Person:
Villalba Orero, María

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First Name
María
Last Name
Villalba Orero
URI
https://hdl.handle.net/20.500.14352/80537
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Veterinaria
Department
Medicina y Cirugía Animal
Area
Medicina y Cirugía Animal
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Author: Lara-Pezzi, Enrique × Subject: 636.09 ×

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Now showing 1 - 2 of 2
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    A novel data-driven method for the analysis and reconstruction of cardiac cine MRI
    (Computers in Biology and Medicine, 2022) Nourelhouda Groun; Villalba Orero, María; Lara-Pezzi, Enrique; Valero, Eusebio; Garicano-Mena, Jesús; Le Clainche, Soledad
    Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed information about cardiac structure, tissue composition and even blood flow, which makes it highly used in medical science. But due to the image time acquisition and several other factors the MRI sequences can easily get corrupted, causing radiologists to misdiagnose 40 million people worldwide each and every single year. Hence, the urge to decrease these numbers, researchers from different fields have been introducing novel tools and methods in the medical field. Aiming to the same target, we consider in this work the application of the higher order dynamic mode decomposition (HODMD) technique. The HODMD algorithm is a linear method, which was originally introduced in the fluid dynamics domain, for the analysis of complex systems. Nevertheless, the proposed method has extended its applicability to numerous domains, including medicine. In this work, HODMD in used to analyze sets of MR images of a heart, with the ultimate goal of identifying the main patterns and frequencies driving the heart dynamics. Furthermore, a novel interpolation algorithm based on singular value decomposition combined with HODMD is introduced, providing a three-dimensional reconstruction of the heart. This algorithm is applied (i) to reconstruct corrupted or missing images, and (ii) to build a reduced order model of the heart dynamics.
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    Non-invasive assessment of HFpEF in mouse models: current gaps and future directions
    (BMC Medicine, 2022) Villalba Orero, María; Garcia-Pavia, Pablo; Lara-Pezzi, Enrique
    Background: Heart failure (HF) with preserved ejection fraction (HFpEF) prevalence is increasing, and large clinical trials have failed to reduce mortality. A major reason for this outcome is the failure to translate results from basic research to the clinics. Evaluation of HFpEF in mouse models requires assessing three major key features defining this complex syndrome: the presence of a preserved left ventricular ejection fraction (LVEF), diastolic dysfunction, and the development of HF. In addition, HFpEF is associated with multiple comorbidities such as systemic arterial hypertension, chronic obstructive pulmonary disease, sleep apnea, diabetes, and obesity; thus, non-cardiac disorders assessment is crucial for a complete phenotype characterization. Non-invasive procedures present unquestionable advantages to maintain animal welfare and enable longitudinal analyses. However, unequivocally determining the presence of HFpEF using these methods remains challenging. Main text: Transthoracic echocardiography (TTE) represents an invaluable tool in HFpEF diagnosis, allowing evaluation of LVEF, diastolic dysfunction, and lung congestion in mice. Since conventional parameters used to evaluate an abnormal diastole like E/A ratio, isovolumic relaxation time, and E/e′ may pose limitations in mice, including advanced TTE techniques to characterize cardiac motion, including an assessment under stress, will improve diagnosis. Patients with HFpEF also show electrical cardiac remodelling and therefore electrocardiography may add valuable information in mouse models to assess chronotropic incompetence and sinoatrial node dysfunction, which are major contributors to exercise intolerance. To complete the non-invasive diagnosis of HF, low aerobic exercise capacity and fatigue using exercise tests, impaired oxygen exchange using metabolic cages, and determination of blood biomarkers can be determined. Finally, since HFpEF patients commonly present non-cardiac pathological conditions, acquisition of systemic and pulmonary arterial pressures, blood glucose levels, and performing glucose tolerance and insulin resistance tests are required for a complete phenotyping. Conclusion: Identification of reliable models of HFpEF in mice by using proper diagnosis tools is necessary to translate basic research results to the clinics. Determining the presence of several HFpEF indicators and a higher number of abnormal parameters will lead to more reliable evidence of HFpEF.