Cardiac MRI: A General Overview with Emphasis on Current Use and Indications

Cardiac MRI: An Overview of Physical Principles With Highlights of Clinical Applications and Technological Advancements

The purpose of this review is to serve as a concise learning tool for clinicians interested in quickly learning more about cardiac magnetic resonance imaging (CMR) and its physical principles. There is heavy coverage of the basic physical fundamentals of CMR as well as updates on the history, clinical indications, cost-effectiveness, role of artificial intelligence in CMR, and examples of common late gadolinium enhancement (LGE) patterns. This literature review was performed by searching the PubMed database for the most up-to-date literature regarding these topics. Relevant, less up-to-date articles, covering the history and physics of CMR, were also obtained from the PubMed database. Clinical indications for CMR include adult congenital heart disease, cardiac ischemia, cardiomyopathies, and heart failure. CMR has a projected cost-benefit ratio of 0.58, leading to potential savings for patients. Despite its utility, CMR has some drawbacks including long image processing times, large space requirements for equipment, and patient discomfort during imaging. Artificial intelligence-based algorithms can address some of these drawbacks by decreasing image processing times and may have reliable diagnostic capabilities. CMR is quickly rising as a high-resolution, non-invasive cardiac imaging modality with an increasing number of clinical indications. Thanks to technological advancements, especially in artificial intelligence, the benefits of CMR often outweigh its drawbacks.

A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images

Aims

Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images.

Methods and results

Fifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic (n = 15, 30%) or non-ischemic (n = 35, 70%) LVD. Delayed enhancement regions were manually traced on late CE-CCT using CE-CMR as reference. On early CE-CCT images, the myocardial sectors were extracted according to AHA 16-segment model and labeled as with scar or not, based on the late CE-CCT manual tracing. A DL model was developed to classify each segment. A total of 44,187 LV segments were analyzed, resulting in accuracy of 71% and area under the ROC curve of 76% (95% CI: 72%-81%), while, with the bull's eye segmental comparison of CE-CMR and respective early CE-CCT findings, an 89% agreement was achieved.

Conclusions

DL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time.

Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing

Ascending aortic aneurysm is a pathology that is important to be supervised and treated. During the years the aorta dilates, it becomes stiff, and its elastic properties decrease. In some cases, the aortic wall can rupture leading to aortic dissection with a high mortality rate. The main reference standard to measure when the patient needs to undertake surgery is the aortic diameter. However, the aortic diameter was shown not to be sufficient to predict aortic dissection, implying other characteristics should be considered. Therefore, the main objective of this work is to assess in-vivo the elastic properties of four different quadrants of the ascending aorta and compare the results with equivalent properties obtained ex-vivo. The database consists of 73 cine-MRI sequences of thoracic aorta acquired in axial orientation at the level of the pulmonary trunk. All the patients have dilated aorta and surgery is required. The exams were acquired just prior to surgery, each consisting of 30 slices on average across the cardiac cycle. Multiple deep learning architectures have been explored with different hyperparameters and settings to automatically segment the contour of the aorta on each image and then automatically calculate the aortic compliance. A semantic segmentation U-Net network outperforms the rest explored networks with a Dice score of 98.09% (±0.96%) and a Hausdorff distance of 4.88 mm (±1.70 mm). Local aortic compliance and local aortic wall strain were calculated from the segmented surfaces for each quadrant and then compared with elastic properties obtained ex-vivo. Good agreement was observed between Young's modulus and in-vivo strain. Our results suggest that the lateral and posterior quadrants are the stiffest. In contrast, the medial and anterior quadrants have the lowest aortic stiffness. The in-vivo stiffness tendency agrees with the values obtained ex-vivo. We can conclude that our automatic segmentation method is robust and compatible with clinical practice (thanks to a graphical user interface), while the in-vivo elastic properties are reliable and compatible with the ex-vivo ones.

Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism

OBJECTIVE

It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI.

METHODS

Firstly, an end-to-end minimally supervised and semi-supervised semantic DE-MRI myocardial fibrosis segmentation framework is proposed, which combines image classification and semantic segmentation branches based on the self-attention mechanism. Following that, a residual hole network fused with the dual attention mechanism was built, and a double attention metabolic pathway classification method for cardiac fibrosis in DE-MRI images was developed.

RESULTS

By adding pixel-level labels to an extra 40 training images, the segmentation model may enhance semantic segmentation performance by 2.6 percent (from 61.2 percent to 63.8 percent). When the number of pixel-level labels is increased to 80, semi-supervised feature extraction increases by 4.7 percent when compared to weakly guided semantic segmentation. Adding an attention mechanism to the critical network DRN (Deep Residual Network) can increase the classifier's performance by a small amount. Experiments revealed that the models worked effectively.

CONCLUSION

This paper investigates the segmentation and classification of cardiac fibrosis in DE-MRI data using a semi-supervised semantic segmentation and dual attention mechanism, dealing with the issue that existing segmentation algorithms have difficulty segmenting myocardial fibrosis tissue. In the future, we can consider optimizing the design of the attention module to reduce the module computation.

Role of cardiac magnetic resonance imaging in the diagnosis and management of COVID-19 related myocarditis: Clinical and imaging considerations

There is a growing evidence of cardiovascular complications in coronavirus disease 2019 (COVID-19) patients. As evidence accumulated of COVID-19 mediated inflammatory effects on the myocardium, substantial attention has been directed towards cardiovascular imaging modalities that facilitate this diagnosis. Cardiac magnetic resonance imaging (CMRI) is the gold standard for the detection of structural and functional myocardial alterations and its role in identifying patients with COVID-19 mediated cardiac injury is growing. Despite its utility in the diagnosis of myocardial injury in this population, CMRI’s impact on patient management is still evolving. This review provides a framework for the use of CMRI in diagnosis and management of COVID-19 patients from the perspective of a cardiologist. We review the role of CMRI in the management of both the acutely and remotely COVID-19 infected patient. We discuss patient selection for this imaging modality; T1, T2, and late gadolinium enhancement imaging techniques; and previously described CMRI findings in other cardiomyopathies with potential implications in COVID-19 recovered patients.

Synergistic multi-contrast cardiac magnetic resonance image reconstruction

Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Gadolinium-based Contrast Agents for Cardiac MRI: Use of Linear and Macrocyclic Agents with Associated Safety Profile from 154 779 European Patients

Purpose

To assess current use and acute safety profiles of gadolinium-based contrast agents (GBCAs) in cardiac MRI given recent suspensions of GBCA approval.

Materials and Methods

Patients were retrospectively included from the multinational multicenter European Society of Cardiovascular Radiology (ESCR) MR/CT Registry collected between January 2013 and October 2019. GBCA-associated acute adverse events (AAEs) were classified as mild (self-limiting), moderate (pronounced AAE requiring medical management), and severe (life threatening). Multivariable generalized linear mixed-effect models were used to assess AAE likelihood.

Results

A total of 154 779 patients (average age, 53 years ± 19 [standard deviation]; 99 106 men) who underwent cardiac MRI were included, the majority of whom underwent administration of GBCAs (94.2% [n = 145 855]). While linear GBCAs were used in 15.2% of examinations through 2011, their use decreased to less than 1% in 2018 and 2019. Overall, 0.36% (n = 556) of AAEs were documented (mild, 0.12% [n = 178]; moderate, 0.21% [n = 331]; severe, 0.03% [n = 47]). For nonenhanced cardiac MRI, examination-related events were reported in 2.59% (231 of 8924) of cases, the majority of which were anxiety (0.98% [n = 87]) and dyspnea (0.93% [n = 83]). AAE rates varied significantly by pharmacologic stressor, GBCA molecular structure (macrocyclic vs linear GBCA: multivariable odds ratio, 0.634; 95% confidence interval: 0.452, 0.888; P = .008), GBCA subtype, and imaging indication.

Conclusion

Gadolinium-based contrast agent administration changed according to recent regulatory decisions, with use of macrocyclic agents almost exclusively in 2018 and 2019; these agents also demonstrated a favorable acute safety profile.Supplemental material is available for this article.© RSNA, 2020.

Development and application of artificial intelligence in cardiac imaging

In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public database. Subsequently, we conducted a systematic literature search for recently published clinically relevant studies on AI in cardiac imaging. As a result, 24 and 14 studies using CT and MRI, respectively, were included and summarized. From these studies, it can be concluded that AI is widely applied in cardiac applications in the clinic, including coronary calcium scoring, coronary CT angiography, fractional flow reserve CT, plaque analysis, left ventricular myocardium analysis, diagnosis of myocardial infarction, prognosis of coronary artery disease, assessment of cardiac function, and diagnosis and prognosis of cardiomyopathy. These advancements show that AI has a promising prospect in cardiac imaging.

Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging

PURPOSE OF REVIEW

Myocardial fibrosis (MF) arises due to myocardial infarction and numerous cardiac diseases. MF may lead to several heart disorders, such as heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF on CMR is a tedious and time-consuming task that is subject to high observer variability. Automated segmentation and quantification of MF is important for risk stratification and treatment planning in patients with heart disorders. This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images.

RECENT FINDINGS

With the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible. The application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.

Reconstruction techniques for cardiac cine MRI

The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic resonance image reconstruction. Additionally, clinical relevance, main challenges, and future trends of this image modality are outlined. Thus, this paper aims to provide a general vision about cine MRI as the standard procedure in functional evaluation of the heart, focusing on technical methodologies.

Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction

PURPOSE

To investigate the ability of texture analysis to differentiate between infarcted nonviable, viable, and remote segments on cardiac cine magnetic resonance imaging (MRI).

METHODS

This retrospective study included 50 patients suffering chronic myocardial infarction. The data were randomly split into training (30 patients) and testing (20 patients) sets. The left ventricular myocardium was segmented according to the 17-segment model in both cine and late gadolinium enhancement (LGE) MRI. Infarcted myocardium regions were identified on LGE in short-axis views. Nonviable segments were identified as those showing LGE ≥ 50%, and viable segments those showing 0 < LGE < 50% transmural extension. Features derived from five texture analysis methods were extracted from the segments on cine images. A support vector machine (SVM) classifier was trained with different combination of texture features to obtain a model that provided optimal classification performance.

RESULTS

The best classification on testing set was achieved with local binary patterns features using a 2D + t approach, in which the features are computed by including information of the time dimension available in cine sequences. The best overall area under the receiver operating characteristic curve (AUC) were: 0.849, sensitivity of 92% to detect nonviable segments, 72% to detect viable segments, and 85% to detect remote segments.

CONCLUSION

Nonviable segments can be detected on cine MRI using texture analysis and this may be used as hypothesis for future research aiming to detect the infarcted myocardium by means of a gadolinium-free approach.

Cardiac MRI: a Translational Imaging Tool for Characterizing Anthracycline-Induced Myocardial Remodeling

Cardiovascular side effects of cancer therapeutics are the leading causes of morbidity and mortality in cancer survivors. Anthracyclines (AC) serve as the backbone of many anti-cancer treatment strategies, but dose-dependent myocardial injury limits their use. Cumulative AC exposure can disrupt the dynamic equilibrium of the myocardial microarchitecture while repeated injury and repair leads to myocyte loss, interstitial myocardial fibrosis, and impaired contractility. Although children are assumed to have greater myocardial plasticity, AC exposure at a younger age portends worse prognosis. In older patients, there is lower overall survival once they develop cardiovascular disease. Because aberrations in the myocardial architecture predispose the heart to a decline in function, early detection with sensitive imaging tools is crucial and the implications for resource utilization are substantial. As a comprehensive imaging modality, cardiac magnetic resonance (CMR) imaging is able to go beyond quantification of ejection fraction and myocardial deformation to characterize adaptive microstructural and microvascular changes that are important to myocardial tissue health. Herein, we describe CMR as an established translational imaging tool that can be used clinically to characterize AC-associated myocardial remodeling.

Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging

The purpose of this study was to differentiate acute from chronic myocardial infarction using machine learning techniques and texture features extracted from cardiac magnetic resonance imaging (MRI). The study group comprised 22 cases with acute myocardial infarction (AMI) and 22 cases with chronic myocardial infarction (CMI). Cine and late gadolinium enhancement (LGE) MRI were analyzed independently to differentiate AMI from CMI. A total of 279 texture features were extracted from predefined regions of interest (ROIs): the infarcted area on LGE MRI, and the entire myocardium on cine MRI. Classification performance was evaluated by a nested cross-validation approach combining a feature selection technique with three predictive models: random forest, support vector machine (SVM) with Gaussian Kernel, and SVM with polynomial kernel. The polynomial SVM yielded the best classification performance. Receiver operating characteristic curves provided area-under-the-curve (AUC) (mean±standard deviation) of 0.86±0.06 on LGE MRI using 72 features; AMI sensitivity=0.81±0.08 and specificity=0.84±0.09. On cine MRI, AUC=0.82±0.06 using 75 features; AMI sensitivity=0.79±0.10 and specificity=0.80±0.10. We concluded that texture analysis can be used for differentiation of AMI from CMI on cardiac LGE MRI, and also on standard cine sequences in which the infarction is visually imperceptible in most cases.

Rapid functional cardiac imaging after gadolinium injection: Evaluation of a highly accelerated sequence with sparse data sampling and iterative reconstruction

To generate a patient-friendly, time-efficient cardiac MRI examination protocol, a highly accelerated real-time CINE MR sequence (SSIR) was acquired in the idle time in between contrast injection and late gadolinium enhancement phase. 20 consecutive patients underwent a cardiac MRI examination including a multi-breath-hold sequence as gold standard (Ref) as well as SSIR sequences with (SSIR-BH) and without breath-hold (SSIR-nonBH). SSIR sequences were acquired 4 minutes after gadolinium injection. Right- (RV) and left-ventricular (LV) volumetric functional parameters were evaluated and compared between Ref and SSIR sequences. Despite reduced contrast between myocardium and intra-ventricular blood, volumetric as well as regional wall movement assessment revealed high agreement between both SSIR sequences and Ref. Excellent correlation and narrow limits of agreements were found for both SSIR-BH and SSIR-nonBH when compared to Ref for both LV (mean LV ejection fraction [EF] Ref: 52.8 ± 12.6%, SSIR-BH 52.3 ± 12.9%, SSIR-nonBH 52.5 ± 12.6%) and RV (mean RV EF Ref: 52.7 ± 9.4%, SSIR-BH 52.0 ± 8.1%, SSIR-nonBH 52.2 ± 9.3%) analyses. Even when acquired in the idle time in between gadolinium injection and LGE acquisition, the highly accelerated SSIR sequence delivers accurate volumetric and regional wall movement information. It thus seems ideal for very time-efficient and robust cardiac MR imaging protocols.

Minimally invasive diagnosis of a pericardial mass by CT-guided fine-needle aspiration

The preferred management of a cardiac mass remains controversial, but it often includes open-chest surgical excision to obtain an adequate tissue sample for histological workup. We herein report a less invasive approach in which an accurate and timely cytological diagnosis of pericardial angiosarcoma was reached by studying a CT-guided fine-needle aspiration cell block. The cell block showed proliferation of atypical cells with occasional mitotic figures, vasoformative features, and immunoreactivity to WT1, vimentin, CD31, CD34, ERG, and Ki67. Recourse to fine-needle aspiration and cell block study is a valuable diagnostic approach to be considered when a cardiac mass is percutaneously accessible.

Screening for cardiovascular disease before kidney transplantation

Pre-kidney transplant cardiac screening has garnered particular attention from guideline committees as an approach to improving post-transplant success. Screening serves two major purposes: To more accurately inform transplant candidates of their risk for a cardiac event before and after the transplant, thereby informing decisions about proceeding with transplantation, and to guide pre-transplant management so that post-transplant success can be maximized. Transplant candidates on dialysis are more likely to be screened for coronary artery disease than those not being considered for transplantation. Thorough history and physical examination taking, resting electrocardiography and echocardiography, exercise stress testing, myocardial perfusion scintigraphy, dobutamine stress echocardiography, cardiac computed tomography, cardiac biomarker measurement, and cardiac magnetic resonance imaging all play contributory roles towards screening for cardiovascular disease before kidney transplantation. In this review, the importance of each of these screening procedures for both coronary artery disease and other forms of cardiac disease are discussed.