Michelle Nanni*, Vivian Hu, Swagata Patnaik, Alejandro Folch Sandoval and Johanna Contreras
Published on: 24th January, 2024
The incidence of heart failure with preserved ejection fraction (HFpEF) continues to rise, and obesity continues to be a predominant comorbid condition affecting patients with HFpEF. Recent research sheds light on the important pathophysiologic role that obesity plays in the development of HFpEF, with many areas of opportunity existing for future developments in understanding the etiology and management of the disease. Crucial in these pathophysiologic developments are studies that clearly characterize the obesity phenotype in HFpEF and compare it to presentations of HFpEF in patients without obesity. This paper reviews the existing literature on the obesity phenotype within HFpEF and discusses some of the prevailing ideas behind the pathophysiologic interplay between the conditions, as well as the existing treatments demonstrating improved outcomes in HFpEF.
Cardiovascular Diseases (CVDs) remain a major global health concern, necessitating accurate and comprehensive diagnostic techniques. Traditional medical imaging modalities, such as CT angiography, PET, MRI, and ultrasound, provide crucial but limited information when used independently. Image fusion techniques integrate complementary modalities, enhance visualization, and improve diagnostic accuracy. This paper presents a theoretical study of advanced image fusion methods applied to cardiovascular imaging. We explore wavelet-based, Principal Component Analysis (PCA), and deep learning-driven fusion models, emphasizing their theoretical underpinnings, mathematical formulation, and potential clinical applications. The proposed framework enables improved coronary artery visualization, cardiac function assessment, and real-time hemodynamic analysis, offering a non-invasive and highly effective approach to cardiovascular diagnostics.MSC Codes: 68U10,94A08,92C55,65T60,62H25,68T07.
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