Background Myocardial iron deficiency (MID) in heart failure (HF) remains largely unexplored. We aim to establish defining criterion for MID, evaluate its pathophysiological role, and evaluate the applicability of monitoring it non‐invasively in human explanted hearts. Methods and Results Biventricular tissue iron levels were measured in both failing (n=138) and non‐failing control (NFC, n=46) explanted human hearts. Clinical phenotyping was complemented with comprehensive assessment of myocardial remodeling and mitochondrial functional profiles, including metabolic and oxidative stress. Myocardial iron status was further investigated by cardiac magnetic resonance imaging. Myocardial iron content in the left ventricle was lower in HF versus NFC (121.4 [88.1–150.3] versus 137.4 [109.2–165.9] μg/g dry weight), which was absent in the right ventricle. With a priori cutoff of 86.1 μg/g d.w. in left ventricle, we identified 23% of HF patients with MID (HF‐MID) associated with higher NYHA class and worsened left ventricle function. Respiratory chain and Krebs cycle enzymatic activities were suppressed and strongly correlated with depleted iron stores in HF‐MID hearts. Defenses against oxidative stress were severely impaired in association with worsened adverse remodeling in iron‐deficient hearts. Mechanistically, iron uptake pathways were impeded in HF‐MID including decreased translocation to the sarcolemma, while transmembrane fraction of ferroportin positively correlated with MID. Cardiac magnetic resonance with T2* effectively captured myocardial iron levels in failing hearts. Conclusions MID is highly prevalent in advanced human HF and exacerbates pathological remodeling in HF driven primarily by dysfunctional mitochondria and increased oxidative stress in the left ventricle. Cardiac magnetic resonance demonstrates clinical potential to non‐invasively monitor MID.
Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.
The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT) images. Although deep supervised learning methods have been established quickly, the scarcity of both image-level and pixel-level labels as well as the lack of explainable transparency still hinder the applicability of AI. Can we identify infected patients and delineate the infections with extreme minimal supervision? Semi-supervised learning has demonstrated promising performance under limited labelled data and sufficient unlabelled data. Inspired by semisupervised learning, we propose a model-agnostic calibrated pseudo-labelling strategy and apply it under a consistency regularization framework to generate explainable identification and delineation results. We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data. Extensive experiments have shown that our model can efficiently utilize limited labelled data and provide explainable classification and segmentation results for decision-making in clinical routine. The code is available at https://github.com/ayanglab/XAI COVID-19.
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