Abstract
Purpose: Individuals with depression have an increased risk of cardiovascular disease, and more often have a poor prognosis with cardiovascular disease. This study aimed to investigate the impact of depression on Left Ventricular (LV) alterations using Cardiovascular Magnetic Resonance Featuretracking (CMR-FT).
Methods: Seven anesthetized, healthy Chinese miniature swine were included in the study. Basic data, including CMR scans at baseline and after 14 days of depression modeling, were collected. Behavioral tests, including the Open-field Test (OFT), Sucrose Preference Test (SPT), and measurements of the time taken to consume a specific amount of food and sugar, were conducted to assess the success of the depression models. CMR cine images were acquired and CVI software was employed to analyze Global Longitudinal Strain (GLS), Global Circumferential Strain (GCS), and Global Radial Strain (GRS). Late Gadolinium Enhancement (LGE) imaging was used to detect myocardial infarction and/or scar.
Results: The outcomes demonstrated successful depression modeling, indicated by reduced scores in the OFT and SPT, as well as an extended time to intake food and sugar compared to baseline. However, no significant differences were observed in LV End-diastolic Volume (LVEDV), LV Endsystolic Volume (LVESV), LV Ejection Fraction (LVEF), LV End-diastolic Myocardial Mass (LVMASSED), and Cardiac Output (CO) before and after modeling. Regarding LV global strain parameters, there was a downward trend in GRS (25.35% ± 6.9% vs. 22.86% ± 6.4%, P=0.021), GCS (-16.71% ± 4.2% vs. -14.78% ± 2.3%, P=0.043), and GLS (-17.66% ± 2.9% vs. -14.53% ± 2.5%, P=0.056), respectively, after modeling. GRS and GCS were significantly reduced after modeling compared to baseline.
Conclusion: The study suggests that depression may contribute to early LV systolic dysfunction, particularly affecting LV GCS and GRS.
Keywords: Depression, Cardiovascular magnetic resonance, Feature-tracking, Swine, Heart failure, Arrhythmia, Myocardial infarction.
Call for Papers in Thematic Issues
Advancements and Applications of Photon Counting CT: Transforming Imaging Precision and Quantification
Photon Counting Computed Tomography (CT) represents a transformative advancement in medical imaging technology, offering unprecedented levels of precision and diagnostic capability. This special issue will explore the latest innovations and applications of photon counting CT, highlighting its advantages over traditional energy-integrating detectors. The scope includes an in-depth examination of its ...read more
Advancements in Deep Learning based Medical Image and Signal Processing for Healthcare Applications
The accurate predication from medical data (signals and images) analytics will be achieved through the use of cutting-edge AI tools for tailoring medical treatment to each patient. When the agents are done analysing the user's health data, the user can consult with the agents to receive advice and the most ...read more
AI-Driven Diagnostics in Radiology and Medical Imaging
Medical imaging data, such as X-rays, MRIs, ultrasounds, CT scans, and DXAs, can be analysed by AI algorithms to help healthcare professionals identify and diagnose diseases more rapidly and accurately. Among the most cutting-edge applications of artificial intelligence (AI) in healthcare are imaging-related applications like cancer detection. AI could revolutionise ...read more
Applying medical image processing techniques for early cancer detection in mammograms
Breast cancer survival and successful treatment are significantly boosted by early identification. Breast cancer screening is most commonly accomplished with mammography. The radiologist's experience and the image quality, among several other factors, might affect how well a mammogram is interpreted. Enhancing mammography analysis and increasing cancer early detection are made ...read more
11