Cardiovascular magnetic resonance stress and rest T1-mapping using regadenoson for detection of ischemic heart disease compared to healthy controls

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.

Perfusion Imaging for the Heart

The use of myocardial perfusion imaging during a stress cardiac magnetic resonance (CMR) examination for the evaluation of coronary artery disease is now recommended by both US and European guidelines. Several studies have demonstrated high diagnostic accuracy for the detection of hemodynamically significant coronary artery disease. Stress perfusion CMR has been shown to be a noninvasive and cost-effective alternative to guide coronary revascularization.

Potential of non-contrast stress T1 mapping for the assessment of myocardial injury in hypertrophic cardiomyopathy

BACKGROUND

Ischemia of the hypertrophied myocardium due to microvascular dysfunction is related to a worse prognosis in hypertrophic cardiomyopathy (HCM). Stress and rest T1 mapping without contrast agents can be used to assess myocardial blood flow. Herein, we evaluated the potential of non-contrast stress T1 mapping in assessing myocardial injury in patients with HCM.

METHODS

Forty-five consecutive subjects (31 HCM patients and 14 control subjects) underwent cardiac magnetic resonance (CMR) at 3T, including cine imaging, T1 mapping at rest and during adenosine triphosphate (ATP) stress, late gadolinium enhancement (LGE), and phase-contrast (PC) cine imaging of coronary sinus flow at rest and during stress to assess coronary flow reserve (CFR). PC cine imaging was performed on 25 subjects (17 patients with HCM and 8 control subjects). Native T1 values at rest and during stress were measured using the 16-segment model, and T1 reactivity was defined as the change in T1 values from rest to stress.

RESULTS

ATP stress induced a significant increase in native T1 values in both the HCM and control groups (HCM: p < 0.001, control: p = 0.002). T1 reactivity in the HCM group was significantly lower than that in the control group (4.2 ± 0.3% vs. 5.6 ± 0.5%, p = 0.044). On univariate analysis, T1 reactivity correlated with native T1 values at rest, left ventricular mass index, and CFR. Multiple linear regression analysis demonstrated that only CFR was independently correlated with T1 reactivity (β = 0.449; 95% confidence interval, 0.048-0.932; p = 0.032). Furthermore, segmental analysis showed decreased T1 reactivity in the hypertrophied myocardium and the non-hypertrophied myocardium with LGE in the HCM group.

CONCLUSIONS

T1 reactivity was lower in the hypertrophied myocardium and LGE-positive myocardium compared to non-injured myocardium. Non-contrast stress T1 mapping is a promising CMR method for assessing myocardial injury in patients with HCM. Trial registration Retrospectively registered.

Cardiac Magnetic Resonance Imaging (CMRI) Applications in Patients with Chest Pain in the Emergency Department: A Narrative Review

CMRI is the exclusive imaging technique capable of identifying myocardial edema, endomyocardial fibrosis, pericarditis accompanied by pericardial effusions, and apical thrombi within either the left or right ventricle. In this work, we examine the research literature on the use of CMRI in the diagnosis of chest discomfort, employing randomized controlled trials (RCTs) to evaluate its effectiveness. The research outlines the disorders of the chest and the machine learning approaches for detecting them. In conclusion, the study ends with an examination of a fundamental illustration of CMRI analysis. To find a comprehensive review, the Scopus scientific resource is analyzed. The issue, based on the findings, is to distinguish ischemia from non-ischemic cardiac causes of chest pain in individuals presenting with sudden chest pain or discomfort upon arrival at the emergency department (ED). Due to the failure of conventional methods in accurately diagnosing acute cardiac ischemia, individuals are still being inappropriately discharged from the ED, resulting in a heightened death rate.

Diagnostic and Prognostic Value of Stress Cardiovascular Magnetic Resonance Imaging in Patients With Known or Suspected Coronary Artery Disease: A Systematic Review and Meta-analysis

Importance

The clinical utility of stress cardiovascular magnetic resonance imaging (CMR) in stable chest pain is still debated, and the low-risk period for adverse cardiovascular (CV) events after a negative test result is unknown.

Objective

To provide contemporary quantitative data synthesis of the diagnostic accuracy and prognostic value of stress CMR in stable chest pain.

Data Sources

PubMed and Embase databases, the Cochrane Database of Systematic Reviews, PROSPERO, and the ClinicalTrials.gov registry were searched for potentially relevant articles from January 1, 2000, through December 31, 2021.

Study Selection

Selected studies evaluated CMR and reported estimates of diagnostic accuracy and/or raw data of adverse CV events for participants with either positive or negative stress CMR results. Prespecified combinations of keywords related to the diagnostic accuracy and prognostic value of stress CMR were used. A total of 3144 records were evaluated for title and abstract; of those, 235 articles were included in the full-text assessment of eligibility. After exclusions, 64 studies (74 470 total patients) published from October 29, 2002, through October 19, 2021, were included.

Data Extraction and Synthesis

This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Main Outcomes and Measures

Diagnostic odds ratios (DORs), sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), odds ratio (OR), and annualized event rate (AER) for all-cause death, CV death, and major adverse cardiovascular events (MACEs) defined as the composite of myocardial infarction and CV death.

Results

A total of 33 diagnostic studies pooling 7814 individuals and 31 prognostic studies pooling 67 080 individuals (mean [SD] follow-up, 3.5 [2.1] years; range, 0.9-8.8 years; 381 357 person-years) were identified. Stress CMR yielded a DOR of 26.4 (95% CI, 10.6-65.9), a sensitivity of 81% (95% CI, 68%-89%), a specificity of 86% (95% CI, 75%-93%), and an AUROC of 0.84 (95% CI, 0.77-0.89) for the detection of functionally obstructive coronary artery disease. In the subgroup analysis, stress CMR yielded higher diagnostic accuracy in the setting of suspected coronary artery disease (DOR, 53.4; 95% CI, 27.7-103.0) or when using 3-T imaging (DOR, 33.2; 95% CI, 19.9-55.4). The presence of stress-inducible ischemia was associated with higher all-cause mortality (OR, 1.97; 95% CI, 1.69-2.31), CV mortality (OR, 6.40; 95% CI, 4.48-9.14), and MACEs (OR, 5.33; 95% CI, 4.04-7.04). The presence of late gadolinium enhancement (LGE) was associated with higher all-cause mortality (OR, 2.22; 95% CI, 1.99-2.47), CV mortality (OR, 6.03; 95% CI, 2.76-13.13), and increased risk of MACEs (OR, 5.42; 95% CI, 3.42-8.60). After a negative test result, pooled AERs for CV death were less than 1.0%.

Conclusion and Relevance

In this study, stress CMR yielded high diagnostic accuracy and delivered robust prognostication, particularly when 3-T scanners were used. While inducible myocardial ischemia and LGE were associated with higher mortality and risk of MACEs, normal stress CMR results were associated with a lower risk of MACEs for at least 3.5 years.

Detection of Myocardial Ischemia Using Cardiovascular MRI Stress T1 Mapping: A Miniature-Swine Validation Study

Purpose

To assess the efficacy of cardiac MRI stress T1 mapping in detecting ischemic and infarcted myocardium in a miniature-swine model, using pathologic findings as the reference standard.

Materials and Methods

Ten adult male Chinese miniature swine, with coronary artery stenosis induced by an ameroid constrictor, and two healthy control swine were studied. Cardiac 3-T MRI rest and adenosine triphosphate stress T1 mapping and perfusion images, along with resting and late gadolinium enhancement images, were acquired at baseline and weekly up to 4 weeks after surgery or until humanely killed. A receiver operating characteristic analysis was used to analyze the performance of T1 mapping in the detection of myocardial ischemia.

Results

In the experimental group, both the infarcted myocardium (ΔT1 = 10 msec ± 2 [SD]; ΔT1 percentage = 0.7% ± 0.1) and ischemic myocardium (ΔT1 = 10 msec ± 2; ΔT1 percentage = 0.9% ± 0.2) exhibited reduced T1 reactivity compared with the remote myocardium (ΔT1 = 53 msec ± 7; ΔT1 percentage = 4.7% ± 0.6) and normal myocardium (ΔT1 = 56 msec ± 11; ΔT1 percentage = 4.9% ± 1.1). Receiver operating characteristic analysis demonstrated high diagnostic performance of ΔT1 in detecting ischemic myocardium, with an area under the curve (AUC) of 0.84 (P < .001). Rest T1 displayed high diagnostic performance in detecting infarcted myocardium (AUC = 0.95; P < .001). When rest T1 and ΔT1 were combined, the diagnostic performance for both ischemic and infarcted myocardium were improved (AUCs, 0.89 and 0.97, respectively; all P < .001). The collagen volume fraction correlated with ΔT1, ΔT1 percentage, and Δ extracellular volume percentage (r = -0.70, -0.70, and -0.50, respectively; P = .001, .001, and .03, respectively).

Conclusion

Using histopathologic validation in a swine model, noninvasive cardiac MRI stress T1 mapping demonstrated high performance in detecting ischemic and infarcted myocardium without the need for contrast agents.Keywords: Coronary Artery Disease, MRI, Myocardial Ischemia, Rest T1 Mapping, Stress T1 Mapping, Swine Model Supplemental material is available for this article. © RSNA, 2023See also commentary by Burrage and Ferreira in this issue.

Stress CMR T1-mapping technique for assessment of coronary microvascular dysfunction in a rabbit model of type II diabetes mellitus: Validation against histopathologic changes

Background

Coronary microvascular dysfunction (CMD) is an early character of type 2 diabetes mellitus (T2DM), and is indicative of adverse events. The present study aimed to validate the performance of the stress T1 mapping technique on cardiac magnetic resonance (CMR) for identifying CMD from a histopathologic perspective and to establish the time course of CMD-related parameters in a rabbit model of T2DM.

Methods

New Zealand white rabbits (n = 30) were randomly divided into a control (n = 8), T2DM 5-week (n = 6), T2DM 10-week (n = 9), and T2DM 15-week (n = 7) groups. The CMR protocol included rest and adenosine triphosphate (ATP) stress T1-mapping imaging using the 5b(20b)3b-modified look-locker inversion-recovery (MOLLI) schema to quantify stress T1 response (stress ΔT1), and first-pass perfusion CMR to quantify myocardial perfusion reserve index (MPRI). After the CMR imaging, myocardial tissue was subjected to hematoxylin-eosin staining to evaluate pathological changes, Masson trichrome staining to measure collagen volume fraction (CVF), and CD31 staining to measure microvascular density (MVD). The associations between CMR parameters and pathological findings were determined using Pearson correlation analysis.

Results

The stress ΔT1 values were 6.21 ± 0.59%, 4.88 ± 0.49%, 3.80 ± 0.40%, and 3.06 ± 0.54% in the control, T2DM 5-week, 10-week, and 15-week groups, respectively (p < 0.001) and were progressively weakened with longer duration of T2DM. Furthermore, a significant correlation was demonstrated between the stress ΔT1 vs. CVF and MVD (r = -0.562 and 0.886, respectively; p < 0.001).

Conclusion

The stress T1 response correlated well with the histopathologic measures in T2DM rabbits, indicating that it may serve as a sensitive CMD-related indicator in early T2DM.

Cardiovascular Magnetic Resonance Parametric Mapping Techniques for the Assessment of Chronic Coronary Syndromes

The term chronic coronary syndromes encompasses a variety of clinical presentations of coronary artery disease (CAD), ranging from stable angina due to epicardial coronary artery disease to microvascular coronary dysfunction. Cardiac magnetic resonance (CMR) imaging has an established role in the diagnosis, prognostication and treatment planning of patients with CAD. Recent advances in parametric mapping CMR techniques have added value in the assessment of patients with chronic coronary syndromes, even without the need for gadolinium contrast administration. Furthermore, quantitative perfusion CMR techniques have enabled the non-invasive assessment of myocardial blood flow and myocardial perfusion reserve and can reliably identify multivessel coronary artery disease and microvascular dysfunction. This review summarizes the clinical applications and the prognostic value of the novel CMR parametric mapping techniques in the setting of chronic coronary syndromes and discusses their strengths, pitfalls and future directions.

Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement

BACKGROUND

Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan without contrast-associated problems.

METHODS

Virtual native enhancement (VNE) is a novel technology that can produce virtual LGE-like images without the need for contrast. VNE combines cine imaging and native T1 maps to produce LGE-like images using artificial intelligence. VNE was developed for patients with previous myocardial infarction from 4271 data sets (912 patients); each data set comprises slice position-matched cine, T1 maps, and LGE images. After quality control, 3002 data sets (775 patients) were used for development and 291 data sets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, using 2 adversarial discriminators to improve the image quality. The left ventricle was contoured semiautomatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull's-eye plots. Lesion quantification by VNE and LGE was compared using linear regression, Pearson correlation (R), and intraclass correlation coefficients. Proof-of-principle histopathologic comparison of VNE in a porcine model of myocardial infarction also was performed.

RESULTS

VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 data sets (all P<0.001). VNE correlated strongly with LGE in quantifying scar size (R, 0.89; intraclass correlation coefficient, 0.94) and transmurality (R, 0.84; intraclass correlation coefficient, 0.90) in 66 patients (277 test data sets). Two cardiovascular magnetic resonance experts reviewed all test image slices and reported an overall accuracy of 84% for VNE in detecting scars when compared with LGE, with specificity of 100% and sensitivity of 77%. VNE also showed excellent visuospatial agreement with histopathology in 2 cases of a porcine model of myocardial infarction.

CONCLUSIONS

VNE demonstrated high agreement with LGE cardiovascular magnetic resonance for myocardial scar assessment in patients with previous myocardial infarction in visuospatial distribution and lesion quantification with superior image quality. VNE is a potentially transformative artificial intelligence-based technology with promise in reducing scan times and costs, increasing clinical throughput, and improving the accessibility of cardiovascular magnetic resonance in the near future.

An automated heart disease prediction approach using linearly support vector regression and stacked linear swarm optimization

In today’s world, mining and learning applications play an essential role in healthcare sectors and intend to transform all the data into an understandable form. However, the healthcare sectors require an automated disease prediction system for better medical analysis and emphasize better prediction accuracy for evaluation purposes. In this paper, a new automated prediction model based on Linearly Support Vector Regression and Stacked Linear Swarm Optimization (LSVR-SLSO) has been proposed to predict heart disease accurately. Primarily, the features are analyzed in a linear and non-linear manner using LSVR feature learning approaches. The extracted features are then fed into the SLSO model in order to extract the global optimal solutions. These global solutions will reduce the data dimensionality and computational complexity during the evaluation phase. Moreover, the optimal solution facilitates the proposed model to predict heart disease appropriately. The simulation can be carried out through the MATLAB environment by utilizing a publicly available benchmark heart disease dataset. The performance results evident that the proposed LSVR-SLSO model can efficiently predict heart disease with superior accuracy of 98%, precision of 98.76%, and recall of 99.7% when compared with conventional approaches. The better performance of the proposed model will pave the way to act as an effective clinical decision support tool for physicians during an emergency.

MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks

Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown good performance in general image registration tasks. We propose MOCOnet, a convolutional neural network solution, for generalisable motion artefact correction in T1 maps.

Methods: The network architecture employs U-Net for producing distance vector fields and utilises warping layers to apply deformation to the feature maps in a coarse-to-fine manner. Using the UK Biobank imaging dataset scanned at 1.5T, MOCOnet was trained on 1,536 mid-ventricular T1 maps (acquired using the ShMOLLI method) with motion artefacts, generated by a customised deformation procedure, and tested on a different set of 200 samples with a diverse range of motion. MOCOnet was compared to a well-validated baseline multi-modal image registration method. Motion reduction was visually assessed by 3 human experts, with motion scores ranging from 0% (strictly no motion) to 100% (very severe motion).

Results: MOCOnet achieved fast image registration (<1 second per T1 map) and successfully suppressed a wide range of motion artefacts. MOCOnet significantly reduced motion scores from 37.1±21.5 to 13.3±10.5 (p < 0.001), whereas the baseline method reduced it to 15.8±15.6 (p < 0.001). MOCOnet was significantly better than the baseline method in suppressing motion artefacts and more consistently (p = 0.007).

Conclusion: MOCOnet demonstrated significantly better motion correction performance compared to a traditional image registration approach. Salvaging data affected by motion with robustness and in a time-efficient manner may enable better image quality and reliable images for immediate clinical interpretation.

Cardiac stress T1-mapping response and extracellular volume stability of MOLLI-based T1-mapping methods

Stress and rest T1-mapping may assess for myocardial ischemia and extracellular volume (ECV). However, the stress T1 response is method-dependent, and underestimation may lead to misdiagnosis. Further, ECV quantification may be affected by time, as well as the number and dosage of gadolinium (Gd) contrast administered. We compared two commonly available T1-mapping approaches in their stress T1 response and ECV measurement stability. Healthy subjects (n = 10, 50% female, 35 ± 8 years) underwent regadenoson stress CMR (1.5 T) on two separate days. Prototype ShMOLLI 5(1)1(1)1 sequence was used to acquire consecutive mid-ventricular T1-maps at rest, stress and post-Gd contrast to track the T1 time evolution. For comparison, standard MOLLI sequences were used: MOLLI 5(3)3 Low (256 matrix) & High (192 matrix) Heart Rate (HR) to acquire rest and stress T1-maps, and MOLLI 4(1)3(1)2 Low & High HR for post-contrast T1-maps. Stress and rest myocardial blood flow (MBF) maps were acquired after IV Gd contrast (0.05 mmol/kg each). Stress T1 reactivity (delta T1) was defined as the relative percentage increase in native T1 between rest and stress. Myocardial T1 values for delta T1 (dT1) and ECV were calculated. Residuals from the identified time dependencies were used to assess intra-method variability. ShMOLLI achieved a greater stress T1 response compared to MOLLI Low and High HR (peak dT1 = 6.4 ± 1.7% vs. 4.8 ± 1.3% vs. 3.8 ± 1.0%, respectively; both p < 0.0001). ShMOLLI dT1 correlated strongly with stress MBF (r = 0.77, p < 0.001), compared to MOLLI Low HR (r = 0.65, p < 0.01) and MOLLI High HR (r = 0.43, p = 0.07). ShMOLLI ECV was more stable to gadolinium dose with less time drift (0.006-0.04% per minute) than MOLLI variants. Overall, ShMOLLI demonstrated less intra-individual variability than MOLLI variants for stress T1 and ECV quantification. Power calculations indicate up to a fourfold (stress T1) and 7.5-fold (ECV) advantage in sample-size reduction using ShMOLLI. Our results indicate that ShMOLLI correlates strongly with increased MBF during regadenoson stress and achieves a significantly higher stress T1 response, greater effect size, and greater ECV measurement stability compared with the MOLLI variants tested.