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Despite developments in circulating biomarker and imaging technology in the assessment of cardiovascular disease, the surveillance and diagnosis of heart transplant rejection has continued to rely on histopathologic interpretation of the endomyocardial biopsy. Increasing evidence shows the utility of molecular evaluations, such as donor-specific antibodies and donor-derived cell-free DNA, as well as advanced imaging techniques, such as cardiac magnetic resonance imaging, in the assessment of rejection, resulting in the elimination of many surveillance endomyocardial biopsies. As non-invasive technologies in heart transplant rejection continue to evolve and are incorporated into practice, they may supplant endomyocardial biopsy even when rejection is suspected, allowing for more precise and expeditious rejection therapy. This review describes the current and near-future states for the evaluation of heart transplant rejection, both in the settings of rejection surveillance and rejection diagnosis. As biomarkers of rejection continue to evolve, rejection risk prediction may allow for a more personalized approach to immunosuppression.

Changes in atrial size and function have historically been considered a surrogate marker of ventricular dysfunction. However, it is now recognized that atrial cardiomyopathy (ACM) may also occur as a primary myocardial disorder. Emerging evidence that ACM is a major risk factor for atrial fibrillation, heart failure, and thromboembolic stroke, has highlighted the significance of this disorder and the need for better assessment of atrial metrics in clinical practice. Key barriers in this regard include a lack of standardized criteria or hierarchy for the diagnosis of ACM and lack of consensus for the most accurate phenotyping methods. In this article we review existing literature on ACM, with a focus on current and future non-invasive imaging methods for detecting abnormalities of atrial structure and function. We discuss the relative advantages and disadvantages of transthoracic echocardiography and cardiac magnetic resonance imaging for assessing a range of parameters, including atrial size and contractile function, strain, tissue characteristics, and epicardial adipose tissue. We will also present the potential application of novel imaging methods such as sphericity index and four- or five-dimensional flow.

AimAcute myocarditis (AM) is a heterogeneous condition with variable estimates of survival. Contemporary criteria for the diagnosis of clinically suspected AM enable non-invasive assessment, resulting in greater sensitivity and more representative cohorts. We aimed to describe the demographic characteristics and long-term outcomes of patients with AM diagnosed using non-invasive criteria.Methods and resultsA total of 199 patients with cardiac magnetic resonance (CMR)-confirmed AM were included. The majority (n = 130, 65%) were male, and the average age was 39 ± 16 years. Half of the patients were White (n = 99, 52%), with the remainder from Black and Minority Ethnic (BAME) groups. The most common clinical presentation was chest pain (n = 156, 78%), with smaller numbers presenting with breathlessness (n = 25, 13%) and arrhythmias (n = 18, 9%). Patients admitted with breathlessness were sicker and more often required inotropes, steroids, and renal replacement therapy (p < 0.001, p < 0.001, and p = 0.01, respectively). Over a median follow-up of 53 (IQR 34–76) months, 11 patients (6%) experienced an adverse outcome, defined as a composite of all-cause mortality, resuscitated cardiac arrest, and appropriate implantable cardioverter defibrillator (ICD) therapy. Patients in the arrhythmia group had a worse prognosis, with a nearly sevenfold risk of adverse events [hazard ratio (HR) 6.97; 95% confidence interval (CI) 1.87–26.00, p = 0.004]. Sex and ethnicity were not significantly associated with the outcome.ConclusionAM is highly heterogeneous with an overall favourable prognosis. Three-quarters of patients with AM present with chest pain, which is associated with a benign prognosis. AM presenting with life-threatening arrhythmias is associated with a higher risk of adverse events.

Congenital coronary anomalies are among the rare disorders of the otherwise normal heart. A 2-year-old toddler was evaluated for de novo heart failure after a flu-like event 2 months before being suspicious of post-Covid-19 dilated cardiomyopathy. The cardiac magnetic resonance (CMR) technique displayed the basal to mid subendocardial to transmural scar, suggestive of an ischemic etiology. Further assessment with CT and invasive angiography confirmed the very uncommon left main coronary artery atresia (LMCAA) as the main cause of the patient's heart failure. This is not only the first reported LMCAA case that had undergone a CMR study but was also initially suspected with characteristic CMR findings.

Late gadolinium enhancement (LGE) with cardiac magnetic resonance (CMR) imaging is the clinical reference for assessment of myocardial scar and focal fibrosis. However, current LGE techniques are confined to imaging of a single cardiac phase, which hampers assessment of scar motility and does not allow cross-comparison between multiple phases. In this work, we investigate a three step approach to obtain cardiac phase-resolved LGE images: (1) Acquisition of cardiac phase-resolved imaging data with varying T1 weighting. (2) Generation of semi-quantitative T1* maps for each cardiac phase. (3) Synthetization of LGE contrast to obtain functional LGE images. The proposed method is evaluated in phantom imaging, six healthy subjects at 3T and 20 patients at 1.5T. Phantom imaging at 3T demonstrates consistent contrast throughout the cardiac cycle with a coefficient of variation of 2.55 ± 0.42%. In-vivo results show reliable LGE contrast with thorough suppression of the myocardial tissue is healthy subjects. The contrast between blood and myocardium showed moderate variation throughout the cardiac cycle in healthy subjects (coefficient of variation 18.2 ± 3.51%). Images were acquired at 40–60 ms and 80 ms temporal resolution, at 3T and 1.5, respectively. Functional LGE images acquired in patients with myocardial scar visualized scar tissue throughout the cardiac cycle, albeit at noticeably lower imaging resolution and noise resilience than the reference technique. The proposed technique bears the promise of integrating the advantages of phase-resolved CMR with LGE imaging, but further improvements in the acquisition quality are warranted for clinical use.

Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.

Paravalvular leak (PVL) is a common complication following transcatheter aortic valve replacement (TAVR). Significant PVL is associated with adverse prognosis, but may be challenging to assess accurately. We report the case of an 81-year-old man with shortness of breath 5 months post TAVR. Echocardiography classified PVL as either moderate or severe depending on the parameter utilized, while angiography found only mild PVL. Cardiac magnetic resonance allowed an exact quantification of regurgitant flow volume, classified as clinically and hemodynamically significant. This case highlights the role of multimodality imaging assessment including cardiac magnetic resonance for a more accurate assessment of PVL severity, especially when other imaging modalities show discordant results.

The development of various antitumor drugs has significantly improved the survival of patients with cancer. Many first-line chemotherapy drugs are cytotoxic and the cardiotoxicity is one of the most significant effects that could leads to poor prognosis and decreased survival rate. Cancer treatment include traditional anthracycline drugs, as well as some new targeted drugs such as trastuzumab and ICIs. These drugs may directly or indirectly cause cardiovascular injury through different mechanisms, and lead to increasing the risk of cardiovascular disease or accelerating the development of cardiovascular disease. Cardiotoxicity is clinically manifested by arrhythmia, decreased cardiac function, or even sudden death. The cardiotoxicity caused by traditional chemotherapy drugs such as anthracyclines are significantly known. The cardiotoxicity of some new antitumor drugs such like immune checkpoint inhibitors (ICIs) is also relatively clear and requiring further observation and verification. This review is focused on major three drugs with relatively high incidence of cardiotoxicity and poor prognosis and intended to provide an update on the clinical complications and outcomes of these drugs, and we innovatively summarize the monitoring status of survivors using these drugs and discuss the biomarkers and non-invasive imaging features to identify early cardiotoxicity. Finally, we summarize the prevention that decreasing antitumor drugs-induced cardiotoxicity.

Right ventricular (RV) involvement in left ventricular (LV) non-compaction (LVNC) remains unknown. We aimed to describe the RV volumetric, functional, and strain characteristics and clinical features of patients with LVNC phenotype and good LV ejection fraction (EF) using cardiac magnetic resonance to characterize RV trabeculation in LVNC and to study the relationships of RV and LV trabeculation with RV volume and function. This retrospective study included 100 Caucasian patients with LVNC phenotype and good LV-EF and 100 age- and sex-matched healthy controls. Patients were further divided into two subgroups according to RV indexed trabecular mass [RV-TMi; patients with RV hypertrabeculation (RV-HT) vs. patients with normal RV trabeculation (RV-NT)]. We measured the LV and RV volumetric, functional, and TMi values using threshold-based postprocessing software and the RV and LV strain values using feature tracking and collected the patients' LVNC-related clinical features. Patients had higher RV volumes, lower RV-EF, and worse RV strain values than controls. A total of 22% of patients had RV-TMi values above the reference range; furthermore, RV-HT patients had higher RV and LV volumes, lower RV- and LV-EF, and worse RV strain values than RV-NT patients. We identified a strong positive correlation between RV- and LV-TMi and between RV-TMi and RV volumes and a significant inverse relationship of both RV- and LV-TMi with RV function. The prevalence of LVNC-related clinical features was similar in the RV-HT and RV-NT groups. These results suggest that some patients with LVNC phenotype might have RV non-compaction with subclinical RV dysfunction and without more severe clinical features.

BackgroundThe quantitative measures used to assess the performance of automated methods often do not reflect the clinical acceptability of contouring. A quality-based assessment of automated cardiac magnetic resonance (CMR) segmentation more relevant to clinical practice is therefore needed.ObjectiveWe propose a new method for assessing the quality of machine learning (ML) outputs. We evaluate the clinical utility of the proposed method as it is employed to systematically analyse the quality of an automated contouring algorithm.MethodsA dataset of short-axis (SAX) cine CMR images from a clinically heterogeneous population (n = 217) were manually contoured by a team of experienced investigators. On the same images we derived automated contours using a ML algorithm. A contour quality scoring application randomly presented manual and automated contours to four blinded clinicians, who were asked to assign a quality score from a predefined rubric. Firstly, we analyzed the distribution of quality scores between the two contouring methods across all clinicians. Secondly, we analyzed the interobserver reliability between the raters. Finally, we examined whether there was a variation in scores based on the type of contour, SAX slice level, and underlying disease.ResultsThe overall distribution of scores between the two methods was significantly different, with automated contours scoring better than the manual (OR (95% CI) = 1.17 (1.07–1.28), p = 0.001; n = 9401). There was substantial scoring agreement between raters for each contouring method independently, albeit it was significantly better for automated segmentation (automated: AC2 = 0.940, 95% CI, 0.937–0.943 vs manual: AC2 = 0.934, 95% CI, 0.931–0.937; p = 0.006). Next, the analysis of quality scores based on different factors was performed. Our approach helped identify trends patterns of lower segmentation quality as observed for left ventricle epicardial and basal contours with both methods. Similarly, significant differences in quality between the two methods were also found in dilated cardiomyopathy and hypertension.ConclusionsOur results confirm the ability of our systematic scoring analysis to determine the clinical acceptability of automated contours. This approach focused on the contours' clinical utility could ultimately improve clinicians' confidence in artificial intelligence and its acceptability in the clinical workflow.

Artificial intelligence (AI) using machine learning techniques will change healthcare as we know it. While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive. Overcoming challenges such as patient and public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Cardiac imaging and imaging of other body parts is likely to be at the frontier for the development of applications as pattern recognition and machine learning are a significant strength of AI with practical links to image processing. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and thus, the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wamil, Goncalves, Rutherford, Borlotti and Pellikka.)