Abstract

Cardiovascular imaging has become an indispensable tool for patient diagnosis and follow up. Probably the wide clinical applications of imaging are due to the possibility of a detailed and high quality description and quantification of cardiovascular system structure and function. Also phenomena that involve complex physiological mechanisms and biochemical pathways, such as inflammation and ischemia, can be visualized in a non-destructive way. The widespread use and evolution of imaging would not have been possible without animal studies. Animal models have allowed for instance, (i) the technical development of different imaging tools, (ii) to test hypothesis generated from human studies and finally, (iii) to evaluate the translational relevance assessment of in vitro and ex-vivo results. In this review, we will critically describe the contribution of animal models to the use of biomedical imaging in cardiovascular medicine. We will discuss the characteristics of the most frequent models used in/for imaging studies. We will cover the major findings of animal studies focused in the cardiovascular use of the repeatedly used imaging techniques in clinical practice and experimental studies. We will also describe the physiological findings and/or learning processes for imaging applications coming from models of the most common cardiovascular diseases. In these diseases, imaging research using animals has allowed the study of aspects such as: ventricular size, shape, global function, and wall thickening, local myocardial function, myocardial perfusion, metabolism and energetic assessment, infarct quantification, vascular lesion characterization, myocardial fiber structure, and myocardial calcium uptake. Finally we will discuss the limitations and future of imaging research with animal models.

Highlights

  • Cardiovascular disease is the most important cause of mortality in the Western world

  • Newer quantitative Cardiac Magnetic Resonance (CMR) methods include: (1) T1 and T2 mapping imaging (Figure 2) that allow direct measurement of intrinsic tissue properties. These sequences are less dependent on confounders affecting signal intensity (Ugander et al, 2012) and their accuracy for area at risk (AAR) quantification is high compared to microsphere blood flow analysis in a dog model of ischemia/reperfusion injury (Fernandez-Jimenez et al, 2015b); (2) BOLD or modified blood oxygen level-dependent sequences which have been recently proposed to detect ischemic myocardium in a dog model of severe coronary stenosis (Tsaftaris et al, 2013); (3) Targeted microparticles of iron oxide, which shorten T2 and T2* relaxation times

  • According to these two different causes, there are two phenotypes of HF, ones derived from the systolic dysfunction, where the EF decreases below 50%, called HF with reduced EF; and ones derived from a diastolic dysfunction, where the EF remains above 50%, called HF with preserved ejection EF

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Summary

Introduction

Cardiovascular disease is the most important cause of mortality in the Western world It is responsible for a huge lost in years of healthy life and one of the principal reasons for hospitalizations and emergency room visits. Animal models in cardiovascular imaging improved the outcomes of patients; diagnosis and monitoring tools have improved a lot in recent years. Between these diagnostic tools a special place is reserved for biomedical imaging. In vitro and ex-vivo models are useful for testing hypotheses derived from the clinical setting. They provide us a scenario in which to evaluate a new imaging tool or tracer. In this review we will discuss the principal animal models used in imaging studies of major cardiovascular diseases

Characteristics of Animal Models For in vivo Cardiovascular Imaging Studies
Imaging in Myocardial Infarction and Coronary Artery Disease
Heart Failure
Dilated cardiomyopathy
Mimic human disease condition
VASCULAR DISEASE
Resembles major features of human disease
No plexogenic arteriopathy
Atherosclerosis and Vascular Lesion
Hypertension Image Studies
Cardiovascular Imaging for Translational
Author Contributions
Findings
Conclusions
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