Browsing by Author "Raimondi, Francesca"

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    AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review
    (2025) Jiménez Jara, Cristina; Salas, Rodrigo; Díaz Navarro, Rienzi; Chabert, Steren; Andía Kohnenkampf, Marcelo Edgardo; Vega, Julián; Urbina, Jesús; Uribe, Sergio; Sekine, Tetsuro; Raimondi, Francesca; Sotelo, Julio
    Cardiac magnetic resonance (CMR) imaging has become a key tool in evaluating myocardial injury secondary to coronary artery disease (CAD), providing detailed assessments of cardiac morphology, function, and tissue composition. The integration of artificial intelligence (AI), including machine learning and deep learning techniques, has enhanced the diagnostic capabilities of CMR by automating segmentation, improving image interpretation, and accelerating clinical workflows. Radiomics, through the extraction of quantitative imaging features, complements AI by revealing sub-visual patterns relevant to disease characterization. This systematic review analyzed AI applications in CMR for CAD. A structured search was conducted in MEDLINE, Web of Science, and Scopus up to 17 March 2025, following PRISMA guidelines and quality-assessed with the CLAIM checklist. A total of 106 studies were included: 46 on classification, 19 using radiomics, and 41 on segmentation. AI models were used to classify CAD vs. controls, predict major adverse cardiovascular events (MACE), arrhythmias, and post-infarction remodeling. Radiomics enabled differentiation of acute vs. chronic infarction and prediction of microvascular obstruction, sometimes from non-contrast CMR. Segmentation achieved high performance for myocardium (DSC up to 0.95), but scar and edema delineation were more challenging. Reported performance was moderate-to-high across tasks (classification AUC = 0.66–1.00; segmentation DSC = 0.43–0.97; radiomics AUC = 0.57–0.99). Despite promising results, limitations included small or overlapping datasets. In conclusion, AI and radiomics offer substantial potential to support diagnosis and prognosis of CAD through advanced CMR image analysis.
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    Clinical impact of novel cardiovascular magnetic resonance technology on patients with congenital heart disease: a scientific statement of the Association for European Pediatric and Congenital Cardiology and the European Association of Cardiovascular Imaging of the European Society of Cardiology
    (2024) Voges, Inga; Raimondi, Francesca; Mcmahon, Colin J.; Ait-Ali, Lamia; Babu-Narayan, Sonya, V; Botnar, Rene M.; Burkhardt, Barbara; Gabbert, Dominik D.; Grosse-Wortmann, Lars; Hasan, Hosan; Hansmann, Georg; Helbing, Willem A.; Krupickova, Sylvia; Latus, Heiner; Martini, Nicola; Martins, Duarte; Muthurangu, Vivek; Ojala, Tiina; van Ooij, Pim; Pushparajah, Kuberan; Rodriguez-Palomares, Jose; Sarikouch, Samir; Grotenhuis, Heynric B.; Greil, F. Gerald
    Cardiovascular magnetic resonance (CMR) imaging is recommended in patients with congenital heart disease (CHD) in clinical practice guidelines as the imaging standard for a large variety of diseases. As CMR is evolving, novel techniques are becoming available. Some of them are already used clinically, whereas others still need further evaluation. In this statement, the authors give an overview of relevant new CMR techniques for the assessment of CHD. Studies with reference values for these new techniques are listed in the .
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    Impact of aortic arch curvature in flow haemodynamics in patients with transposition of the great arteries after arterial switch operation
    (2022) Sotelo, Julio; Valverde, Israel; Martins, Duarte; Bonnet, Damien; Boddaert, Nathalie; Pushparajan, Kuberan; Uribe, Sergio; Raimondi, Francesca
    Aims In this study, we will describe a comprehensive haemodynamic analysis and its relationship to the dilation of the aorta in transposition of the great artery (TGA) patients post-arterial switch operation (ASO) and controls using 4D-flow magnetic resonance imaging (MRI) data. Methods and results Using 4D-flow MRI data of 14 TGA young patients and 8 age-matched normal controls obtained with 1.5 T GE-MR scanner, we evaluate 3D maps of 15 different haemodynamics parameters in six regions; three of them in the aortic root and three of them in the ascending aorta (anterior-left, -right, and posterior for both cases) to find its relationship with the aortic arch curvature and root dilation. Differences between controls and patients were evaluated using Mann-Whitney U test, and the relationship with the curvature was accessed by unpaired t-test. For statistical significance, we consider a P-value of 0.05. The aortic arch curvature was significantly different between patients 46.238 +/- 5.581 m(-1) and controls 41.066 +/- 5.323 m(-1). Haemodynamic parameters as wall shear stress circumferential (WSS-C), and eccentricity (ECC), were significantly different between TGA patients and controls in both the root and ascending aorta regions. The distribution of forces along the ascending aorta is highly inhomogeneous in TGA patients. We found that the backward velocity (B-VEL), WSS-C, velocity angle (VEL-A), regurgitation fraction (RF), and ECC are highly correlated with the aortic arch curvature and root dilatation. Conclusion We have identified six potential biomarkers (B-VEL, WSS-C, VEL-A, RF, and ECC), which may be helpful for follow-up evaluation and early prediction of aortic root dilatation in this patient population.