts of the LV. The extent of LE correlated to LV mass (207 ± 70 g, P < 0.05, r = 0.6). The intra- and inter-observer variabilities were -0.6 to 1.0 mL and -0.7 to 1.6 mL, respectively (95% confidence intervals). ICC values were 0.981-0.999. The semi-automatic software allowed quantification of LE areas in all patients. The comparison of LE amount determined by semi-automatic software versus manual planimetry yielded an intra-observer variability ranging from -1.9 to 2.3 mL.

CONCLUSION

Semi-automatic planimetry of patchy LE in patients with Fabry disease is feasible. The determined intra- and inter-observer variabilities for manual and semi-automatic planimetry were in the range of 20-40% of LE amount with high ICC values.

Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images

We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.

Contrast-enhancement cardiac magnetic resonance imaging beyond the scope of viability

The clinical applications of cardiovascular magnetic resonance imaging with contrast enhancement are expanding. Besides the direct visualisation of viable and non-viable myocardium, this technique is increasingly used in a variety of cardiac disorders to determine the exact aetiology, guide proper treatment, and predict outcome and prognosis. In this review, we discuss the value of cardiovascular magnetic resonance imaging with contrast enhancement in a range of cardiac disorders, in which this technique may provide insights beyond the scope of myocardial viability.

Guidelines and protocols for cardiovascular magnetic resonance in children and adults with congenital heart disease: SCMR expert consensus group on congenital heart disease

Cardiovascular magnetic resonance (CMR) has taken on an increasingly important role in the diagnostic evaluation and pre-procedural planning for patients with congenital heart disease. This article provides guidelines for the performance of CMR in children and adults with congenital heart disease. The first portion addresses preparation for the examination and safety issues, the second describes the primary techniques used in an examination, and the third provides disease-specific protocols. Variations in practice are highlighted and expert consensus recommendations are provided. Indications and appropriate use criteria for CMR examination are not specifically addressed.

Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) board of trustees task force on standardized post processing

With mounting data on its accuracy and prognostic value, cardiovascular magnetic resonance (CMR) is becoming an increasingly important diagnostic tool with growing utility in clinical routine. Given its versatility and wide range of quantitative parameters, however, agreement on specific standards for the interpretation and post-processing of CMR studies is required to ensure consistent quality and reproducibility of CMR reports. This document addresses this need by providing consensus recommendations developed by the Task Force for Post Processing of the Society for Cardiovascular MR (SCMR). The aim of the task force is to recommend requirements and standards for image interpretation and post processing enabling qualitative and quantitative evaluation of CMR images. Furthermore, pitfalls of CMR image analysis are discussed where appropriate.

Free-breathing, motion-corrected late gadolinium enhancement is robust and extends risk stratification to vulnerable patients

BACKGROUND

Routine clinical use of novel free-breathing, motion-corrected, averaged late-gadolinium-enhancement (moco-LGE) cardiovascular MR may have advantages over conventional breath-held LGE (bh-LGE), especially in vulnerable patients.

METHODS AND RESULTS

In 390 consecutive patients, we collected bh-LGE and moco-LGE with identical image matrix parameters. In 41 patients, bh-LGE was abandoned because of image quality issues, including 10 with myocardial infarction. When both were acquired, myocardial infarction detection was similar (McNemar test, P=0.4) with high agreement (κ=0.95). With artifact-free bh-LGE images, pixelwise myocardial infarction measures correlated highly (R(2)=0.96) without bias. Moco-LGE was faster, and image quality and diagnostic confidence were higher on blinded review (P<0.001 for all). During a median of 1.2 years, 20 heart failure hospitalizations and 18 deaths occurred. For bh-LGE, but not moco-LGE, inferior image quality and bh-LGE nonacquisition were linked to patient vulnerability confirmed by adverse outcomes (log-rank P<0.001). Moco-LGE significantly stratified risk in the full cohort (log-rank P<0.001), but bh-LGE did not (log-rank P=0.056) because a significant number of vulnerable patients did not receive bh-LGE (because of arrhythmia or inability to hold breath).

CONCLUSIONS

Myocardial infarction detection and quantification are similar between moco-LGE and bh-LGE when bh-LGE can be acquired well, but bh-LGE quality deteriorates with patient vulnerability. Acquisition time, image quality, diagnostic confidence, and the number of successfully scanned patients are superior with moco-LGE, which extends LGE-based risk stratification to include patients with vulnerability confirmed by outcomes. Moco-LGE may be suitable for routine clinical use.

Scar heterogeneity on cardiovascular magnetic resonance as a predictor of appropriate implantable cardioverter defibrillator therapy

BACKGROUND

Despite the survival benefit of implantable-cardioverter-defibrillators (ICDs), the vast majority of patients receiving an ICD for primary prevention do not receive ICD therapy. We sought to assess the role of heterogeneous scar area (HSA) identified by late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) in predicting appropriate ICD therapy for primary prevention of sudden cardiac death (SCD).

METHODS

From September 2003 to March 2011, all patients who underwent primary prevention ICD implantation and had a pre-implantation LGE-CMR were identified. Scar size was determined using thresholds of 4 and 6 standard deviations (SD) above remote normal myocardium; HSA was defined using 3 different criteria; as the region between 2 SD and 4 SD (HSA2-4SD), between 2SD and 6SD (HSA2-6SD), and between 4SD and 6SD (HSA4-6SD). The end-point was appropriate ICD therapy.

RESULTS

Out of 40 total patients followed for 25 ± 24 months, 7 had appropriate ICD therapy. Scar size measured by different thresholds was similar in ICD therapy and non-ICD therapy groups (P = NS for all). However, HSA2-4SD and HSA4-6SD were significantly larger in the ICD therapy group (P = 0.001 and P = 0.03, respectively). In multivariable model HSA2-4SD was the only significant independent predictor of ICD therapy (HR = 1.08, 95%CI: 1.00-1.16, P = 0.04). Kaplan-Meier analysis showed that patients with greater HSA2-4SD had a lower survival free of appropriate ICD therapy (P = 0.026).

CONCLUSIONS

In primary prevention ICD implantation, LGE-CMR HSA identifies patients with appropriate ICD therapy. If confirmed in larger series, HSA can be used for risk stratification in primary prevention of SCD.

Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

BACKGROUND

T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.

METHODS

We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.

RESULTS

The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.

CONCLUSION

Compared to standard manual approaches, the new highly automatic method for estimating myocardial oedema is accurate and straightforward. It has potential as a generic software tool for physicians to use in clinical practice.