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Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Secondly, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher peak signal-to-noise ratio and structural similarity index measure compared to state-of-art methods. The de-artifact images have clear texture edges. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Background Myocarditis is challenging to diagnose due to its varied presentations. Endomyocardial biopsy is the gold standard for diagnosis, but its invasive nature has led to alternative non-invasive modalities, notably cardiovascular magnetic resonance (CMR). Identifying the precise aetiology of myocarditis is crucial for effective treatment, yet extra-cardiac causes are often overlooked. In this paper, we spotlight the underexplored role of CMR in diagnosing extra-cardiac aetiologies, utilizing three insightful cases for illustration. Case summary The first case is a 31-year-old patient with myocarditis secondary to a pyogenic liver abscess, identified through CMR, who improved after abscess drainage. The second case involves a 54-year-old patient with myocarditis attributed to adult T-cell leukaemia–lymphoma, with the loco-regional thickening process identified thanks to CMR. This patient had an unfavourable disease progression due to the underlying malignancy. The third case concerns a 23-year-old patient suffering from myocarditis associated with pneumonia, again illustrated effectively through CMR imaging, who recovered after antibiotic treatment. Discussion These cases underline the overlooked potential of CMR in diagnosing extra-cardiac aetiologies of myocarditis, even though such causes are rare. Despite current guidelines recognizing the importance of identifying the aetiology of myocarditis, they do not explicitly address the role of CMR in diagnosing extra-cardiac aetiologies. This article, therefore, proposes that future guidelines could emphasize the utility of CMR in exploring these causes, potentially leading to more accurate diagnoses and improved patient outcomes. It also advocates for a comprehensive, multidisciplinary approach to myocarditis diagnosis, encouraging vigilance for potential loco-regional causes, and calls for further research in this area. [ABSTRACT FROM AUTHOR]

Background: IgG4-related disease is a fibro-inflammatory disorder with an unknown etiology, which can affect multiple organ systems, including the cardiovascular system. While most reported cases of cardiovascular involvement are primarily associated with the aorta, there have been sporadic reports of isolated cardiac involvement. Case presentation: This paper presents a documented case of IgG4-related systemic disease with symptoms indicative of restrictive cardiomyopathy. Subsequent Cardiac Magnetic Resonance imaging revealed diffuse myopericardial involvement, characterized by pericardial thickening and enhancement, accompanied by subepicardial and myocardial infiltration. Considering the rarity of cardiac involvement in our case, we conducted a thorough review of the existing literature pertaining to various patterns of cardiac involvement in IgG4-related disease, as well as the diagnostic modalities that can be employed for accurate identification and assessment. Conclusions: This case report sheds light on the importance of recognizing and evaluating cardiac manifestations in IgG4-related systemic disease to facilitate timely diagnosis and appropriate management. [ABSTRACT FROM AUTHOR]

Automatic and accurate segmentation of medical images plays an essential role in disease diagnosis and treatment planning. Convolution neural networks have achieved remarkable results in medical image segmentation in the past decade. Meanwhile, deep learning models based on Transformer architecture also succeeded tremendously in this domain. However, due to the ambiguity of the medical image boundary and the high complexity of physical organization structures, implementing effective structure extraction and accurate segmentation remains a problem requiring a solution. In this paper, we propose a novel Dual Encoder Network named DECTNet to alleviate this problem. Specifically, the DECTNet embraces four components, which are a convolution-based encoder, a Transformer-based encoder, a feature fusion decoder, and a deep supervision module. The convolutional structure encoder can extract fine spatial contextual details in images. Meanwhile, the Transformer structure encoder is designed using a hierarchical Swin Transformer architecture to model global contextual information. The novel feature fusion decoder integrates the multi-scale representation from two encoders and selects features that focus on segmentation tasks by channel attention mechanism. Further, a deep supervision module is used to accelerate the convergence of the proposed method. Extensive experiments demonstrate that, compared to the other seven models, the proposed method achieves state-of-the-art results on four segmentation tasks: skin lesion segmentation, polyp segmentation, Covid-19 lesion segmentation, and MRI cardiac segmentation. [ABSTRACT FROM AUTHOR]

There is currently no clear consensus on screening techniques to evaluate the presence or severity of Fontan-associated liver disease (FALD). Cardiac MRI (CMR) is used routinely for post-Fontan surveillance, but CMR-derived measures that relate to the severity of FALD are not yet defined. This was a cross-sectional single-center study of post-Fontan patients who underwent a CMR. CMR exams were re-analyzed by a single pediatric cardiologist. Surrogates of FALD included Gamma-Glutamyl Transferase (GGT), Fibrosis-4 laboratory score (FIB-4), and imaging findings. Findings consistent with cirrhosis on liver ultrasound included increased liver echogenicity and/or nodularity. Statistical analyses were performed to investigate potential relationships between CMR parameters and markers of FALD. Sixty-one patients were included. A larger inferior vena cava cross-sectional area (IVC-CSA) indexed to height was significantly associated with a higher FIB-4 score (Spearman's ρ = 0.28, p = 0.04), a higher GGT level (Spearman's ρ = 0.40, p = 0.02), and findings consistent with cirrhosis on liver ultrasound (OR 1.17, 95% CI: (1.01, 1.35), p = 0.04). None of the other CMR parameters were associated with markers of FALD. A larger indexed IVC-CSA was associated with higher systemic ventricle end-diastolic pressure (EDP) on cardiac catheterization (Spearman's ρ = 0.39, p = 0.018) as well as older age (Spearman's ρ = 0.46, p = < 0.001). Indexed IVC-CSA was the only CMR parameter that was associated with markers of FALD. This measure has the potential to serve as an additional non-invasive tool to improve screening strategies for FALD. Visual abstract summarizing the primary findings of this paper [ABSTRACT FROM AUTHOR]

Lipomatous hypertrophy of the interatrial septum (LHIAS) represents a benign proliferation of lipoid cells at the level of the interatrial septum (IAS) inducing an important thickening of this structure. It respects the fossa ovalis (FO) region, having a typical "hourglass" echocardiographic appearance. There are certain cases though, with unusual appearances and/or with associated pathologies that may induce similar lesions in the heart, in which the differential diagnosis cannot be guaranteed using only the standard methods. The final diagnosis has important implications in these patients' treatment plan. In this paper, we present an unusual case of a female patient undergoing chemotherapy for lung carcinoma, suspected of right atrial thrombosis/metastasis. As the diagnosis was unclear after transthoracic echocardiography (TTE), inducing the suspicion of an IAS mass with atrial wall infiltration, bi- and tridimensional transesophageal echocardiography (TOE) was performed, revealing a severely and homogenously hypertrophied IAS respecting the FO, but lacking a clear visualization of the atrial wall. The diagnosis of LHIAS was established by cardiac magnetic resonance (CMR) that certified the adipose nature of the structure, excluding the need for invasive investigations and/or treatment options. Multimodality imaging is very important for the clinician in adopting the best management plan for each individual patient. [ABSTRACT FROM AUTHOR]

Background: Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator‐interleaved GA trajectory to realize free‐running cardiac MRI. However, navigator‐interleaved GA trajectory suffers from the eddy‐current effect, which reduces the image quality. Purpose: This work aims to integrate the navigator‐interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy‐current artifacts. The ultimate goal is to realize a high‐quality free‐running cardiac imaging technique. Methods: In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k‐space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart‐healthy subjects (aged 21–25 years) were recruited for free‐running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low‐rank subspace‐constrained algorithm. The image quality was evaluated by signal‐to‐noise‐ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images. Results: SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in‐vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high‐frequency artifacts. The free‐running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns. Conclusions: The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator‐interleaved GA sequence. SwingGA is a promising sampling pattern for free‐running cardiac MRI. [ABSTRACT FROM AUTHOR]

Background: Recent advances in hardware and software permit the use of cardiac MRI of late gestation fetuses, however there is a paucity of MRI‐based reference values. Purpose: To provide initial data on fetal cardiac MRI‐derived cardiac dimensions, volumes, ventricular function, and left ventricular longitudinal strain in healthy developing fetuses >30 weeks gestational age. Study Type: Prospective. Population: Twenty‐five third trimester (34 ± 1 weeks, range of 32–37 weeks gestation) women with healthy developing fetuses. Field Strength/Sequence: Studies were performed at 1.5 T and 3 T. Cardiac synchronization was achieved with a Doppler ultrasound device. The protocol included T2 single shot turbo spin echo stacks for fetal weight and ultrasound probe positioning, and multiplanar multi‐slice cine balanced steady state free precession gradient echo sequences. Assessment: Primary analyses were performed by a single observer. Weight indexed right ventricular (RV) and left ventricular (LV) volumes and function were calculated from short axis (SAX) stacks. Cardiac dimensions were calculated from the four‐chamber and SAX stacks. Single plane LV longitudinal strain was calculated from the four‐chamber stack. Interobserver variability was assessed in 10 participants. Cardiac MRI values were compared against available published normative fetal echocardiogram data using z‐scores. Statistical Tests: Mean and SDs were calculated for baseline maternal/fetal demographics, cardiac dimensions, volumes, ventricular function, and left ventricular longitudinal strain. Bland–Altman and intraclass correlation coefficient analysis was performed to test interobserver variability. Results: The mean gestational age was 34 ± 1.4 weeks. The mean RV and LV end diastolic volumes were 3.1 ± 0.6 mL/kg and 2.4 ± 0.5 mL/kg respectively. The mean RV cardiac output was 198 ± 49 mL/min/kg while the mean LV cardiac output was 173 ± 43 mL/min/kg. Data Conclusion: This paper reports initial reference values obtained by cardiac MRI in healthy developing third trimester fetuses. MRI generally resulted in slightly larger indexed values (by z‐score) compared to reports in literature using fetal echocardiography. Evidence level: 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Background: Cushing's Syndrome (CS) is associated with increased cardiovascular morbidity and mortality. In endogenous CS, cardiovascular mortality remains increased for up to 15 years post remission of hypercortisolism. Similarly, patients with exogenous CS have 4-fold increased incidence of cardiovascular events, regardless of pre-existing cardiovascular disease (CVD). Objective: To present the pathophysiology, prognosis, clinical and imaging phenotype of cardiac disease in CS. Methods: A Pubmed search for cardiac disease in CS over the last 20 years was conducted using combinations of relevant terms. Preclinical and clinical studies, as well as review papers reporting on subclinical heart failure (HF), cardiomyopathy, coronary heart disease (CHD), and cardiovascular imaging were selected. Results: Cardiac disease in CS is associated with direct mineralocorticoid and glucocorticoid receptor activation, increased responsiveness to angiotensin II, ectopic epicardial adiposity, arterial stiffness and endothelial dysfunction, as well as with diabetes mellitus, hypertension, hyperlipidemia, obesity and prothrombotic diathesis. Subclinical HF and cardiomyopathy are principally related to direct glucocorticoid (GC) effects and markedly improve or regress post hypercortisolism remission. In contrast, CHD is related to both direct GC effects and CS comorbidities and persists post cure. In patients without clinical evidence of CVD, echocardiography and cardiac magnetic resonance (CMR) imaging reveal left ventricular hypertrophy, fibrosis, diastolic and systolic dysfunction, with the latter being underestimated by echocardiography. Finally, coronary microvascular disease is encountered in one third of cases. Conclusion: Cardiovascular imaging is crucial in evaluation of cardiac involvement in CS. CMR superiority in terms of reproducibility, operator independency, unrestricted field of view and capability of tissue characterisation makes this modality ideal for future studies. [ABSTRACT FROM AUTHOR]

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation. [Display omitted] • Cardiac imaging plays a crucial role in the evaluation and management of patients with congenital heart disease. • Advancements in cardiac imaging techniques will likely find rapid clinical application in the assessment of patients with congenital heart disease. • Strategies to assess clinical value and resources for training are needed before widespread implementation. [ABSTRACT FROM AUTHOR]

Cardiac Magnetic Resonance (CMR) is widely used in diagnosing cardiac diseases for its excellent contrast of cardiovascular features. However, due to the long imaging time of CMR scanning, the patient's respiration, limb shaking, and heart beating will lead to a certain degree of motion artifacts in the image, seriously degrade the image quality and affect the doctor's clinical judgment. This paper proposes a superpixel conditional Generative Adversarial Network (spcGAN) based on a conditional Generative Adversarial Network (cGAN) by applying superpixel to both generator and discriminator parts. In the generator section, a generator network based on superpixel segmentation and pooling is proposed for feature extraction at the superpixel level to enhance the reconstruction of image edge texture and structural details. In the discriminator part, superpixel pooling is used to construct a superpixel discriminator. It is fused with the traditional convolutional discriminator to produce a superpixel-based dual discriminator, which makes the discriminator consider the image's local structure and details. Based on the generator and discriminator structure proposed in this paper, superpixel pooling and edge texturing loss functions are designed for optimization. Adequate ablation experiments and comparison experiments are conducted in terms of experimental results. Three types of objective metrics, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Focus Measurement (Tenengrad), were selected as references. The experimental results show that the effect of removing motion artifacts from authentic CMR images on the three datasets is most significant in the dataset produced in this paper. The results obtained from the fusion between the designed generator, discriminator, and loss function are the most obvious. Compared with the existing methods, the spcGAN proposed in this paper performs better. • Applying superpixels to CMR artifact removal networks. • Proposing Generator Networks Based on Superpixel Segmentation and Pooling. • Constructing Superpixel Discriminator and combine them with Regular Discriminator. • Designing loss functions for superpixel pooling and edge texture optimization. [ABSTRACT FROM AUTHOR]

Deep learning-based segmentation models often struggle to achieve optimal performance when encountering new, unseen semantic classes. Their effectiveness hinges on vast amounts of annotated data and high computational resources for training. However, a promising solution to mitigate these challenges is the adoption of few-shot segmentation (FSS) networks, which can train models with reduced annotated data. The inherent complexity of medical images limits the applicability of FSS in medical imaging, despite its potential. Recent advancements in self-supervised label-efficient FSS models have demonstrated remarkable efficacy in medical image segmentation tasks. This paper presents a novel FSS architecture that enhances segmentation accuracy by utilising fewer features than existing methodologies. Additionally, this paper proposes a novel self-supervised learning approach that utilises supervoxel and augmented superpixel images to further enhance segmentation accuracy. This paper assesses the efficacy of the proposed model on two different datasets: abdominal magnetic resonance imaging (MRI) and cardiac MRI. The proposed model achieves a mean dice score and mean intersection over union of 81.62% and 70.38% for abdominal images, and 79.38% and 65.23% for cardiac images. [ABSTRACT FROM AUTHOR]

Catastrophic Antiphospholipid Syndrome (CAPS) is a rare complication that can occur in patients with Antiphospholipid Syndrome (APS). CAPS occurs even more rarely during pregnancy/puerperium and pregnant patients, even less likely to show cardiac involvement without signs of damage on ultrasound and angiography with non-obstructive coronary arteries. We present a case of a 26-year-old breastfeeding woman, the youngest described with CAPS and acute myocardial infarction, whose diagnosis was made with cardiac magnetic resonance imaging (CMRI). A literature review of pregnant patients with similar problems was performed. There are diagnostic and therapeutic difficulties in treating these patients. CMRI demonstrated a transmural late enhancement area. A combination of therapies led to rapid clinical improvement. CMRI is an underused tool that reaffirms the pathophysiology of CAPS and leads clinicians to the possibility of a diffuse thrombotic process. CAPS involves more organs with high mortality rates. CMRI could be optimized in order to reach an early diagnosis and the most effective treatment. This study provides real-world evidence of the feasibility of MRI in a primary care setting during pregnancy/puerperium. Evidence from this study may influence future APS screening and inform policymakers regarding the use of leading MRI technology in the detection of the thrombotic process in a primary care setting. [ABSTRACT FROM AUTHOR]

To develop a deep learning-based model capable of segmenting the left ventricular (LV) myocardium on native T1 maps from cardiac MRI in both long-axis and short-axis orientations. Models were trained on native myocardial T1 maps from 50 healthy volunteers and 75 patients using manual segmentation as the reference standard. Based on a U-Net architecture, we systematically optimized the model design using two different training metrics (Sørensen-Dice coefficient = DSC and Intersection-over-Union = IOU), two different activation functions (ReLU and LeakyReLU) and various numbers of training epochs. Training with DSC metric and a ReLU activation function over 35 epochs achieved the highest overall performance (mean error in T1 10.6 ± 17.9 ms, mean DSC 0.88 ± 0.07). Limits of agreement between model results and ground truth were from -35.5 to + 36.1 ms. This was superior to the agreement between two human raters (-34.7 to + 59.1 ms). Segmentation was as accurate for long-axis views (mean error T1: 6.77 ± 8.3 ms, mean DSC: 0.89 ± 0.03) as for short-axis images (mean error ΔT1: 11.6 ± 19.7 ms, mean DSC: 0.88 ± 0.08). Fully automated segmentation and quantitative analysis of native myocardial T1 maps is possible in both long-axis and short-axis orientations with very high accuracy. [ABSTRACT FROM AUTHOR]

Introduction: Assessment of left ventricular ejection fraction (LVEF) is one of the primary objectives of echocardiography. The gold standard assessment technique in emergency medicine is eyeballing. A new tool is now available on pocket ultrasound devices (PUD): automatic LVEF. The primary aim of this study was to evaluate the concordance between LVEF values estimated by automatic LVEF with PUD and by cardiac magnetic resonance imaging (MRI). Materials: This was a prospective, monocentric, and observational study. All adult patients with an indication for cardiac MRI underwent a point-of-care ultrasound. Blinded to the MRI results, the emergency physician assessed LVEF using the automatic PUD tool and by visual evaluation. Results: Sixty patients were included and analyzed. Visual estimation of LVEF was feasible for all patients and automatic evaluation for 52 (87%) patients. Lin's concordance correlation coefficient between automatic ejection fraction with PUD and by cardiac MRI was 0.23 (95% CI, 0.03–0.40). Conclusion: Concordance between LVEF estimated by the automatic ejection fraction with PUD and LVEF estimated by MRI was non-existent. [ABSTRACT FROM AUTHOR]

An editorial is presented on the 2023 European Society of Cardiology (ESC) Guidelines for the Management of Acute Coronary Syndromes (ACS). Topics include the comprehensive management approach from admission to long-term care, the emphasis on a person-centered approach including symptom recognition and patient education, and the recommendations for antithrombotic therapy and long-term treatment strategies.

Apical hypertrophic cardiomyopathy (ApHCM) is an HCM variant, affecting frequently males in midlife. It is characterized by apical obliteration and persistent diastolic contraction, often resulting in microvascular ischaemia. We report five cases of ApHCM, with evidence of intramyocardial calcification on echocardiogram. On cardiac magnetic imaging (MRI), a hypointense component at early gadolinium enhancement (EGE) sequences, compatible with calcium, and a deep layer, with hyperintensity at late gadolinium enhancement (LGE) sequences, referable to fibrosis, suggest an endomyocardial fibrosis (EMF) diagnosis. EMF pathologic hallmark is endocardium and myocardium scarring, evolving to dystrophic calcification. It is found only in few ApHCM patients. Our series is the largest one described until now. Analysing patients' history, coexistent inflammatory triggers were evident in all of them, so their co‐morbidities could represent a further cause of small vessel disease, in the context of ischaemic microvascular stress due to hypertrophy, leading to fibrosis and dystrophic calcification. This series could demonstrate the relation between apical fibrosis/calcification and microvascular ischaemia due to hypertrophy and inflammatory triggers. [ABSTRACT FROM AUTHOR]

Background: Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine. Methods: A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects. Results: The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91−0.98 and 0.89−0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99−1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001). Conclusion: Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment. Trials registration: ClinicalTrials.gov: NCT05114785. Relevance statement: Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes. Key points: • Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias. [ABSTRACT FROM AUTHOR]

Background: Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechanistic relationships between immune response and hypertensive LVH or to find novel therapeutic targets. This study aimed to screen hub immune-related genes involved in hypertensive LVH as well as to explore immune target-based therapeutic drugs. Materials and methods: RNA-sequencing data from a mouse model generated by angiotensin II infusion were subjected to weighted gene co-expression network analysis (WGCNA) to identify core expression modules. Machine learning algorithms were applied to screen immune-related LVH characteristic genes. Heart structures were evaluated by echocardiography and cardiac magnetic resonance imaging (CMRI). Validation of hub genes was conducted by RT-qPCR and western blot. Using the Connectivity Map database and molecular docking, potential small-molecule drugs were explored. Results: A total of 1215 differentially expressed genes were obtained, most of which were significantly enriched in immunoregulation and collagen synthesis. WGCNA and multiple machine learning strategies uncovered six hub immunerelated genes (Ankrd1, Birc5, Nuf2, C1qtnf6, Fcgr3, and Cdca3) that may accurately predict hypertensive LVH diagnosis. Immune analysis revealed that fibroblasts and macrophages were closely correlated with hypertensive LVH, and hub gene expression was significantly associated with these immune cells. A regulatory network of transcription factor-mRNA and a ceRNA network of miRNA-lncRNA was established. Notably, six hub immune-related genes were significantly increased in the hypertensive LVH model, which were positively linked to left ventricle wall thickness. Finally, 12 small-molecule compounds with the potential to reverse the high expression of hub genes were ruled out as potential therapeutic agents for hypertensive LVH. Conclusion: This study identified and validated six hub immune-related genes that may play essential roles in hypertensive LVH, providing new insights into the potential pathogenesis of cardiac remodeling and novel targets for medical interventions. [ABSTRACT FROM AUTHOR]

Tetralogy of Fallot is a congenital heart disease affecting newborns and involves stenosis of the right ventricular outflow tract (RVOT). Surgical correction often widens the RVOT with a transannular enlargement patch, but this causes issues including pulmonary valve insufficiency and progressive right ventricle failure. A monocusp valve can prevent pulmonary regurgitation; however, valve failure resulting from factors including leaflet design, morphology, and immune response can occur, ultimately resulting in pulmonary insufficiency. A multimodal platform to quantitatively evaluate the effect of shape, size, and material on clinical outcomes could optimize monocusp design. This study introduces a benchtop soft biorobotic heart model, a computational fluid model of the RVOT, and a monocusp valve made from an entirely biological cell-assembled extracellular matrix (CAM) to tackle the multifaceted issue of monocusp failure. The hydrodynamic and mechanical performance of RVOT repair strategies was assessed in biorobotic and computational platforms. The monocusp valve design was validated in vivo in ovine models through echocardiography, cardiac magnetic resonance, and catheterization. These models supported assessment of surgical feasibility, handling, suturability, and hemodynamic and mechanical monocusp capabilities. The CAM-based monocusp offered a competent pulmonary valve with regurgitation of 4.6 ± 0.9% and a transvalvular pressure gradient of 4.3 ± 1.4 millimeters of mercury after 7 days of implantation in sheep. The biorobotic heart model, in silico analysis, and in vivo RVOT modeling allowed iteration in monocusp design not now feasible in a clinical environment and will support future surgical testing of biomaterials for complex congenital heart malformations. Editor's summary: Congenital heart defects such as Tetralogy of Fallot require surgical correction to address complex and multifactorial deficits in cardiac function. Here, Singh et al. develop a model of impaired right ventricular outflow tract function in a biorobotic heart paired with an in silico hemodynamic model. The hybrid soft robotic and ovine myocardium exhibited anatomical and biomechanical characteristics of the heart for ex vivo iteration of surgical repair strategies with a cell-assembled matrix biomaterial. The optimized surgical approach was further supported by an acute in vivo ovine model. This multimodal platform will support the hemodynamic evaluation of complex cardiac defects, aiding in the translation of new therapeutic approaches. —Molly Ogle [ABSTRACT FROM AUTHOR]

Background: This study investigates myocardial structural changes in stable coronary artery disease (CAD) patients with type 2 diabetes (T2D) using cardiac magnetic resonance (CMR) strain and T1 mapping. Methods: A total of 155 stable CAD patients underwent CMR examination, including left ventricular (LV) morphology and function assessment, late gadolinium enhancement (LGE), and feature tracking (CMR-FT) for LV global longitudinal, circumferential, and radial strain. T1 mapping with extracellular volume (ECV) evaluation was also performed. Results: Among the enrolled patients, 67 had T2D. Diabetic patients exhibited impaired LV strain and higher ECV compared to non-diabetics. Multivariate analysis identified T2D as an independent predictor of increased ECV and decreased strain. Conclusions: CMR-based strain and T1 mapping highlighted impaired myocardial contractility, elevated ECV, and potential interstitial fibrosis in diabetic patients with stable CAD. This suggests a significant impact of diabetes on myocardial health beyond CAD, emphasizing the importance of a comprehensive assessment in these individuals. Trial registration: http://www.controlled-trials.com/ISRCTN09454308 [ABSTRACT FROM AUTHOR]

Different rat strains are used in various animal models of pulmonary hypertension and right ventricular (RV) failure. No systematic assessment has been made to test differences in RV response to pressure overload between rat strains. We compared RV adaptation to pulmonary trunk banding (PTB) in Wistar (W), Sprague Dawley (SD), and Fischer344 (F) rats by hemodynamic profiling focusing on diastolic function. Age‐matched male rat weanlings were randomized to sham surgery (W‐sham, n = 5; SD‐sham, n = 4; F‐sham, n = 4) or PTB (W‐PTB, n = 8; SD‐PTB, n = 8; F‐PTB, n = 8). RV function was evaluated after 5 weeks by echocardiography, cardiac MRI, and invasive pressure‐volume measurements. PTB caused RV failure and increased RV systolic pressures four‐fold in all three PTB groups compared with sham. W‐ and SD‐PTB had a 2.4‐fold increase in RV end‐systolic volume index compared with sham, while F‐PTB rats were less affected. Diastolic and right atrial impairment were evident by increased RV end‐diastolic elastance, filling pressure, and E/e' in PTB rats compared with sham, again F‐PTB the least affected. In conclusions, PTB caused RV failure with signs of diastolic dysfunction. Despite a similar increase in RV systolic pressure, F‐PTB rats showed less RV dilatation and a more preserved diastolic function compared with W‐ and SD‐PTB. [ABSTRACT FROM AUTHOR]

Left atrial appendage (LAA) is the source of thrombi formation in more than 90% of strokes in patients with nonvalvular atrial fibrillation. Catheter-based LAA occlusion is being increasingly applied as a treatment strategy to prevent stroke. Anatomical complexity of LAA makes percutaneous occlusion commonly performed under transesophageal echocardiography (TEE) and X-ray (XR) guidance especially challenging. Image fusion techniques integrating 3D anatomical models derived from pre-procedural imaging into the live XR fluoroscopy can be applied to guide each step of the LAA closure. Cardiac magnetic resonance (CMR) imaging gains in importance for radiation-free evaluation of cardiac morphology as alternative to gold-standard TEE or computed tomography angiography (CTA). Manual delineation of cardiac structures from non-contrast enhanced CMR is, however, labor-intensive, tedious, and challenging due to the rather low contrast. Additionally, arrhythmia often impairs the image quality in ECG synchronized acquisitions causing blurring and motion artifacts. Thus, for cardiac segmentation in arrhythmic patients, there is a strong need for an automated image segmentation method. Deep learning-based methods have shown great promise in medical image analysis achieving superior performance in various imaging modalities and different clinical applications. Fully-convolutional neural networks (CNNs), especially U-Net, have become the method of choice for cardiac segmentation. In this paper, we propose an approach for automatic segmentation of cardiac structures from non-contrast enhanced CMR images of arrhythmic patients based on CNNs implemented in a multi-stage pipeline. Two-stage implementation allows subdividing the task into localization of the relevant cardiac structures and segmentation of these structures from the cropped sub-regions obtained from previous step leading to efficient and effective way of automated cardiac segmentation. • Deep learning approaches utilizing CNNs successfully achieved state-of-the-art for cardiac image segmentation. • Proposed two-stage CNN-based approach subdivides the segmentation task into localization and subsequent segmentation. • Introduced approach yields efficient method of cardiac image segmentation in arrhythmic patients with improved performance. [ABSTRACT FROM AUTHOR]

The efficacy of an implantable cardioverter-defibrillator (ICD) in patients with a non-ischaemic cardiomyopathy for primary prevention of sudden cardiac death is increasingly debated. We developed a multimodal deep learning model for arrhythmic risk prediction that integrated late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI), electrocardiography (ECG) and clinical data. Short-axis LGE-MRI scans and 12-lead ECGs were retrospectively collected from a cohort of 289 patients prior to ICD implantation, across two tertiary hospitals. A residual variational autoencoder was developed to extract physiological features from LGE-MRI and ECG, and used as inputs for a machine learning model (DEEP RISK) to predict malignant ventricular arrhythmia onset. In the validation cohort, the multimodal DEEP RISK model predicted malignant ventricular arrhythmias with an area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval (CI) 0.71–0.96), a sensitivity of 0.98 (95% CI 0.75–1.00) and a specificity of 0.73 (95% CI 0.58–0.97). The models trained on individual modalities exhibited lower AUROC values compared to DEEP RISK [MRI branch: 0.80 (95% CI 0.65–0.94), ECG branch: 0.54 (95% CI 0.26–0.82), Clinical branch: 0.64 (95% CI 0.39–0.87)]. These results suggest that a multimodal model achieves high prognostic accuracy in predicting ventricular arrhythmias in a cohort of patients with non-ischaemic systolic heart failure, using data collected prior to ICD implantation. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. [ABSTRACT FROM AUTHOR]

Objective: The meta-analysis aimed to explore the cardiac adaptation in hypothyroidism patients by cardiac magnetic resonance. Research methods and procedures: Databases including PubMed, Cochrane Library, Embase, CNKI, and Sinomed for clinical studies of hypothyroidism on cardiac function changes. Databases were searched from the earliest data to 15 June 2023. Two authors retrieved studies and evaluated their quality. Review Manager 5.4.1 and Stata18 were used to analyze the data. This study is registered with the International Platform of Registered Systematic Review and Metaanalysis Protocols (INPLASY), 202440114. Results: Six studies were selected for further analysis. Five of them reported differences in cardiac function measures between patients with hypothyroidism and healthy controls, and three studies reported cardiac function parameters after treatment in patients with hypothyroidism. The fixed-effect model combined WMD values for left ventricular ejection fraction (LVEF) had a pooled effect size of -1.98 (95% CI -3.50 to -0.44], P=0.01), implying that LVEF was lower in patients with hypothyroidism than in healthy people. Analysis of heterogeneity found moderate heterogeneity (P=0.08, I² = 50%). WMD values for stroke volume (SV), cardiac index (CI), left ventricular end-diastolic volume index(LVEDVI), left ventricular end-systolic volume (LESVI), and left ventricular mass index(LVMI) were also analyzed, and pooled effect sizes showed the CI and LVEDVI of patients with hypothyroidism ware significantly decrease (WMD=-0.47, 95% CI [-0.93 to -0.00], P=0.05, WMD=-7.99, 95%CI [-14.01 to -1.96], P=0.009, respectively). Patients with hypothyroidism tended to recover cardiac function after treatment [LVEF (WMD = 6.37, 95%CI [2.05, 10.69], P=0.004), SV (WMD = 7.67, 95%CI [1.61, 13.74], P=0.01), CI (WMD = 0.40, 95%CI [0.01, 0.79], P=0.05)], and there was no difference from the healthy controls. Conclusion: Hypothyroidism could affect cardiac function, although this does not cause significant heart failure. Itmay be an adaptation of the heart to the hypothyroid state. There was a risk that this adaptationmay turn intomyocardial damage. Cardiac function could be restored after treatment in patients with hypothyroidism. Aggressive levothyroxine replacement therapy should be used to reverse cardiac function. [ABSTRACT FROM AUTHOR]

Background: Stress hyperglycemia occurs frequently in patients following acute myocardial infarction (AMI) and may aggravate myocardial stiffness, but relevant evidence is still lacking. Accordingly, this study aimed to examine the impact of admission stress hyperglycemia on left ventricular (LV) myocardial deformation in patients following AMI. Methods: A total of 171 patients with first AMI (96 with normoglycemia and 75 with hyperglycemia) underwent cardiac magnetic resonance (CMR) examination were included. AMI patients were classified according to admission blood glucose level (aBGL): < 7.8 mmol/L (n = 96), 7.8–11.1 mmol/L (n = 41) and ≥ 11.1 mmol/L (n = 34). LV strains, including global radial/circumferential/longitudinal peak strain (PS)/peak systolic strain rate (PSSR)/peak diastolic strain rate (PDSR), were measured and compared between groups. Further, subgroup analyses were separately conducted for AMI patients with and without diabetes. Multivariate analysis was employed to assess the independent association between aBGL and LV global PS in AMI patients. Results: LV global PS, PSSR and PDSR were decreased in radial, circumferential and longitudinal directions in hyperglycemic AMI patients compared with normoglycemic AMI patients (all P < 0.05). These differences were more obvious in patients with diabetes than those without diabetes. AMI patients with aBGL between 7.8 and 11.1 mmol/L demonstrated significant decreased radial and longitudinal PS, radial PSSR, and radial and longitudinal PDSR than those with aBGL < 7.8 mmol/L (all P < 0.05). AMI patients with aBGL ≥ 11.1 mmol/L showed significantly decreased PS, PSSR and PDSR in all three directions than those with aBGL < 7.8 mmol/L, and decreased longitudinal PSSR than those with aBGL between 7.8 and 11.1 (all P < 0.05). Further, aBGL was significantly and independently associated with radial (β = − 0.166, P = 0.003) and longitudinal (β = 0.143, P = 0.008) PS. Conclusions: Hyperglycemia may exacerbate LV myocardial stiffness in patients experienced first AMI, leading to reduction in LV strains. aBGL was an independent indicator of impaired LV global PS in AMI patients. Blood glucose monitoring is more valuable for AMI patients with diabetes. [ABSTRACT FROM AUTHOR]

Background: The potential role of cardiac computed tomography (CT) has increasingly been demonstrated for the assessment of diffuse myocardial fibrosis through the quantification of extracellular volume (ECV). Photon-counting detector (PCD)-CT technology may deliver more accurate ECV quantification compared to energy-integrating detector CT. We evaluated the impact of reconstruction settings on the accuracy of ECV quantification using PCD-CT, with magnetic resonance imaging (MRI)-based ECV as reference. Methods: In this post hoc analysis, 27 patients (aged 53.1 ± 17.2 years (mean ± standard deviation); 14 women) underwent same-day cardiac PCD-CT and MRI. Late iodine CT scans were reconstructed with different quantum iterative reconstruction levels (QIR 1−4), slice thicknesses (0.4−8 mm), and virtual monoenergetic imaging levels (VMI, 40−90 keV); ECV was quantified for each reconstruction setting. Repeated measures ANOVA and t-test for pairwise comparisons, Bland–Altman plots, and Lin's concordance correlation coefficient (CCC) were used. Results: ECV values did not differ significantly among QIR levels (p = 1.000). A significant difference was observed throughout different slice thicknesses, with 0.4 mm yielding the highest agreement with MRI-based ECV (CCC = 0.944); 45-keV VMI reconstructions showed the lowest mean bias (0.6, 95% confidence interval 0.1–1.4) compared to MRI. Using the most optimal reconstruction settings (QIR4. slice thickness 0.4 mm, VMI 45 keV), a 63% reduction in mean bias and a 6% increase in concordance with MRI-based ECV were achieved compared to standard settings (QIR3, slice thickness 1.5 mm; VMI 65 keV). Conclusions: The selection of appropriate reconstruction parameters improved the agreement between PCD-CT and MRI-based ECV. Relevance statement: Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility. Key points: • CT is increasingly promising for myocardial tissue characterization, assessing focal and diffuse fibrosis via late iodine enhancement and ECV quantification, respectively. • PCD-CT offers superior performance over conventional CT, potentially improving ECV quantification and its agreement with MRI-based ECV. • Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86–0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60–15.30%) and 9.90% (IQR: 8.47–11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment. Author summary: Computational fluid dynamics is an engineering tool that can be used in a clinical setting to non-invasively model blood flow through blood vessels, such as the pulmonary artery. This information can be used to inform treatment planning for patients, especially those with cardiovascular conditions. However, its routine use is limited by a labor-intensive process, requiring substantial expertise and computational resources. Recently, machine learning has offered solutions to automate parts of this process, but no single model has addressed the entire workflow. Therefore, we created Image2Flow, a machine learning model capable of generating a patient-specific representation of a pulmonary artery while predicting blood flow through the vessel. Image2Flow can generate highly accurate pulmonary artery representations and reasonably accurate blood flow predictions. Notably, it performs this whole process in 330ms, which is ~5000x faster than the manual methods. With its speed and accuracy, Image2Flow holds substantial promise for facilitating computational fluid dynamics in clinical settings. [ABSTRACT FROM AUTHOR]

Numerous diagnostic criteria for excessive trabeculation, or "noncompaction," score the extent of the trabecular layer. Whether the trabeculations themselves have a poor or good contractility is largely unknown. We retrospectively analyzed cardiac magnetic resonance (CMR) of patients with excessive trabeculation of the left ventricle (LV). The LV was labeled into four regions: compact wall, central cavity (CC), trabeculations, and intertrabecular recesses (IR). For each label we calculated the systolic fractional volume change (SFVC) in short‐axis images (n = 15) and systolic fractional area change (SFAC) in four‐chamber images (n = 30). We measured the ejection fraction (EF) of IR, CC, and total cavity. Three methods to calculate EF of the total cavity were compared: trabeculations included (per guidelines), IR excluded (Jacquier criterion), and trabeculations contoured and excluded (contour‐EF). The SFVC and SFAC of the compact wall were similar with SFVC and SFAC of trabeculations. In contrast, the IR were more diminished in systole by comparison with the CC, having lower SFVC (39% vs. 56%) and SFAC (37% vs. 72%). EF of the IR was also greater than EF of the CC (61% vs. 44%). Excluding IR from the total cavity or including trabeculations negatively impacts the EF (44% and 40%, respectively, vs. 51% for contour‐EF). The trabecular layer operates at a high EF. [ABSTRACT FROM AUTHOR]

Chronic coronary syndrome (CCS) is one of the leading cardiovascular causes of morbidity, mortality, and use of medical resources. After the introduction by international guidelines of the same level of recommendation to non-invasive imaging techniques in CCS evaluation, a large debate arose about the dilemma of choosing anatomical (with coronary computed tomography angiography (CCTA)) or functional imaging (with stress echocardiography (SE), cardiovascular magnetic resonance (CMR), or nuclear imaging techniques) as a first diagnostic evaluation. The determinant role of the atherosclerotic burden in defining cardiovascular risk and prognosis more than myocardial inducible ischemia has progressively increased the use of a first anatomical evaluation with CCTA in a wide range of pre-test probability in CCS patients. Functional testing holds importance, both because the role of revascularization in symptomatic patients with proven ischemia is well defined and because functional imaging, particularly with stress cardiac magnetic resonance (s-CMR), gives further prognostic information regarding LV function, detection of myocardial viability, and tissue characterization. Emerging techniques such as stress computed tomography perfusion (s-CTP) and fractional flow reserve derived from CT (FFRCT), combining anatomical and functional evaluation, appear capable of addressing the need for a single non-invasive examination, especially in patients with high risk or previous revascularization. Furthermore, CCTA in peri-procedural planning is promising to acquire greater importance in the non-invasive planning and guiding of complex coronary revascularization procedures, both by defining the correct strategy of interventional procedure and by improving patient selection. This review explores the different roles of non-invasive imaging techniques in managing CCS patients, also providing insights into preoperative planning for percutaneous or surgical myocardial revascularization. [ABSTRACT FROM AUTHOR]

A similarity-driven multi-dimensional binning algorithm (SIMBA) reconstruction of free-running cardiac magnetic resonance imaging data was previously proposed. While very efficient and fast, the original SIMBA focused only on the reconstruction of a single motion-consistent cluster, discarding the remaining data acquired. However, the redundant data clustered by similarity may be exploited to further improve image quality. In this work, we propose a novel compressed sensing (CS) reconstruction that performs an effective regularization over the clustering dimension, thanks to the integration of inter-cluster motion compensation (XD-MC-SIMBA). This reconstruction was applied to free-running ferumoxytol-enhanced datasets from 24 patients with congenital heart disease, and compared to the original SIMBA, the same XD-MC-SIMBA reconstruction but without motion compensation (XD-SIMBA), and a 5D motion-resolved CS reconstruction using the free-running framework (FRF). The resulting images were compared in terms of lung-liver and blood-myocardium sharpness, blood-myocardium contrast ratio, and visible length and sharpness of the coronary arteries. Moreover, an automated image quality score (IQS) was assigned using a pretrained deep neural network. The lung-liver sharpness and blood-myocardium sharpness were significantly higher in XD-MC-SIMBA and FRF. Consistent with these findings, the IQS analysis revealed that image quality for XD-MC-SIMBA was improved in 18 of 24 cases, compared to SIMBA. We successfully tested the hypothesis that multiple motion-consistent SIMBA clusters can be exploited to improve the quality of ferumoxytol-enhanced cardiac MRI when inter-cluster motion-compensation is integrated as part of a CS reconstruction. [ABSTRACT FROM AUTHOR]

Objective: Sodium glucose cotransporter 2 (SGLT2) inhibitors significantly improve cardiovascular outcomes in diabetic patients; however, the mechanism is unclear. We hypothesized that dapagliflozin improves cardiac outcomes via beneficial effects on systemic and cardiac inflammation and cardiac fibrosis. Research and design methods: This randomized placebo-controlled clinical trial enrolled 62 adult patients (mean age 62, 17% female) with type 2 diabetes (T2D) without known heart failure. Subjects were randomized to 12 months of daily 10 mg dapagliflozin or placebo. For all patients, blood/plasma samples and cardiac magnetic resonance imaging (CMRI) were obtained at time of randomization and at the end of 12 months. Systemic inflammation was assessed by plasma IL-1B, TNFα, IL-6 and ketone levels and PBMC mitochondrial respiration, an emerging marker of sterile inflammation. Global myocardial strain was assessed by feature tracking; cardiac fibrosis was assessed by T1 mapping to calculate extracellular volume fraction (ECV); and cardiac tissue inflammation was assessed by T2 mapping. Results: Between the baseline and 12-month time point, plasma IL-1B was reduced (− 1.8 pg/mL, P = 0.003) while ketones were increased (0.26 mM, P = 0.0001) in patients randomized to dapagliflozin. PBMC maximal oxygen consumption rate (OCR) decreased over the 12-month period in the placebo group but did not change in patients receiving dapagliflozin (− 158.9 pmole/min/106 cells, P = 0.0497 vs. − 5.2 pmole/min/106 cells, P = 0.41), a finding consistent with an anti-inflammatory effect of SGLT2i. Global myocardial strain, ECV and T2 relaxation time did not change in both study groups. Clinical Trial.gov Registration: NCT03782259. [ABSTRACT FROM AUTHOR]

Background: Aberrant left coronary artery from pulmonary artery (ALCAPA) is a very rare congenital heart defect. Its coexistence with patent ductus arteriosus (PDA) is extremely rare. The high pressures created by the left-to-right shunt in the pulmonary arteries can delay symptoms and create a real challenge in diagnosing ALCAPA. Missing this diagnosis can have severe results, including extensive ischemia and sudden death. Case presentation: We present a case of an infant born with a large PDA. Initially treated conservatively, however, due to congestive heart failure and lack of weight gain, she underwent surgical ligation of the PDA at the age of four and a half months. Following surgery, she developed pulmonary edema. Echocardiography revealed decreased ventricular function. ECG revealed ST elevations on lateral leads, and serum troponin was significantly increased. The patient underwent cardiac magnetic resonance imaging (MRI), which revealed signs of wall ischemia and decreased function of the left ventricle (LV) with unclear coronary anatomy. Diagnostic catheterization revealed an ALCAPA. She underwent surgical intervention, and the left coronary artery was re-implanted in the aortic sinus. Follow-up revealed slow improvement of cardiac function. Discussion and literature review: The coexistence of PDA and ALCAPA is a very rare occurrence. We found at least 10 reported cases in the literature. Delayed diagnosis might be detrimental. The prognosis of these patients is variable. Conclusion: An unusual post-surgical course following PDA repair requires a high index of suspicion and appropriate evaluation for ALCAPA, preferably with angiography. [ABSTRACT FROM AUTHOR]

This study leverages sophisticated machine learning methodologies, particularly XGBoost, to analyze cardiovascular diseases through cardiac datasets. The methodology encompasses meticulous data pre-processing, training of the XGBoost algorithm, and its performance evaluation using metrics such as accuracy, precision, and ROC curves. This technique represents a notable progression in the realm of medical research, potentially leading to enhanced diagnostic precision and a deeper comprehension of cardiovascular ailments, thereby improving patient care and treatment modalities in cardiology. Furthermore, the research delves into the utilization of deep learning methodologies for the automated delineation of cardiac structures in MRI and mammography images, aiming to boost diagnostic precision and patient management. [24][3][5][6] In assessing machine learning algorithms' efficacy in diagnosing cardiovascular diseases, this analysis underscores the pivotal role of such algorithms and their possible data inputs. Additionally, it investigates promising directions for future exploration, such as the application of reinforcement learning. A significant aspect of our investigation is the development and deployment of sophisticated deep learning models for segmenting right ventricular images from cardiac MRI scans, aiming at heightened accuracy and dependability in diagnostics. Through the utilization of advanced techniques like Fourier Convolutional Neural Network (FCNN) and improved versions of Vanilla Convolutional Neural Networks (Vanilla-CNN) and Residual Networks (ResNet), we achieved a substantial improvement in accuracy and reliability. This enhancement allows for more precise and quicker identification and diagnosis of cardiovascular diseases, which is of utmost importance in clinical practice. Evaluation of Machine Learning Algorithms: We conducted a comprehensive evaluation of machine learning algorithms in the context of cardiovascular disease diagnosis. This assessment emphasized the fundamental role of machine learning algorithms and their potential data sources. We also explored promising avenues, such as reinforcement learning, for future research. Factors Affecting Predictive Models: We highlighted the critical factors affecting the effectiveness of machine learning-based predictive models. These factors include data heterogeneity, depth, and breadth, as well as the nature of the modeling task, and the choice of algorithms and feature selection methods. Recognizing and addressing these factors are essential for building reliable models. [ABSTRACT FROM AUTHOR]

Systemic sclerosis (SSc) is an autoimmune rheumatic disease, characterized by vascular, inflammatory and fibrotic alterations. Cardiac involvement is the « fatal tip of the iceberg» in SSc, as it leads to high morbidity/mortality. Cardiovascular imaging modalities play an important role in the early diagnosis and treatment assessment of cardiac involvement. Echocardiography is the corner stone for evaluation of cardiac involvement, providing information about function, wall motion, pulmonary pressure, pericardium and valvular disease. It is a low-cost modality, widely available, without radiation and with great experience among cardiologists. However, it is a window and operator dependent modality and cannot provide tissue characterization information, absolutely necessary for diagnosis and treatment of cardiac involvement in SSc. Cardiovascular magnetic resonance (CMR) can perform myocardial function and tissue characterization in the same examination without radiation, has excellent reproducibility and is window and operator independent. The great advantage of CMR is the capability to assess peri- myo-vascular inflammation, myocardial ischemia and presence of replacement and diffuse myocardial fibrosis in parallel with ventricular function assessment. The modified Lake Louise criteria including T2, native T1 mapping and extracellular volume fraction (ECV) has been recently used to diagnose inflammatory cardiomyopathy. According to expert recommendations, myocardial inflammation should be considered if at least 2 indices, one T2 and one T1 parameter are positive, whereas native T1 mapping and ECV assess diffuse fibrosis or oedema, even in the absence of late gadolinium enhancement (LGE). Moreover, transmural/subendocardial LGE following the distribution of coronary arteries and diffuse subendocardial fibrosis not related with epicardial coronary arteries are indicative of epicardial and micro-vascular coronary artery disease, respectively. To conclude, CMR can overcome the limitations of echocardiography by identifying acute/active or chronic myocardial inflammation/fibrosis, ischemia and myocardial infarction using classic and parametric indices in parallel with biventricular function assessment [ABSTRACT FROM AUTHOR]

Background: Congenital absence of pericardium is a rare cardiac disorder with a reported incidence of less than 1 in 10,000. Although most of the cases are of little clinical significance, some of them are associated with serious complications, including risk of herniation and strangulation or coronary artery compression. Detailed Case Description: We report a case of a 36-year-old male referred for routine cardiovascular examination. He had a medical history of a heart murmur since childhood. Electrocardiogram (ECG) revealed sinus rhythm, normal axis, poor R-wave progression in the precordial leads and repolarization abnormalities with negative T waves in leads V1–V4. On 2D transthoracic echocardiography (TTE), an unusual heart position was noted with poor image quality from the standard acoustic windows. The parasternal long axis view gave the impression of right ventricular dilatation. The findings raised the suspicion of left to right shunt and possible atrial septal defect. For further evaluation, the patient was referred for cardiac magnetic resonance which demonstrated complete left-sided absence of the pericardium. Discussion: Due to indistinct and atypical symptoms and lack of clinical awareness, pericardial congenital absence is frequently misdiagnosed. Patients may complain of atypical chest pain. Patient's history and physical examination are often nonspecific. In cases with complete pericardial absence, ECG findings may include right axis deviation, right bundle block and sinus bradycardia. Echocardiography findings are also not characteristic, but some may raise the clinical suspicion of this diagnosis. The imaging modalities of choice are computed tomography and cardiac magnetic resonance. Treatment depends on the type of defect and clinical symptoms. [ABSTRACT FROM AUTHOR]

Background Fluoroscopy-guided catheter ablation has become the gold standard for treatment of cardiac arrhythmias. High resolution electro-anatomical mapping systems have become fundamental to perform these procedures. Recently, interventional cardiac magnetic resonance (iCMR) has been proposed as an alternative for fluoroscopy to guide atrial flutter ablations. The clinical experience with iCMR and dedicated three-dimensional mapping systems is growing. NorthStar is currently the first available vendor-neutral mapping system. Case summary We performed a real-time CMR-guided cavotricuspid isthmus (CTI) catheter ablation (CA) on a 69-year-old man using a novel mapping system (NorthStar Mapping System, Imricor Medical Systems, MN, USA). Starting from the CMR imaging, a pre-rendered segmentation model was loaded on NorthStar and used to guide the catheters, display voltage and activation maps, show mapping and ablation points. NorthStar can also take full control of the CMR scanner (i.e. start/stop sequences for anatomical information, tissue characterization, and catheter visualization) and communicate with the recorder/stimulator system (Advantage-MR EP, Imricor Medical Systems, MN, USA). With comparable procedural time to standard fluoroscopy-guided CA, CTI bidirectional block was achieved, without any complication. Discussion Using the NorthStar Mapping System, we managed to achieve a successful CMR-guided CTI ablation without any complication. Its further use should be explored, especially in more complex arrhythmias where a substrate-guided ablation is critical, as it could significantly improve results in terms of arrhythmia recurrence. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to assess the importance of T1-mapping sequences in the diagnosis of hypertrophic cardiomyopathy (HCM) in patients without foci of non-ischemic myocardial injury in classic cardiac magnetic resonance (CMR) sequences. Methods: Two groups were compared: 28 patients with HCM, without any foci of myocardial injury in the late gadolinium enhancement (LGE) sequence (HCM group), and 28 patients without cardiomyopathy (CON group). Classic CMR sequences and T1-mapping sequences were performed. The following parameters were assessed: T1 time of the whole left ventricular myocardium, T1 time of myocardium in the basal, middle and apical layers of the left ventricle, and T1 time in individual segments of the left ventricular myocardium. Myocardial extracellular volume (ECV) was assessed similarly. Results: ECV was significantly higher in the HCM group than in the CON group, for the whole left ventricular myocardium, for the basal and apical layers of the left ventricle, and for segments 1–3, 8, and 13–16 of the left ventricle. Regression analysis showed that a higher left-ventricular mass index (LVMI), a higher body mass index and older age are factors independently associated with a higher ECV of the whole myocardium but only in the group with LVMI ≥ 131.84 g/m2. Conclusion: In patients with HCM without foci of non-ischemic myocardial injury, higher ECV values of the left ventricular myocardium are observed. [ABSTRACT FROM AUTHOR]

The existing image semantic segmentation models have low accuracy in detecting tiny targets or multi-targets at overlapping regions. This work proposes a hybrid vision transformer with unified-perceptual-parsing network (ViT-UperNet) for medical image segmentation. A self-attention mechanism is embedded in a vision transformer to extract multi-level features. The image features are extracted hierarchically from low to high dimensions using 4 groups of Transformer blocks with different numbers. Then, it uses a unified-perceptual-parsing network based on a feature pyramid network (FPN) and a pyramid pooling module (PPM) for the fusion of multi-scale contextual features and semantic segmentation. FPN can naturally use hierarchical features, and generate strong semantic information on all scales. PPM can better use the global prior knowledge to understand complex scenes, and extract features with global context information to improve segmentation results. In the training process, a scalable self-supervised learner named masked autoencoder is used for pre-training, which strengthens the visual representation ability and improves the efficiency of the feature learning. Experiments are conducted on cardiac magnetic resonance image segmentation where the left and right atrium and ventricle are selected for segmentation. The pixels accuracy is 93.85%, the Dice coefficient is 92.61% and Hausdorff distance is 11.16, which are improved compared with the other methods. The results show the superiority of Vit-UperNet in medical images segmentation, especially for the low-recognition and serious-occlusion targets. [ABSTRACT FROM AUTHOR]

The precise reconstruction of accelerated magnetic resonance imaging (MRI) brings about notable advantages, such as enhanced diagnostic precision and decreased examination costs. In contrast, traditional cardiac MRI necessitates repetitive acquisitions across multiple heartbeats, resulting in prolonged acquisition times. Significant strides have been made in accelerating MRI through deep learning-based reconstruction methods. However, these existing methods encounter certain limitations: (1) The intricate nature of heart reconstruction involving multiple complex time-series data poses a challenge in exploring nonlinear dependencies between temporal contexts. (2) Existing research often overlooks weight sharing in iterative frameworks, impeding the effective capturing of non-local information and, consequently, limiting improvements in model performance. In order to improve cardiac MRI reconstruction, we propose a novel temporal-spatial transformer with a strategy in this study. Based on the multi-level encoder and decoder transformer architecture, we conduct multi-level spatiotemporal information feature aggregation over several adjacent views, that create nonlinear dependencies among features and efficiently learn important information among adjacent cardiac temporal frames. Additionally, in order to improve contextual awareness between neighboring views, we add cross-view attention for temporal information fusion. Furthermore, we introduce an iterative strategy for training weights during the reconstruction process, which improves feature fusion in critical locations and reduces the number of computations required to calculate global feature dependencies. Extensive experiments have demonstrated the substantial superiority of this procedure over the most advanced techniques, suggesting that it has broad potential for clinical use. [ABSTRACT FROM AUTHOR]

Objectives: Our study aimed to assess the safety and efficacy of cardiac contractility modulation (CCM) therapy in patients with heart failure with reduced ejection fraction (HFrEF) depending on HF etiology. Methods: We enrolled 166 patients with optimal medical therapy-resistant HFrEF (median age 59 years, 83.7% males, median NYHA class − 2, median left ventricular ejection fraction (LVEF) − 29.0%) who underwent CCM therapy device implantation from 2013 to 2019 in four medical centers in Russia. The HF etiology was determined based on invasive coronary angiography or cardiac MRI data. Transthoracic echocardiography (TTE), 6-minute walking test (6MWT), and NTproBNP-tests were performed at a baseline and 12 months after the implantation. Results: The ischemic etiology of HF was revealed in 100 patients (61.5%) (ICM group); the non-ischemic group (NICM) evolved 66 patients (38.5%). Patients in the ICM group were significantly older (61[57–69] vs. 55 [42.8–61], p < 0.001), more frequently had hypertension (79% vs. 42.4%, p < 0.001) and chronic kidney disease (43% vs. 22.7%, p = 0.012). Patients in the NICM group had significantly more often atrial fibrillation (AF) (58% vs. 74%, p = 0.048), larger end-diastolic volume (EDV) (249 [208–309] vs. 220 [192–271], p = 0.019) and end-systolic volume (ESV) (183 [147–230] vs. 154 [128–199], p = 0.003). There were no significant differences in mortality between ICM and NICM groups (14.4 vs. 10.8%, p = 0.51). In 12 months, there was a significant increase in LVEF in the NICM group (+ 2.0 [2–6] vs. +7.7 [2–12], p < 0.001), while the improvement in the 6MWT (+ 75 [22–108] vs. +80 [10–160], p = 0.851) and NYHA class did not reach the level of significance. The subanalysis between patients with improved NYHA class and those without improvement revealed that patients without improvement more frequently had AF (56% vs. 89%; p < 0.01), chronic obstructive lung disease (18% vs. 35% p = 0.047), higher blood pressure (110 [105–120] vs. 120[110–129]; p = 0.032). Conclusion: In this multicenter retrospective study, patients with non-ischemic HFrEF showed a significantly higher improvement in LVEF and LV reverse remodeling following CCM therapy device implantation. There was no significant association between HF etiology and survival in drug-resistant HFrEF patients following CCM therapy. [ABSTRACT FROM AUTHOR]