Browsing by Author "Botnar, Rene"

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    Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?
    (FRONTIERS MEDIA SA, 2022) Fotaki, Anastasia; Puyol Anton, Esther; Chiribiri, Amedeo; Botnar, Rene; Pushparajah, Kuberan; Prieto, Claudia
    Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.
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    Assessment of hepatic fatty acids during non-alcoholic steatohepatitis progression using magnetic resonance spectroscopy
    (2021) Xavier, Aline; Zacconi, Flavia C. M.; Santana Romo, Fabián Mauricio; Eykyn, Thomas R.; Lavin, Begona; Phinikaridou, Alkystis; Botnar, Rene; Uribe, Sergio; Esteban Oyarzun, Juan; Cabrera, Daniel; Arrese, Marco; Andia, Marcelo E.
    Abstract: Introduction and objectives: Non-alcoholic fatty liver disease (NAFLD) encompasses a spectrum of liver abnormalities including steatosis, steatohepatitis, fibrosis, and cirrhosis. Liver biopsy remains the gold standard method to determine the disease stage in NAFLD but is an invasive and risky procedure. Studies have previously reported that changes in intrahepatic fatty acids (FA) composition are related to the progression of NAFLD, mainly in its early stages. The aim of this study was to characterize the liver FA composition in mice fed a Choline-deficient L-amino-defined (CDAA) diet at different stages of NAFLD using magnetic resonance spectroscopy (MRS). Methods: We used in-vivo MRS to perform a longitudinal characterization of hepatic FA changes in NAFLD mice for 10 weeks. We validated our findings with ex-vivo MRS, gas chromatography-mass spectrometry and histology. Results: In-vivo and ex-vivo results showed that livers from CDAA-fed mice exhibit a significant increase in liver FA content as well as a change in FA composition compared with control mice. After 4 weeks of CDAA diet, a decrease in polyunsaturated and an increase in monounsaturated FA were observed. These changes were associated with the appearance of early stages of steatohepatitis, confirmed by histology (NAFLD Activity Score (NAS) = 4.5). After 10 weeks of CDAA-diet, the liver FA composition remained stable while the NAS increased further to 6 showing a combination of early and late stages of steatohepatitis. Conclusion: Our results suggest that monitoring lipid composition in addition to total water/fat with MRS may yield additional insights that can be translated for non-invasive stratification of high-risk NAFLD patients.
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    Synergistic multi-contrast cardiac magnetic resonance image reconstruction
    (ROYAL SOC, 2021) Qi, Haikun; Cruz, Gastao; Botnar, Rene; Prieto, Claudia
    Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed.