The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence-Based Approach Using Perfusion Mapping

Emerging Pathway to a Precision Medicine Approach for Angina With Nonobstructive Coronary Arteries in Women

Women are disproportionately affected by symptoms of angina with nonobstructive coronary arteries (ANOCA) which is associated with significant mortality and economic impact. Although distinct endotypes of ANOCA have been defined, it is underdiagnosed and is often incompletely characterized when identified. Patients are often unresponsive to traditional therapeutic options, which are typically antianginal, and the current ability to guide treatment modification by specific pathways is limited. Studies have associated specific genetic loci, transcriptomic features, and biomarkers with ANOCA. Such panomic data, in combination with known imaging and invasive diagnostic techniques, should be utilized to define more precise pathophysiologic subtypes of ANOCA in women, which will in turn help to identify targeted, effective therapies. A precision medicine-based approach to managing ANOCA incorporating these techniques in women has the potential to significantly improve their clinical care.

Clinical implementation of a fully automated quantitative perfusion cardiovascular magnetic resonance imaging workflow with a simplified dual-bolus contrast administration scheme

This study clinically implemented a ready-to-use quantitative perfusion (QP) cardiovascular magnetic resonance (QP CMR) workflow, encompassing a simplified dual-bolus gadolinium-based contrast agent (GBCA) administration scheme and fully automated QP image post-processing. Twenty-five patients with suspected obstructive coronary artery disease (CAD) underwent both adenosine stress perfusion CMR and an invasive coronary angiography or coronary computed tomography angiography. The dual-bolus protocol consisted of a pre-bolus (0.0075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) and a main bolus (0.075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) at an infusion rate of 3 ml/s. The arterial input function curves showed excellent quality. Stress MBF ≤ 1.84 ml/g/min accurately detected obstructive CAD (area under the curve 0.79; 95% Confidence Interval: 0.66 to 0.89). Combined visual assessment of color pixel QP maps and conventional perfusion images yielded a diagnostic accuracy of 84%, sensitivity of 70% and specificity of 93%. The proposed easy-to-use dual-bolus QP CMR workflow provides good image quality and holds promise for high accuracy in diagnosis of obstructive CAD. Implementation of this approach has the potential to serve as an alternative to current methods thus increasing the accessibility to offer high-quality QP CMR imaging by a wide range of CMR laboratories.

Underlying mechanisms and cardioprotective effects of SGLT2i and GLP-1Ra: insights from cardiovascular magnetic resonance

Originally designed as anti-hyperglycemic drugs, Glucagon-Like Peptide-1 receptor agonists (GLP-1Ra) and Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have demonstrated protective cardiovascular effects, with significant impact on cardiovascular morbidity and mortality. Despite several mechanisms have been proposed, the exact pathophysiology behind these effects is not yet fully understood. Cardiovascular imaging is key for the evaluation of diabetic patients, with an established role from the identification of early subclinical changes to long-term follow up and prognostic assessment. Among the different imaging modalities, CMR may have a key-role being the gold standard for volumes and function assessment and having the unique ability to provide tissue characterization. Novel techniques are also implementing the possibility to evaluate cardiac metabolism through CMR and thereby further increasing the potential role of the modality in this context. Aim of this paper is to provide a comprehensive review of changes in CMR parameters and novel CMR techniques applied in both pre-clinical and clinical studies evaluating the effects of SGLT2i and GLP-1Ra, and their potential role in better understanding the underlying CV mechanisms of these drugs.

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

Myocardial perfusion in cardiac amyloidosis

AIMS

Cardiac involvement is the main driver of clinical outcomes in systemic amyloidosis and preliminary studies support the hypothesis that myocardial ischaemia contributes to cellular damage. The aims of this study were to assess the presence and mechanisms of myocardial ischaemia using cardiovascular magnetic resonance (CMR) with multiparametric mapping and histopathological assessment.

METHODS AND RESULTS

Ninety-three patients with cardiac amyloidosis (CA) (light-chain amyloidosis n = 42, transthyretin amyloidosis n = 51) and 97 without CA (three-vessel coronary disease [3VD] n = 47, unobstructed coronary arteries n = 26, healthy volunteers [HV] n = 24) underwent quantitative stress perfusion CMR with myocardial blood flow (MBF) mapping. Twenty-four myocardial biopsies and three explanted hearts with CA were analysed histopathologically. Stress MBF was severely reduced in patients with CA with lower values than patients with 3VD, unobstructed coronary arteries and HV (CA: 1.04 ± 0.51 ml/min/g, 3VD: 1.35 ± 0.50 ml/min/g, unobstructed coronary arteries: 2.92 ± 0.52 ml/min/g, HV: 2.91 ± 0.73 ml/min/g; CA vs. 3VD p = 0.011, CA vs. unobstructed coronary arteries p < 0.001, CA vs. HV p < 0.001). Myocardial perfusion abnormalities correlated with amyloid burden, systolic and diastolic function, structural parameters and blood biomarkers (p < 0.05). Biopsies demonstrated abnormal vascular endothelial growth factor staining in cardiomyocytes and endothelial cells, which may be related to hypoxia conditions. Amyloid infiltration in intramural arteries was associated with severe lumen reduction and severe reduction in capillary density.

CONCLUSION

Cardiac amyloidosis is associated with severe inducible myocardial ischaemia demonstrable by histology and CMR stress perfusion mapping. Histological evaluation indicates a complex pathophysiology, where in addition to systolic and diastolic dysfunction, amyloid infiltration of the epicardial arteries and disruption and rarefaction of the capillaries play a role in contributing to myocardial ischaemia.

Automatic calculation of myocardial perfusion reserve using deep learning with uncertainty quantification

Background

Myocardial perfusion reserve index (MPRI) in magnetic resonance imaging (MRI) is an important indicator of ischemia, and its measurement typically involves manual procedures. The purposes of this study were to develop a fully automatic method for estimating the MPRI and to evaluate its performance.

Methods

The method consisted of segmenting the myocardium in dynamic contrast-enhanced (DCE) myocardial perfusion MRI data using Monte Carlo dropout U-Net, dividing the myocardium into segments based on landmark localization with machine learning, and estimating the MPRI after the calculation of the left ventricular and myocardial contrast upslopes. The proposed method was compared with a reference method, which involved manual adjustments of the myocardial contours and upslope ranges.

Results

In test subjects, MPRIs measured by the proposed technique correlated with those by the manual reference in segmental assessment [intraclass correlation coefficient (ICC) =0.75, 95% CI: 0.70-0.79, P<0.001]. The automatic and reference MPRI values showed a mean difference of -0.02 and 95% limits of agreement of (-0.86, 0.82).

Conclusions

The proposed automatic method is based on deep learning segmentation and machine learning landmark detection for MPRI measurements in DCE perfusion MRI. It holds the potential to efficiently and quantitatively assess myocardial ischemia without any user's interaction.

Integrated myocardial flow reserve (iMFR) assessment: diffuse atherosclerosis and microvascular dysfunction are more strongly associated with mortality than focally impaired perfusion

BACKGROUND AND AIMS

Although treatment of ischemia-causing epicardial stenoses may improve symptoms of ischemia, current evidence does not suggest that revascularization improves survival. Conventional myocardial ischemia imaging does not uniquely identify diffuse atherosclerosis, microvascular dysfunction, or nonobstructive epicardial stenoses. We sought to evaluate the prognostic value of integrated myocardial flow reserve (iMFR), a novel noninvasive approach to distinguish the perfusion impact of focal atherosclerosis from diffuse coronary disease.

METHODS

This study analyzed a large single-center registry of consecutive patients clinically referred for rest-stress myocardial perfusion positron emission tomography. Cox proportional hazards modeling was used to assess the association of two previously reported and two novel perfusion measures with mortality risk: global stress myocardial blood flow (MBF); global myocardial flow reserve (MFR); and two metrics derived from iMFR analysis: the extents of focal and diffusely impaired perfusion.

RESULTS

In total, 6867 patients were included with a median follow-up of 3.4 years [1st-3rd quartiles, 1.9-5.0] and 1444 deaths (21%). Although all evaluated perfusion measures were independently associated with death, diffusely impaired perfusion extent (hazard ratio 2.65, 95%C.I. [2.37-2.97]) and global MFR (HR 2.29, 95%C.I. [2.08-2.52]) were consistently stronger predictors than stress MBF (HR 1.62, 95%C.I. [1.46-1.79]). Focally impaired perfusion extent (HR 1.09, 95%C.I. [1.03-1.16]) was only moderately related to mortality. Diffusely impaired perfusion extent remained a significant independent predictor of death when combined with global MFR (p < 0.0001), providing improved risk stratification (overall net reclassification improvement 0.246, 95%C.I. [0.183-0.310]).

CONCLUSIONS

The extent of diffusely impaired perfusion is a strong independent and additive marker of mortality risk beyond traditional risk factors, standard perfusion imaging, and global MFR, while focally impaired perfusion is only moderately related to mortality.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

The coronary microvascular angina cardiovascular magnetic resonance imaging trial: Rationale and design

BACKGROUND

Coronary microvascular dysfunction may cause myocardial ischemia with no obstructive coronary artery disease (INOCA). If functional testing is not performed INOCA may pass undetected. Stress perfusion cardiovascular MRI (CMR) quantifies myocardial blood flow (MBF) but the clinical utility of stress CMR in the management of patients with suspected angina with no obstructive coronary arteries (ANOCA) is uncertain.

OBJECTIVES

First, to undertake a diagnostic study using stress CMR in patients with ANOCA following invasive coronary angiography and, second, in a nested, double-blind, randomized, controlled trial to assess the effect of disclosure on the final diagnosis and health status in the longer term.

DESIGN

All-comers referred for clinically indicated coronary angiography for the investigation of suspected coronary artery disease will be screened in 3 regional centers in the United Kingdom. Following invasive coronary angiography, patients with ANOCA who provide informed consent will undergo noninvasive endotyping using stress CMR within 3 months of the angiogram.

DIAGNOSTIC STUDY

Stress perfusion CMR imaging to assess the prevalence of coronary microvascular dysfunction and clinically significant incidental findings in patients with ANOCA. The primary outcome is the between-group difference in the reclassification rate of the initial diagnosis based on invasive angiography versus the final diagnosis after CMR imaging.

RANDOMIZED, CONTROLLED TRIAL

Participants will be randomized to inclusion (intervention group) or exclusion (control group) of myocardial blood flow to inform the final diagnosis. The primary outcome of the clinical trial is the mean within-subject change in the Seattle Angina Questionnaire summary score (SAQSS) at 6 months. Secondary outcome assessments include the EUROQOL EQ-5D-5L questionnaire, the Brief Illness Perception Questionnaire (Brief-IPQ), the Treatment Satisfaction Questionnaire (TSQM-9), the Patient Health Questionnaire-4 (PHQ-4), the Duke Activity Status Index (DASI), the International Physical Activity Questionnaire- Short Form (IPAQ-SF), the Montreal Cognitive Assessment (MOCA) and the 8-item Productivity Cost Questionnaire (iPCQ). Health and economic outcomes will be assessed using electronic healthcare records.

VALUE

To clarify if routine stress perfusion CMR imaging reclassifies the final diagnosis in patients with ANOCA and whether this strategy improves symptoms, health-related quality of life and health economic outcomes.

CLINICALTRIALS

GOV: NCT04805814.

Focal and diffuse myocardial fibrosis both contribute to regional hypoperfusion assessed by post-processing quantitative-perfusion MRI techniques

Introduction

Indications for stress-cardiovascular magnetic resonance imaging (CMR) to assess myocardial ischemia and viability are growing. First pass perfusion and late gadolinium enhancement (LGE) have limited value in balanced ischemia and diffuse fibrosis. Quantitative perfusion (QP) to assess absolute pixelwise myocardial blood flow (MBF) and extracellular volume (ECV) as a measure of diffuse fibrosis can overcome these limitations. We investigated the use of post-processing techniques for quantifying both pixelwise MBF and diffuse fibrosis in patients with clinically indicated CMR stress exams. We then assessed if focal and diffuse myocardial fibrosis and other features quantified during the CMR exam explain individual MBF findings.

Methods

This prospective observational study enrolled 125 patients undergoing a clinically indicated stress-CMR scan. In addition to the clinical report, MBF during regadenoson-stress was quantified using a post-processing QP method and T1 maps were used to calculate ECV. Factors that were associated with poor MBF were investigated.

Results

Of the 109 patients included (66 ± 11 years, 32% female), global and regional perfusion was quantified by QP analysis in both the presence and absence of visual first pass perfusion deficits. Similarly, ECV analysis identified diffuse fibrosis in myocardium beyond segments with LGE. Multivariable analysis showed both LGE (β = -0.191, p = 0.001) and ECV (β = -0.011, p < 0.001) were independent predictors of reduced MBF. In patients without clinically defined first pass perfusion deficits, the microvascular risk-factors of age and wall thickness further contributed to poor MBF (p < 0.001).

Discussion

Quantitative analysis of MBF and diffuse fibrosis detected regional tissue abnormalities not identified by traditional visual assessment. Multi-parametric quantitative analysis may refine the work-up of the etiology of myocardial ischemia in patients referred for clinical CMR stress testing in the future and provide a deeper insight into ischemic heart disease.

2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

AIM

The "2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease" provides an update to and consolidates new evidence since the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease" and the corresponding "2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease."

METHODS

A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

STRUCTURE

This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost-value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.

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.

Multimodality Cardiac Imaging in COVID

Infection with SARS-CoV-2, the virus that causes COVID, is associated with numerous potential secondary complications. Global efforts have been dedicated to understanding the myriad potential cardiovascular sequelae which may occur during acute infection, convalescence, or recovery. Because patients often present with nonspecific symptoms and laboratory findings, cardiac imaging has emerged as an important tool for the discrimination of pulmonary and cardiovascular complications of this disease. The clinician investigating a potential COVID-related complication must account not only for the relative utility of various cardiac imaging modalities but also for the risk of infectious exposure to staff and other patients. Extraordinary clinical and scholarly efforts have brought the international medical community closer to a consensus on the appropriate indications for diagnostic cardiac imaging during this protracted pandemic. In this review, we summarize the existing literature and reference major societal guidelines to provide an overview of the indications and utility of echocardiography, nuclear imaging, cardiac computed tomography, and cardiac magnetic resonance imaging for the diagnosis of cardiovascular complications of COVID.

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.

Abnormal left ventricular subendocardial perfusion and diastolic function in women with obesity and heart failure and preserved ejection fraction

PURPOSE

- Coronary microvascular dysfunction (CMD) is common in patients with heart failure with preserved ejection fraction (HFpEF) and obesity. Stress cardiovascular magnetic resonance (CMR) has been proposed as a non-invasive tool for detection of CMD. The aim of this study was to determine relationship between CMD and diastolic function in patients with HFpEF using a novel CMR technique.

METHODS

- Patients with obesity and HFpEF without epicardial coronary artery disease (CAD) underwent Doppler echocardiography to measure diastolic function, followed by vasodilator stress CMR, using a single bolus, dual sequence, quantitative myocardial perfusion mapping to measure myocardial blood flow (MBF) at rest and at peak hyperemia. With this, myocardial perfusion reserve (MPR), global stress endocardial-to-epicardial (endo:epi) perfusion ratio, and total ischemic burden (IB, defined as myocardial segments with MBF < 1.94 mL/min/g) were calculated. Results are reported as median and interquartile range.

RESULTS

- Nineteen subjects were enrolled (100% female, 42% Black). Median age was 64 [56-72] years. Global stress MBF was 2.43 ml/min/g [2.16-2.78] and global myocardial perfusion reserve (MPR) was 2.34 [2.07-2.88]. All had an abnormal subendocardial perfusion with an endo:epi of less than 1 (0.87 [0.81-0.90]). Regional myocardial hypoperfusion was detected in 14 (74%) patients with an IB of 6% [0-34.4]. Endo:epi ratio significantly correlated with IB (R=-0.510, p = 0.026) and measures of diastolic function (R = 0.531, p = 0.019 and R=-0.544, p = 0.014 for e' and E/e' respectively).

CONCLUSION

- Using a novel quantitative stress CMR myocardial perfusion mapping technique, women with obesity and HFpEF were found to have patterns of abnormal subendocardial perfusion which significantly correlated with measures of diastolic dysfunction.

Strengths and weaknesses of alternative noninvasive imaging approaches for microvascular ischemia

Structural and functional abnormalities of coronary microvasculature are highly prevalent in several clinical settings and often associated with worse clinical outcomes. Therefore, there is a growing interest in the detection and treatment of this, often overlooked, disease. Coronary angiography allows the assessment of the Coronary flow reserve (CFR) and the index of microcirculatory resistance (IMR). However, the measurement of these parameters is not always feasible because of limited technical availability and the need for a cardiac catheterization with a small but real risk of potential complications. Recent advances in non-invasive imaging techniques allow the assessment of coronary microvascular function with good accuracy and reproducibility. The objective of this review is to discuss the strengths and weaknesses of alternative non-invasive approaches used in the diagnosis of coronary microvascular dysfunction (CMD), highlighting the most recent advances for each imaging modality.

Prognostic implications of unrecognized myocardial infarction before elective percutaneous coronary intervention

Unrecognized myocardial infarction (UMI) is associated with adverse outcomes. This prospective, single-center study elucidated the prevalence and prognostic significance of UMI before elective percutaneous coronary intervention (PCI) using delayed-enhancement cardiac magnetic resonance (DE-CMR). We enrolled 236 patients with stable coronary artery disease who underwent DE-CMR before elective PCI. The prevalence of UMI and the association of clinical and CMR-derived variables with major adverse cardiac events (MACE), defined as cardiovascular death, nonfatal MI, hospitalization for congestive heart failure, and unplanned late revascularization, were assessed. Final analysis revealed that 63/213 (29.6%) patients had UMI. Target territory UMI was observed in 38 patients (17.8% of the total cohort, 60.3% of patients with UMI). UMI was significantly associated with sex, diabetes mellitus, left ventricular ejection fraction, SYNTAX score, and fractional flow reserve in the target vessels. During follow-up (median, 23 months), MACE occurred in 17 (27.0%) patients with UMI and 17 (11.3%) without UMI (P = 0.001). Multivariable modeling revealed that UMI (hazard ratio: 2.18, 95%CI, 1.10-4.33, P = 0.001) was an independent predictor of MACE. Kaplan-Meier analysis indicated that the presence of UMI was significantly associated with a higher incidence of MACE. UMI was independently associated with a greater risk of MACE after successful PCI.

Phenotype-based management of coronary microvascular dysfunction

40-70% of patients undergoing invasive coronary angiography with signs and symptoms of ischemia are found to have no obstructive coronary artery disease (INOCA). When this heterogeneous group undergo coronary function testing, approximately two-thirds have demonstrable coronary microvascular dysfunction (CMD), which is independently associated with adverse prognosis. There are four distinct phenotypes, or subgroups, each with unique pathophysiological mechanisms and responses to therapies. The clinical phenotypes are microvascular angina, vasospastic angina, mixed (microvascular and vasospastic), and non-cardiac symptoms (reclassification as non-INOCA). The Coronary Vasomotor Disorders International Study Group (COVADIS) have proposed standardized criteria for diagnosis. There is growing awareness of these conditions among clinicians and within guidelines. Testing for CMD can be done using invasive or non-invasive modalities. The CorMicA study advocates the concept of 'functional angiography' to guide stratified medical therapy. Therapies broadly fall into two categories: those that modulate cardiovascular risk and those to alleviate angina. Management should be tailored to the individual, with periodic reassessment for efficacy. Phenotype-based management is a worthy endeavor for both patients and clinicians, aligning with the concept of 'precision medicine' to improve prognosis, symptom burden, and quality of life. Here, we present a contemporary approach to the phenotype-based management of patients with INOCA.

Multi-modality cardiac imaging in the management of diabetic heart disease

Diabetic heart disease is a major healthcare problem. Patients with diabetes show an excess of death from cardiovascular causes, twice as high as the general population and those with diabetes type 1 and longer duration of the disease present with more severe cardiovascular complications. Premature coronary artery disease and heart failure are leading causes of morbidity and reduced life expectancy. Multimodality cardiac imaging, including echocardiography, cardiac computed tomography, nuclear medicine, and cardiac magnetic resonance play crucial role in the diagnosis and management of different pathologies included in the definition of diabetic heart disease. In this review we summarise the utility of multi-modality cardiac imaging in characterising ischaemic and non-ischaemic causes of diabetic heart disease and give an overview of the current clinical practice. We also describe emerging imaging techniques enabling early detection of coronary artery inflammation and the non-invasive characterisation of the atherosclerotic plaque disease. Furthermore, we discuss the role of MRI-derived techniques in studying altered myocardial metabolism linking diabetes with the development of diabetic cardiomyopathy. Finally, we discuss recent data regarding the use of artificial intelligence applied to large imaging databases and how those efforts can be utilised in the future in screening of patients with diabetes for early signs of disease.

State-of-the-Art of Myocardial Perfusion by CMR: A Practical View

Ischemic heart disease (IHD) outstands among diseases threatening public health. Essential for its management are the continuous advances in medical and interventional therapies, although a prompt and accurate diagnosis and prognostic stratification are equally important. Besides information on the anatomy of coronary arteries, well covered nowadays by invasive and non-invasive angiographic techniques, there are also other components of the disease with clinical impact, as the presence of myocardial necrosis, the extent of pump function impairment, and the presence and extent of inducible myocardial ischemia, that must be considered in every patient. Cardiovascular Magnetic Resonance (CMR) is a multiparametric diagnostic imaging technique that provides reliable information on these issues. Regarding the detection and grading of inducible ischemia in particular, the technique has been widely adopted in the form of myocardial perfusion sequences under vasodilator stress, which is the subject of this review. While the analysis of images is conventionally performed by visual inspection of dynamic first-pass studies, with the inherent dependency on the operator capability, the recent introduction of a reliable application of quantitative perfusion (QP) represents a significant advance in the field. QP is based on a dual-sequence strategy for conversion of signal intensities into contrast agent concentration units and includes a full automatization of processes such as myocardial blood flow (MBF) calculation (in mL/min/g), generation of a pixel-wise flow mapping, myocardial segmentation, based on machine learning, and allocation of MBF values to myocardial segments. The acquisition of this protocol during induced vasodilation and at rest gives values of stress/rest MBF (in mL/min/g) and myocardial perfusion reserve (MPR), both global and per segment. Dual-sequence QP has been successfully validated against different reference methods, and its prognostic value has been shown in large longitudinal studies. The fact of the whole process being automated, without operator interaction, permits to conceive new interesting scenarios of integration of CMR into systems of entirely automated diagnostic workflow in patients with IHD.

Cardiac MRI assessment of anthracycline-induced cardiotoxicity

The objective of this review article is to discuss how cardiovascular magnetic resonance (CMR) imaging measures left ventricular (LV) function, characterizes tissue, and identifies myocardial fibrosis in patients receiving anthracycline-based chemotherapy (Anth-bC). Specifically, CMR can measure LV ejection fraction (EF), volumes at end-diastole (LVEDV), and end-systole (LVESV), LV strain, and LV mass. Tissue characterization is accomplished through T1/T2-mapping, late gadolinium enhancement (LGE), and CMR perfusion imaging. Despite CMR’s accuracy and efficiency in collecting data about the myocardium, there are challenges that persist while monitoring a cardio-oncology patient undergoing Anth-bC, such as the presence of other cardiovascular risk factors and utility controversies. Furthermore, CMR can be a useful adjunct during cardiopulmonary exercise testing to pinpoint cardiovascular mediated exercise limitations, as well as to assess myocardial microcirculatory damage in patients undergoing Anth-bC.

The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review

Background and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac magnetic resonance imaging (CMR) remains unclear. This systematic review assesses the existing literature on AI in CMR to predict outcomes in patients with cardiovascular disease. Materials and Methods: Medline and Embase were searched for studies published up to November 2021. Any study assessing outcome prediction using AI in CMR in patients with cardiovascular disease was eligible for inclusion. All studies were assessed for compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: A total of 5 studies were included, with a total of 3679 patients, with 225 deaths and 265 major adverse cardiovascular events. Three methods demonstrated high prognostic accuracy: (1) three-dimensional motion assessment model in pulmonary hypertension (hazard ratio (HR) 2.74, 95%CI 1.73−4.34, p < 0.001), (2) automated perfusion quantification in patients with coronary artery disease (HR 2.14, 95%CI 1.58−2.90, p < 0.001), and (3) automated volumetric, functional, and area assessment in patients with myocardial infarction (HR 0.94, 95%CI 0.92−0.96, p < 0.001). Conclusion: There is emerging evidence of the prognostic role of AI in predicting outcomes for three-dimensional motion assessment in pulmonary hypertension, ischaemia assessment by automated perfusion quantification, and automated functional assessment in myocardial infarction.

Definition and epidemiology of coronary microvascular disease

Ischemic heart disease remains one of the leading causes of death and disability worldwide. However, most patients referred for a noninvasive computed tomography coronary angiogram (CTA) or invasive coronary angiogram for the investigation of angina do not have obstructive coronary artery disease (CAD). Approximately two in five referred patients have coronary microvascular disease (CMD) as a primary diagnosis and, in addition, CMD also associates with CAD and myocardial disease (dual pathology). CMD underpins excess morbidity, impaired quality of life, significant health resource utilization, and adverse cardiovascular events. However, CMD often passes undiagnosed and the onward management of these patients is uncertain and heterogeneous. International standardized diagnostic criteria allow for the accurate diagnosis of CMD, ensuring an often overlooked patient population can be diagnosed and stratified for targeted medical therapy. Key to this is assessing coronary microvascular function-including coronary flow reserve, coronary microvascular resistance, and coronary microvascular spasm. This can be done by invasive methods (intracoronary temperature-pressure wire, intracoronary Doppler flow-pressure wire, intracoronary provocation testing) and non-invasive methods [positron emission tomography (PET), cardiac magnetic resonance imaging (CMR), transthoracic Doppler echocardiography (TTDE), cardiac computed tomography (CT)]. Coronary CTA is insensitive for CMD. Functional coronary angiography represents the combination of CAD imaging and invasive diagnostic procedures.

Diagnosis and Nursing Intervention of Gynecological Ovarian Endometriosis with Magnetic Resonance Imaging under Artificial Intelligence Algorithm

This research was aimed to study the application value of the magnetic resonance imaging (MRI) diagnosis under artificial intelligence algorithms and the effect of nursing intervention on patients with gynecological ovarian endometriosis. 116 patients with ovarian endometriosis were randomly divided into a control group (routine nursing) and an experimental group (comprehensive nursing), with 58 cases in each group. The artificial intelligence fuzzy C-means (FCM) clustering algorithm was proposed and used in the MRI diagnosis of ovarian endometriosis. The application value of the FCM algorithm was evaluated through the accuracy, Dice, sensitivity, and specificity of the imaging diagnosis, and the nursing satisfaction and the incidence of adverse reactions were used to evaluate the effect of nursing intervention. The results showed that, compared with the traditional hard C-means (HCM) algorithm, the artificial intelligence FCM algorithm gave a significantly higher partition coefficient, and its partition entropy and running time were significantly reduced, with significant differences (P < 0.05). The average values of Dice, sensitivity, and specificity of patients' MRI images were 0.77, 0.73, and 0.72, respectively, which were processed by the traditional HCM algorithm, while those values obtained by the improved artificial intelligence FCM algorithm were 0.92, 0.90, and 0.93, respectively; all the values were significantly improved (P < 0.05). In addition, the accuracy of MRI diagnosis based on the artificial intelligence FCM algorithm was 94.32 ± 3.05%, which was significantly higher than the 81.39 ± 3.11% under the HCM algorithm (P < 0.05). The overall nursing satisfaction of the experimental group was 96.5%, which was significantly better than the 87.9% of the control group (P < 0.05). The incidence of postoperative adverse reactions in the experimental group (7.9%) was markedly lower than that in the control group (24.1%), with a significant difference (P < 0.05). In short, MRI images under the artificial intelligence FCM algorithm could greatly improve the clinical diagnosis of ovarian endometriosis, and the comprehensive nursing intervention would also improve the prognosis and recovery of patients.

Assessment and Treatment for Coronary Microvascular Dysfunction by Contrast Enhanced Ultrasound

With growing evidence in clinical practice, the understanding of coronary syndromes has gradually evolved out of focusing on the well-established link between stenosis of epicardial coronary artery and myocardial ischemia to the structural and functional abnormalities at the level of coronary microcirculation, known as coronary microvascular dysfunction (CMD). CMD encompasses several pathophysiological mechanisms of coronary microcirculation and is considered as an important cause of myocardial ischemia in patients with angina symptoms without obstructive coronary artery disease (CAD). As a result of growing knowledge of the understanding of CMD assessed by multiple non-invasive modalities, CMD has also been found to be involved in other cardiovascular diseases, including primary cardiomyopathies as well as heart failure with preserved ejection fraction (HFpEF). In the past 2 decades, almost all the imaging modalities have been used to non-invasively quantify myocardial blood flow (MBF) and promote a better understanding of CMD. Myocardial contrast echocardiography (MCE) is a breakthrough as a non-invasive technique, which enables assessment of myocardial perfusion and quantification of MBF, exhibiting promising diagnostic performances that were comparable to other non-invasive techniques. With unique advantages over other non-invasive techniques, MCE has gradually developed into a novel modality for assessment of the coronary microvasculature, which may provide novel insights into the pathophysiological role of CMD in different clinical conditions. Moreover, the sonothrombolysis and the application of artificial intelligence (AI) will offer the opportunity to extend the use of contrast ultrasound theragnostics.

Highlights of the Virtual Society for Cardiovascular Magnetic Resonance 2022 Scientific Conference: CMR: improving cardiovascular care around the world

The 25th Society for Cardiovascular Magnetic Resonance (SCMR) Annual Scientific Sessions saw 1524 registered participants from more than 50 countries attending the meeting virtually. Supporting the theme "CMR: Improving Cardiovascular Care Around the World", the meeting included 179 invited talks, 52 sessions including 3 plenary sessions, 2 keynote talks, and a total of 93 cases and 416 posters. The sessions were designed so as to showcase the multifaceted role of cardiovascular magnetic resonance (CMR) in identifying and prognosticating various myocardial pathologies. Additionally, various social networking sessions as well as fun activities were organized. The major areas of focus for the future are likely to be rapid efficient and high value CMR exams, automated and quantitative acquisition and post-processing using artificial intelligence and machine learning, multi-contrast imaging and advanced vascular imaging including 4D flow.

Prognostic value of early left ventricular ejection fraction reserve during regadenoson stress solid-state SPECT-MPI

BACKGROUND

We hypothesized early post-stress left ventricular ejection fraction reserve (EFR) on solid-state-SPECT is associated with major cardiac adverse events (MACE).

METHODS

151 patients (70 ± 12 years, male 50%) undergoing same-day rest/regadenoson stress 99mTc-sestamibi solid-state SPECT were followed for MACE. Rest imaging was performed in the upright and supine positions. Early stress imaging was started 2 minutes after the regadenoson injection in the supine position and followed by late stress acquisition in the upright position. Total perfusion deficit (TPD) and functional parameters were quantified automatically. EFR, ∆end-diastolic volume (EDV), and end-systolic volume (ESV) were calculated as the difference between stress and rest values in the same position. EFR < 0%, ∆EDV ≥ 5 ml, or ∆ESV ≥ 5 ml was defined as abnormal.

RESULTS

During the follow-up (mean 3.2 years), 28 MACE occurred (19%). In Kaplan-Meier analysis, there was a significantly decreased event-free survival in patients with early EFR < 0% (P = 0.004). Similarly, there was a decreased event-free survival in patients with ∆ESV ≥ 5 ml at early stress (P = 0.003). However, EFR, ∆EDV, and ∆ESV at late stress were not associated with MACE-free survival. Cox proportional hazards model adjusting for clinical information and stress TPD demonstrated that EFR, ∆EDV, and ∆ESV at early stress were significantly associated with MACE (P < 0.05 for all).

CONCLUSIONS

Reduced early post-stress EFR on vasodilator stress solid-state SPECT is associated with MACE.

Myocardial microvascular function assessed by CMR first-pass perfusion in patients treated with chemotherapy for gynecologic malignancies

OBJECTIVES

Cancer chemotherapy potentially increases the risk of myocardial ischemia. This study assessed myocardial microvascular function by cardiac magnetic resonance (CMR) first-pass perfusion in patients treated with chemotherapy for gynecologic malignancies.

METHODS

A total of 81 patients treated with chemotherapy for gynecologic malignancies and 39 healthy volunteers were prospectively enrolled and underwent CMR imaging. Among the patients, 32 completed CMR follow-up, with a median interval of 6 months. The CMR sequences comprised cardiac cine, rest first-pass perfusion, and late gadolinium enhancement.

RESULTS

There were no significant differences in the baseline characteristics between the patients and normal controls (all p > 0.05). Compared with the normal controls, the patients had a lower myocardial perfusion index (PI) (13.62 ± 2.01% vs. 12% (11 to 14%), p = 0.001) but demonstrated no significant variation with an increase in the number of chemotherapy cycles at follow-up (11.79 ± 2.36% vs. 11.19 ± 2.19%, p = 0.234). In multivariate analysis with adjustments for clinical confounders, a decrease in the PI was independently associated with chemotherapy treatment (β = - 0.362, p = 0.002) but had no correlation with the number of chemotherapy cycles (r = - 0.177, p = 0.053).

CONCLUSION

Myocardial microvascular dysfunction was associated with chemotherapy treatment in patients with gynecologic malignancies, and can be assessed and monitored by rest CMR first-pass perfusion.

KEY POINTS

• Chemotherapy was associated with but did not aggravate myocardial microvascular dysfunction in patients with gynecologic malignancies. • Rest CMR first-pass perfusion is an ideal modality for assessing and monitoring alterations in myocardial microcirculation during chemotherapy treatment.

High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration

Introduction

To develop and test the feasibility of free-breathing (FB), high-resolution quantitative first-pass perfusion cardiac MR (FPP-CMR) using dual-echo Dixon (FOSTERS; Fat-water separation for mOtion-corrected Spatio-TEmporally accelerated myocardial peRfuSion).

Materials and Methods

FOSTERS was performed in FB using a dual-saturation single-bolus acquisition with dual-echo Dixon and a dynamically variable Cartesian k-t undersampling (8-fold) approach, with low-rank and sparsity constrained reconstruction, to achieve high-resolution FPP-CMR images. FOSTERS also included automatic in-plane motion estimation and T2* correction to obtain quantitative myocardial blood flow (MBF) maps. High-resolution (1.6 x 1.6 mm²) FB FOSTERS was evaluated in eleven patients, during rest, against standard-resolution (2.6 x 2.6 mm²) 2-fold SENSE-accelerated breath-hold (BH) FPP-CMR. In addition, MBF was computed for FOSTERS and spatial wavelet-based compressed sensing (CS) reconstruction. Two cardiologists scored the image quality (IQ) of FOSTERS, CS, and standard BH FPP-CMR images using a 4-point scale (1–4, non-diagnostic – fully diagnostic).

Results

FOSTERS produced high-quality images without dark-rim and with reduced motion-related artifacts, using an 8x accelerated FB acquisition. FOSTERS and standard BH FPP-CMR exhibited excellent IQ with an average score of 3.5 ± 0.6 and 3.4 ± 0.6 (no statistical difference, p > 0.05), respectively. CS images exhibited severe artifacts and high levels of noise, resulting in an average IQ score of 2.9 ± 0.5. MBF values obtained with FOSTERS presented a lower variance than those obtained with CS.

Discussion

FOSTERS enabled high-resolution FB FPP-CMR with MBF quantification. Combining motion correction with a low-rank and sparsity-constrained reconstruction results in excellent image quality.

Mechanistic study of the effect of Endothelin SNPs in microvascular angina – Protocol of the PRIZE Endothelin Sub-Study

•

Microvascular angina is a common cause of ischemia with non-obstructive coronary arteries (INOCA).

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Endothelin-1 (ET-1) is a potent vasoconstrictor implicated in the pathophysiology of microvascular angina.

•

Zibotentan, an Endothelin Receptor Antagonist is being tested as a treatment for microvascular angina in the ‘PRIZE’ trial using a genetic ‘precision medicine’ approach.

•

The PRIZE ET Sub-study will provide a comprehensive genotype and phenotype bio-resource for microvascular angina patients.

Introduction

Microvascular angina is a common cause of ischemia with non-obstructive coronary arteries (INOCA) and limited therapeutic options are available to those affected. Endothelin-1 (ET-1) is a potent vasoconstrictor implicated in the pathophysiology of microvascular angina. A large randomised, double blinded, placebo controlled crossover trial, the PRecIsion medicine with ZibotEntan in microvascular angina (PRIZE) trial is currently underway, investigating an endothelin receptor antagonist – Zibotentan, as a new drug treatment for microvascular angina. The trial uses a 'precision medicine' approach by preferential selection of those with higher ET-1 expression conferred by the PHACTR1 minor G allele single nucleotide polymorphism (SNP). The incidence of this SNP occurs in approximately one third of the population therefore a considerable number of screened patients will be ineligible for randomisation and the treatment phase of the trial.

Methods

In the PRIZE Endothelin (ET) Sub-Study, patients screened out of the PRIZE trial will be genotyped for other genetic variants in the ET-1 pathway. These will be correlated with phenotypic characteristics including exercise tolerance, angina severity and quantitative measures of microvascular function on cardiovascular MRI as well as mechanistic data on endothelin pathway signalling.

Conclusions

The study will provide a comprehensive genotype and phenotype bio-resource identifying novel ET-1 genotypes to inform the potential wider use of endothelin receptor antagonists for this indication.

Myocardial blood flow is the dominant factor influencing cardiac magnetic resonance adenosine stress T2

Stress imaging identifies ischemic myocardium by comparing hemodynamics during rest and hyperemic stress. Hyperemia affects multiple hemodynamic parameters in myocardium, including myocardial blood flow (MBF), myocardial blood volume (MBV), and venous blood oxygen levels (PvO2 ). Cardiac T2 is sensitive to these changes and therefore is a promising non-contrast option for stress imaging; however, the impact of individual hemodynamic factors on T2 is poorly understood, making the connection from altered T2 to changes within the tissue difficult. To better understand this interplay, we performed T2 mapping and measured various hemodynamic factors independently in healthy pigs at multiple levels of hyperemic stress, induced by different doses of adenosine (0.14-0.56 mg/kg/min). T1 mapping quantified changes in MBV. MBF was assessed with microspheres, and oxygen consumption was determined by the rate pressure product (RPP). Simulations were also run to better characterize individual contributions to T2. Myocardial T2, MBF, oxygen consumption, and MBV all changed to varying extents between each level of adenosine stress (T2 = 37.6-41.8 ms; MBF = 0.48-1.32 mL/min/g; RPP = 6507-4001 bmp*mmHg; maximum percent change in MBV = 1.31%). Multivariable analyses revealed MBF as the dominant influence on T2 during hyperemia (significant β-values >7). Myocardial oxygen consumption had almost no effect on T2 (β-values <0.002); since PvO2 is influenced by both oxygen consumption and MBF, PvO2 changes detected by T2 during adenosine stress can be attributed to MBF. Simulations varying PvO2 and MBV confirmed that PvO2 had the strongest influence on T2, but MBV became important at high PvO2 . Together, these data suggest a model where, during adenosine stress, myocardial T2 responds predominantly to changes in MBF, but at high hyperemia MBV is also influential. Thus, changes in adenosine stress T2 can now be interpreted in terms of the physiological changes that led to it, enabling T2 mapping to become a viable non-contrast option to detect ischemic myocardial tissue.

Prognostic Value of Coronary Sinus Flow Quantification by Cardiac Magnetic Resonance Imaging in Patients With Acute Myocardial Infarction

Background This study aimed to evaluate the prognostic value of hyperemic coronary sinus flow (h-CSF) and global coronary flow reserve (g-CFR) obtained by phase-contrast cine-magnetic resonance imaging in patients with acute myocardial infarction (MI). Methods and Results This retrospective study analyzed patients with acute MI (n=523) who underwent primary (ST-segment-elevation MI) or urgent (non-ST-segment-elevation MI) percutaneous coronary intervention. Absolute coronary sinus blood flow (CSF) at rest and during vasodilator stress hyperemia was quantified at 30 days (24-36 days) after the index infarct-related lesion percutaneous coronary intervention and revascularization of functionally significant non-infarct-related lesions. We used Cox proportional hazards regression modeling to examine the association between h-CSF, g-CFR, and major adverse cardiac events defined as all-cause death, nonfatal MI, hospitalization for congestive heart failure, and stroke. Finally, 325 patients with ST-segment-elevation MI (62.1%) and 198 patients with non-ST-segment-elevation MI (37.9%) were studied over a median follow-up of 2.5 years. The rest CSF, h-CSF, and g-CFR were 0.94 (0.68-1.26) mL/min per g, 2.05 (1.42-2.73) mL/min per g, and 2.17 (1.54-3.03), respectively. Major adverse cardiac events occurred in 62 patients, and Cox proportional hazards analysis showed that h-CSF and g-CFR were independent predictors of major adverse cardiac events (h-CSF: hazard ratio [HR], 0.64; 95% CI, 0.47-0.88; P=0.005; g-CFR: HR, 0.62; 95% CI, 0.47-0.82; P=0.001). When stratified by h-CSF and g-CFR, cardiac event-free survival was the worst in patients with concordantly impaired h-CSF (<1.6 mL/min per g) and g-CFR (<1.7) (P<0.001). Conclusions Global coronary sinus flow quantification using phase-contrast cine-magnetic resonance imaging provided significant prognostic information independent of infarction size and conventional risk factors in patients with acute MI undergoing revascularization.

Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome

Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care.

Multimodality Imaging in Ischemic Chronic Cardiomyopathy

Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.

Non-invasive Ischaemia Testing in Patients With Prior Coronary Artery Bypass Graft Surgery: Technical Challenges, Limitations, and Future Directions

Coronary artery bypass graft (CABG) surgery effectively relieves symptoms and improves outcomes. However, patients undergoing CABG surgery typically have advanced coronary atherosclerotic disease and remain at high risk for symptom recurrence and adverse events. Functional non-invasive testing for ischaemia is commonly used as a gatekeeper for invasive coronary and graft angiography, and for guiding subsequent revascularisation decisions. However, performing and interpreting non-invasive ischaemia testing in patients post CABG is challenging, irrespective of the imaging modality used. Multiple factors including advanced multi-vessel native vessel disease, variability in coronary hemodynamics post-surgery, differences in graft lengths and vasomotor properties, and complex myocardial scar morphology are only some of the pathophysiological mechanisms that complicate ischaemia evaluation in this patient population. Systematic assessment of the impact of these challenges in relation to each imaging modality may help optimize diagnostic test selection by incorporating clinical information and individual patient characteristics. At the same time, recent technological advances in cardiac imaging including improvements in image quality, wider availability of quantitative techniques for measuring myocardial blood flow and the introduction of artificial intelligence-based approaches for image analysis offer the opportunity to re-evaluate the value of ischaemia testing, providing new insights into the pathophysiological processes that determine outcomes in this patient population.

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.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review

The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.

Myocardial Perfusion Imaging After Severe COVID-19 Infection Demonstrates Regional Ischemia Rather Than Global Blood Flow Reduction

Background: Acute myocardial damage is common in severe COVID-19. Post-mortem studies have implicated microvascular thrombosis, with cardiovascular magnetic resonance (CMR) demonstrating a high prevalence of myocardial infarction and myocarditis-like scar. The microcirculatory sequelae are incompletely characterized. Perfusion CMR can quantify the stress myocardial blood flow (MBF) and identify its association with infarction and myocarditis. Objectives: To determine the impact of the severe hospitalized COVID-19 on global and regional myocardial perfusion in recovered patients. Methods: A case-control study of previously hospitalized, troponin-positive COVID-19 patients was undertaken. The results were compared with a propensity-matched, pre-COVID chest pain cohort (referred for clinical CMR; angiography subsequently demonstrating unobstructed coronary arteries) and 27 healthy volunteers (HV). The analysis used visual assessment for the regional perfusion defects and AI-based segmentation to derive the global and regional stress and rest MBF. Results: Ninety recovered post-COVID patients {median age 64 [interquartile range (IQR) 54-71] years, 83% male, 44% requiring the intensive care unit (ICU)} underwent adenosine-stress perfusion CMR at a median of 61 (IQR 29-146) days post-discharge. The mean left ventricular ejection fraction (LVEF) was 67 ± 10%; 10 (11%) with impaired LVEF. Fifty patients (56%) had late gadolinium enhancement (LGE); 15 (17%) had infarct-pattern, 31 (34%) had non-ischemic, and 4 (4.4%) had mixed pattern LGE. Thirty-two patients (36%) had adenosine-induced regional perfusion defects, 26 out of 32 with at least one segment without prior infarction. The global stress MBF in post-COVID patients was similar to the age-, sex- and co-morbidities of the matched controls (2.53 ± 0.77 vs. 2.52 ± 0.79 ml/g/min, p = 0.10), though lower than HV (3.00 ± 0.76 ml/g/min, p< 0.01). Conclusions: After severe hospitalized COVID-19 infection, patients who attended clinical ischemia testing had little evidence of significant microvascular disease at 2 months post-discharge. The high prevalence of regional inducible ischemia and/or infarction (nearly 40%) may suggest that occult coronary disease is an important putative mechanism for troponin elevation in this cohort. This should be considered hypothesis-generating for future studies which combine ischemia and anatomical assessment.

Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm

The purpose of this study is to explore the application value of artificial intelligence algorithm in multimodal MRI image diagnosis of cervical cancer. Based on the traditional convolutional neural network (CNN), an artificial intelligence 3D-CNN algorithm is designed according to the characteristics of cervical cancer. 70 patients with cervical cancer were selected as the experimental group, and 10 healthy people were selected as the reference group. The 3D-CNN algorithm was applied to the diagnosis of clinical cervical cancer multimodal MRI images. The value of the algorithm was comprehensively evaluated by the image quality and diagnostic accuracy. The results showed that compared with the traditional CNN algorithm, the convergence rate of the loss curve of the artificial intelligence 3D-CNN algorithm was accelerated, and the segmentation accuracy of whole-area tumors (WT), core tumor areas (CT), and enhanced tumor areas (ET) was significantly improved. In addition, the clarity of the multimodal MRI image and the recognition performance of the lesion were significantly improved. Under the artificial intelligence 3D-CNN algorithm, the Dice values of WT, ET, and CT regions were 0.78, 0.71, and 0.64, respectively. The sensitivity values were 0.92, 0.91, and 0.88, respectively. The specificity values were 0.93, 0.92, and 0.9 l, respectively. The Hausdorff (Haus) distances were 0.93, 0.92, and 0.90, respectively. The data of various indicators were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of the artificial intelligence 3D-CNN algorithm was 93.11 ± 4.65%, which was also significantly higher than that of the traditional CNN algorithm (82.45 ± 7.54%) (P < 0.05). In summary, the recognition and segmentation ability of multimodal MRI images based on artificial intelligence 3D-CNN algorithm for cervical cancer lesions were significantly improved, which can significantly enhance the clinical diagnosis rate of cervical cancer.

2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

This clinical practice guideline for the evaluation and diagnosis of chest pain provides recommendations and algorithms for clinicians to assess and diagnose chest pain in adult patients.

METHODS

A comprehensive literature search was conducted from November 11, 2017, to May 1, 2020, encompassing randomized and nonrandomized trials, observational studies, registries, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Collaboration, Agency for Healthcare Research and Quality reports, and other relevant databases. Additional relevant studies, published through April 2021, were also considered. Structure: Chest pain is a frequent cause for emergency department visits in the United States. The "2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain" provides recommendations based on contemporary evidence on the assessment and evaluation of chest pain. This guideline presents an evidence-based approach to risk stratification and the diagnostic workup for the evaluation of chest pain. Cost-value considerations in diagnostic testing have been incorporated, and shared decision-making with patients is recommended.

2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

This executive summary of the clinical practice guideline for the evaluation and diagnosis of chest pain provides recommendations and algorithms for clinicians to assess and diagnose chest pain in adult patients.

METHODS

A comprehensive literature search was conducted from November 11, 2017, to May 1, 2020, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Collaboration, Agency for Healthcare Research and Quality reports, and other relevant databases. Additional relevant studies, published through April 2021, were also considered. Structure: Chest pain is a frequent cause for emergency department visits in the United States. The "2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain" provides recommendations based on contemporary evidence on the assessment and evaluation of chest pain. These guidelines present an evidence-based approach to risk stratification and the diagnostic workup for the evaluation of chest pain. Cost-value considerations in diagnostic testing have been incorporated and shared decision-making with patients is recommended.

Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients’ outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the “unhealthy” and “healthy” classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy; the patients’ class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the “healthy” (good outcome) or “unhealthy” (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.

Clinical Application of Dynamic Contrast Enhanced Perfusion Imaging by Cardiovascular Magnetic Resonance

Functionally significant coronary artery disease impairs myocardial blood flow and can be detected non-invasively by myocardial perfusion imaging. While multiple myocardial perfusion imaging modalities exist, the high spatial and temporal resolution of cardiovascular magnetic resonance (CMR), combined with its freedom from ionising radiation make it an attractive option. Dynamic contrast enhanced CMR perfusion imaging has become a well-validated non-invasive tool for the assessment and risk stratification of patients with coronary artery disease and is recommended by international guidelines. This article presents an overview of CMR perfusion imaging and its clinical application, with a focus on chronic coronary syndromes, highlighting its strengths and challenges, and discusses recent advances, including the emerging role of quantitative perfusion analysis.

Stress Cardiac Magnetic Resonance Myocardial Perfusion Imaging: JACC Review Topic of the Week

Stress cardiovascular magnetic resonance imaging (CMR) is a cost-effective, noninvasive test that accurately assesses myocardial ischemia, myocardial viability, and cardiac function without the need for ionizing radiation. There is a large body of literature, including randomized controlled trials, validating its diagnostic performance, risk stratification capabilities, and ability to guide appropriate use of coronary intervention. Specifically, stress CMR has shown higher diagnostic sensitivity than single-photon emission computed tomography imaging in detecting angiographically significant coronary artery disease. Stress CMR is particularly valuable for the evaluation of patients with moderate to high pretest probability of having stable ischemic heart disease and for patients known to have challenging imaging characteristics, including women, individuals with prior revascularization, and those with left ventricular dysfunction. This paper reviews the basics principles of stress CMR, the data supporting its clinical use, the added-value of myocardial blood flow quantification, and the assessment of myocardial function and viability routinely obtained during a stress CMR study.

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.

The New Role of Cardiac Imaging Following the ISCHEMIA Trial

Purpose of Review

This review is aimed at summarizing the recently published ISCHEMIA trial (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) and how its findings may impact cardiac imaging for stable ischemic heart disease (SIHD) moving forward.

Recent Findings

The ISCHEMIA trial compared an initial invasive management strategy with goal of complete coronary revascularization versus an initial medical therapy strategy among stable patients with newly diagnosed moderate to severe myocardial ischemia on non-invasive testing. The trial results showed that an early invasive strategy did not reduce the incidence of major cardiovascular events over 3.2 years of follow-up as compared to optimal medical therapy in patients with SIHD.

Summary

The results of the landmark ISCHEMIA trial solidified the importance of guideline-directed medical therapy and have provided more evidence against the prevailing dogma that moderate to severe ischemia on traditional stress testing mandates coronary revascularization. This trial was not designed to compare different cardiac imaging and stress testing modalities for the assessment of coronary artery disease in patients undergoing their index evaluation for SIHD; however, its design, which included coronary computed tomographic angiography (CCTA) in most patients, and results have generated robust discussion regarding ways to improve non-invasive testing strategies in similar patient populations. We believe that increased utilization of CCTA to identify patients with and without high-risk SIHD, and advanced tests for ischemia, such as positron emission tomography and stress cardiac magnetic resonance imaging, when selected based on individual patient characteristics, may allow for improved decision-making and outcomes.