Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy

The beating heart: artificial intelligence for cardiovascular application in the clinic

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

Cardiovascular Magnetic Resonance-Based Tissue Characterization in Patients With Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a common hereditable cardiomyopathy that affects between 1:200 to 1:500 of the general population. The role of cardiovascular magnetic resonance (CMR) imaging in the management of HCM has expanded over the past 2 decades to become a key informant of risk in this patient population, delivering unique insights into tissue health and its influence on future outcomes. Numerous mature CMR-based techniques are clinically available for the interrogation of tissue health in patients with HCM, inclusive of contrast and noncontrast methods. Late gadolinium enhancement imaging remains a cornerstone technique for the identification and quantification of myocardial fibrosis with large cumulative evidence supporting value for the prediction of arrhythmic outcomes. T1 mapping delivers improved fidelity for fibrosis quantification through direct estimations of extracellular volume fraction but also offers potential for noncontrast surrogate assessments of tissue health. Water-sensitive imaging, inclusive of T2-weighted dark blood imaging and T2 mapping, have also shown preliminary potential for assisting in risk discrimination. Finally, emerging techniques, inclusive of innovative multiparametric methods, are expanding the utility of CMR to assist in the delivery of comprehensive tissue characterization toward the delivery of personalized HCM care. In this narrative review we summarize the contemporary landscape of CMR techniques aimed at characterizing tissue health in patients with HCM. The value of these respective techniques to identify patients at elevated risk of future cardiovascular outcomes are highlighted.

Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification

Purpose

Ischemic myocardial scarring (IMS) is a common outcome of coronary artery disease that potentially leads to lethal arrythmias and heart failure. Late-gadolinium-enhanced cardiac magnetic resonance (CMR) imaging scans have served as the diagnostic bedrock for IMS, with recent advancements in machine learning enabling enhanced scar classification. However, the trade-off for these improvements is intensive computational and time demands. As a solution, we propose a combination of lightweight preprocessing (LWP) and template matching (TM) to streamline IMS classification.

Approach

CMR images from 279 patients (151 IMS, 128 control) were classified for IMS presence using two convolutional neural networks (CNNs) and TM, both with and without LWP. Evaluation metrics included accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUROC), and processing time. External testing dataset analysis encompassed patient-level classifications (PLCs) and a CNN versus TM classification comparison (CVTCC).

Results

LWP enhanced the speed of both CNNs (4.9x) and TM (21.9x). Furthermore, in the absence of LWP, TM outpaced CNNs by over 10x, while with LWP, TM was more than 100x faster. Additionally, TM performed similarly to the CNNs in accuracy, sensitivity, specificity, F1-score, and AUROC, with PLCs demonstrating improvements across all five metrics. Moreover, the CVTCC revealed a substantial 90.9% agreement.

Conclusions

Our results highlight the effectiveness of LWP and TM in streamlining IMS classification. Anticipated enhancements to LWP's region of interest (ROI) isolation and TM's ROI targeting are expected to boost accuracy, positioning them as a potential alternative to CNNs for IMS classification, supporting the need for further research.

Discrimination models with radiomics features derived from cardiovascular magnetic resonance images for distinguishing hypertensive heart disease from hypertrophic cardiomyopathy

Background

Discriminating hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) is challenging, because both are characterized by left ventricular hypertrophy (LVH). Radiomics might be effective to differentiate HHD from HCM. Therefore, this study aimed to investigate discriminators and build discrimination models between HHD and HCM using multiparametric cardiac magnetic resonance (CMR) findings and radiomics score (radscore) derived from late gadolinium enhancement (LGE) and cine images.

Methods

In this single center, retrospective study, 421 HCM patients [median and interquartile range (IQR), 50.0 (38.0-59.0) years; male, 70.5%] from January 2017 to September 2021 and 200 HHD patients [median and IQR, 44.5 (35.0-57.0) years; male, 88.5%] from September 2015 to July 2022 were consecutively included and randomly stratified into a training group and a validation group at a ratio of 6:4. Multiparametric CMR findings were obtained using cvi42 software and radiomics features using Python software. After dimensional reduction, the radscore was calculated by summing the remaining radiomics features weighted by their coefficients. Multiparametric CMR findings and radscore that were statistically significant in univariate logistic regression were used to build combined discrimination models via multivariate logistic regression.

Results

After multivariate logistic regression, the maximal left ventricular end diastolic wall thickness (LVEDWT), left ventricular ejection fraction (LVEF), presence of LGE, cine radscore and LGE radscore were identified as significant characteristics and used to build a combined discrimination model. This model achieved an area under the receiver operator characteristic curve (AUC) of 0.979 (0.968-0.990) in the training group and 0.981 (0.967-0.995) in the validation group, significantly better than the model using multiparametric CMR findings alone (P<0.001).

Conclusions

Radiomics features derived from cardiac cine and LGE images can effectively discriminate HHD from HCM.

Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk for Heart Failure

Purpose To develop a model integrating radiomics features from cardiac MR cine images with clinical and standard cardiac MRI predictors to identify patients with hypertrophic cardiomyopathy (HCM) at high risk for heart failure (HF). Materials and Methods In this retrospective study, 516 patients with HCM (median age, 51 years [IQR: 40-62]; 367 [71.1%] men) who underwent cardiac MRI from January 2015 to June 2021 were divided into training and validation sets (7:3 ratio). Radiomics features were extracted from cardiac cine images, and radiomics scores were calculated based on reproducible features using the least absolute shrinkage and selection operator Cox regression. Radiomics scores and clinical and standard cardiac MRI predictors that were significantly associated with HF events in univariable Cox regression analysis were incorporated into a multivariable analysis to construct a combined prediction model. Model performance was validated using time-dependent area under the receiver operating characteristic curve (AUC), and the optimal cutoff value of the combined model was determined for patient risk stratification. Results The radiomics score was the strongest predictor for HF events in both univariable (hazard ratio, 10.37; P < .001) and multivariable (hazard ratio, 10.25; P < .001) analyses. The combined model yielded the highest 1- and 3-year AUCs of 0.81 and 0.80, respectively, in the training set and 0.82 and 0.77 in the validation set. Patients stratified as high risk had more than sixfold increased risk of HF events compared with patients at low risk. Conclusion The combined model with radiomics features and clinical and standard cardiac MRI parameters accurately identified patients with HCM at high risk for HF. Keywords: Cardiomyopathies, Outcomes Analysis, Cardiovascular MRI, Hypertrophic Cardiomyopathy, Radiomics, Heart Failure Supplemental material is available for this article. © RSNA, 2024.

Association of increased epicardial adipose tissue derived from cardiac magnetic resonance imaging with myocardial fibrosis in Duchenne muscular dystrophy: a clinical prediction model development and validation study in 283 participants

Background

Epicardial adipose tissue (EAT) contributes to inflammation and fibrosis of the neighboring myocardial tissue via paracrine signaling. In this retrospective study, we investigated the abnormal changes in the amount of EAT in male children with Duchenne muscular dystrophy (DMD) using cardiac magnetic resonance (CMR) imaging. Furthermore, we constructed and validated a nomogram including EAT-related CMR imaging parameter for predicting the occurrence of myocardial fibrosis in patients with DMD.

Methods

This study enrolled 283 patients with DMD and 57 healthy participants who underwent CMR acquisitions to measure the quantitative parameters of EAT, pericardial adipose tissue (PAT), paracardial adipose tissue, and subcutaneous adipose tissue. Late gadolinium enhancement (LGE) was performed to confirm myocardial fibrosis in patients with DMD. The DMD group consisted of 200 patients from institution 1 (the ratio of the training set and the internal validation set was 7:3) and 83 patients from four other institutions (the external validation set). Logistic and least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal predictors and to develop and validate the nomogram model predicting LGE risk in the training set, internal validation set, and external validation set.

Results

Compared with those in healthy controls, some regional EAT thicknesses, areas, and global volumes were significantly higher in patients with DMD, and 41.7% of patients with DMD showed positive LGE. These LGE-positive patients with DMD showed significantly higher EAT volume (median 23.9 mL/m3; P<0.001) and PAT volume (median 31.8 mL/m3; P<0.001) compared with the LGE-negative patients with DMD. Age [odds ratio (OR) 2.0; P<0.001], body fat percentage (OR 1.3; P<0.001), and EAT volume (OR 1.4; P<0.001) were independently associated with positive LGE in the training set. The interactive dynamic nomogram showed superior prediction performance, with a high degree of the calibration, discrimination, and clinical net benefit in the training and validation of the DMD datasets. The area under the curve (AUC) values of the nomogram in the training set, internal validation set, and external validation set were 0.95 [95% confidence interval (CI): 0.91-0.98], 0.97 (95% CI: 0.92-0.99), and 0.95 (95% CI: 0.91-0.99), respectively.

Conclusions

The onset of LGE-based myocardial fibrosis was associated with EAT volume in patients with DMD. Additionally, the nomogram with EAT volumes showed superior performance in patients with DMD for predicting the occurrence of myocardial fibrosis.

Present and Future Innovations in AI and Cardiac MRI

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.

The year 2022 in the European Heart Journal-Cardiovascular Imaging: Part I

The European Heart Journal-Cardiovascular Imaging with its over 10 years existence is an established leading multi-modality cardiovascular imaging journal. Pertinent publications including original research, how-to papers, reviews, consensus documents, and in our journal from 2022 have been highlighted in two reports. Part I focuses on cardiomyopathies, heart failure, valvular heart disease, and congenital heart disease and related emerging techniques and technologies.

Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study

PURPOSE

In the past decade, there has been a rapid increase in the development of automatic cardiac segmentation methods. However, the automatic quality control (QC) of these segmentation methods has received less attention. This study aims to address this gap by developing an automatic pipeline that incorporates DL-based cardiac segmentation and radiomics-based quality control.

METHODS

In the DL-based localization and segmentation part, the entire heart was first located and cropped. Then, the cropped images were further utilized for the segmentation of the right ventricle cavity (RVC), myocardium (MYO), and left ventricle cavity (LVC). As for the radiomics-based QC part, a training radiomics dataset was created with segmentation tasks of various quality. This dataset was used for feature extraction, selection, and QC model development. The model performance was then evaluated using both internal and external testing datasets.

RESULTS

In the internal testing dataset, the segmentation model demonstrated a great performance with a dice similarity coefficient (DSC) of 0.954 for whole heart segmentations. Images were then appropriately cropped to 160 × 160 pixels. The models also performed well for cardiac substructure segmentations. The DSC values were 0.863, 0.872, and 0.940 for RVC, MYO, and LVC for 2D masks and 0.928, 0.886, and 0.962 for RVC, MYO, and LVC for 3D masks with an attention-UNet. After feature selection with the radiomics dataset, we developed a series of models to predict the automatic segmentation quality and its DSC value for the RVC, MYO, and LVC structures. The mean absolute values for our best prediction models were 0.060, 0.032, and 0.021 for 2D segmentations and 0.027, 0.017, and 0.011 for 3D segmentations, respectively. Additionally, the radiomics-based classification models demonstrated a high negative detection rate of >0.85 in all 2D groups. In the external dataset, models showed similar results.

CONCLUSIONS

We developed a pipeline including cardiac substructure segmentation and QC at both the slice (2D) and subject (3D) levels. Our results demonstrate that the radiomics method possesses great potential for the automatic QC of cardiac segmentation.

Arrhythmic Risk Stratification among Patients with Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a cardiac muscle disorder characterized by generally asymmetric abnormal hypertrophy of the left ventricle without abnormal loading conditions (such as hypertension or valvular heart disease) accounting for the left ventricular wall thickness or mass. The incidence of sudden cardiac death (SCD) in HCM patients is about 1% yearly in adults, but it is far higher in adolescence. HCM is the most frequent cause of death in athletes in the Unites States of America. HCM is an autosomal-dominant genetic cardiomyopathy, and mutations in the genes encoding sarcomeric proteins are identified in 30-60% of cases. The presence of this genetic mutation carries more than 2-fold increased risk for all outcomes, including ventricular arrhythmias. Genetic and myocardial substrate, including fibrosis and intraventricular dispersion of conduction, ventricular hypertrophy and microvascular ischemia, increased myofilament calcium sensitivity and abnormal calcium handling, all play a role as arrhythmogenic determinants. Cardiac imaging studies provide important information for risk stratification. Transthoracic echocardiography can be helpful to evaluate left ventricular (LV) wall thickness, LV outflow-tract gradient and left atrial size. Additionally, cardiac magnetic resonance can evaluate the prevalence of late gadolinium enhancement, which when higher than 15% of LV mass is a prognostic maker of SCD. Age, family history of SCD, syncope and non-sustained ventricular tachycardia at Holter ECG have also been validated as independent prognostic markers of SCD. Arrhythmic risk stratification in HCM requires careful evaluation of several clinical aspects. Symptoms combined with electrocardiogram, cardiac imaging tools and genetic counselling are the modern cornerstone for proper risk stratification.

Identification of fibrosis in hypertrophic cardiomyopathy: a radiomic study on cardiac magnetic resonance cine imaging

OBJECTIVES

Hypertrophic cardiomyopathy (HCM) often requires repeated enhanced cardiac magnetic resonance (CMR) imaging to detect fibrosis. We aimed to develop a practical model based on cine imaging to help identify patients with high risk of fibrosis and screen out patients without fibrosis to avoid unnecessary injection of contrast.

METHODS

A total of 273 patients with HCM were divided into training and test sets at a ratio of 7:3. Logistic regression analysis was used to find predictive image features to construct CMR model. Radiomic features were derived from the maximal wall thickness (MWT) slice and entire left ventricular (LV) myocardium. Extreme gradient boosting was used to build radiomic models. Integrated models were established by fusing image features and radiomic models. The model performance was validated in the test set and assessed by ROC and calibration curve and decision curve analysis (DCA).

RESULTS

We established five prediction models, including CMR, R1 (based on the MWT slice), R2 (based on the entire LV myocardium), and two integrated models (I_CMR+R1 and I_CMR+R2). In the test set, I_CMR+R2 model had an excellent AUC value (0.898), diagnostic accuracy (89.02%), sensitivity (92.54%), and F1 score (93.23%) in identifying patients with positive late gadolinium enhancement. The calibration plots and DCA indicated that I_CMR+R2 model was well-calibrated and presented a better net benefit than other models.

CONCLUSIONS

A predictive model that fused image and radiomic features from the entire LV myocardium had good diagnostic performance, robustness, and clinical utility.

KEY POINTS

• Hypertrophic cardiomyopathy is prone to fibrosis, requiring patients to undergo repeated enhanced cardiac magnetic resonance imaging to detect fibrosis over their lifetime follow-up. • A predictive model based on the entire left ventricular myocardium outperformed a model based on a slice of the maximal wall thickness. • A predictive model that fused image and radiomic features from the entire left ventricular myocardium had excellent diagnostic performance, robustness, and clinical utility.

Applications of Machine Learning in Cardiology

In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.

Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy

BACKGROUND

Myocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), has important prognostic value in hypertrophic cardiomyopathy (HCM). However, nearly 50% of HCM patients have no scar but undergo repeated gadolinium-based CMR over their life span. We sought to develop an artificial intelligence (AI)-based screening model using radiomics and deep learning (DL) features extracted from balanced steady state free precession (bSSFP) cine sequences to identify HCM patients without scar.

METHODS

We evaluated three AI-based screening models using bSSFP cine image features extracted by radiomics, DL, or combined DL-Radiomics. Images for 759 HCM patients (50 ± 16 years, 66% men) in a multi-center/vendor study were used to develop and test model performance. An external dataset of 100 HCM patients (53 ± 14 years, 70% men) was used to assess model generalizability. Model performance was evaluated using area-under-receiver-operating curve (AUC).

RESULTS

The DL-Radiomics model demonstrated higher AUC compared to DL and Radiomics in the internal (0.83 vs 0.77, p = 0.006 and 0.78, p = 0.05; n = 159) and external (0.74 vs 0.64, p = 0.006 and 0.71, p = 0.27; n = 100) datasets. The DL-Radiomics model correctly identified 43% and 28% of patients without scar in the internal and external datasets compared to 42% and 16% by Radiomics model and 42% and 23% by DL model, respectively.

CONCLUSIONS

A DL-Radiomics AI model using bSSFP cine images outperforms DL or Radiomics models alone as a scar screening tool prior to gadolinium administration. Despite its potential, the clinical utility of the model remains limited and further investigation is needed to improve the accuracy and generalizability.

Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?

Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.

Detection of early phenotype cardiac sarcoidosis by cardiovascular magnetic resonance

PURPOSE OF REVIEW

Cardiac sarcoidosis has high prevalence in sarcoidosis patients and contributes to significant morbidity and mortality. Early detection of cardiac sarcoidosis is essential to improving patients' symptoms and cardiovascular outcomes.

RECENT FINDINGS

Cardiovascular magnetic resonance imaging (CMR) is an excellent diagnostic modality for cardiac sarcoidosis. However, early phenotypes of cardiac sarcoidosis have more mild imaging phenotypes. These mild and sometimes subtle imaging phenotypes of cardiac sarcoidosis have lower diagnostic sensitivity and specificity for cardiac sarcoidosis by CMR when compared with more severe imaging phenotypes of cardiac sarcoidosis. In addition, many sarcoidosis patient cohorts frequently have heterogenous potential alternative etiologies for mild myocardial disease detected by mild late gadolinium enhancement (LGE) findings. In early phenotype cardiac sarcoidosis, analysis of the LGE pattern and location can improve the diagnostic specificity of these mild LGE findings.

SUMMARY

The current review focuses on the current strengths and challenges in CMR detection of early phenotypes of cardiac sarcoidosis by the LGE technique.