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Cardiovascular PET-CT
Published in Yi-Hwa Liu, Albert J. Sinusas, Hybrid Imaging in Cardiovascular Medicine, 2017
Etienne Croteau, Ran Klein, Jennifer M. Renaud, Manuja Premaratne, Robert A. Dekemp
Nitrates are typically administered immediately prior to CTA for coronary vasodilatation and enhancement of image quality; 400–800 μg of sublingual nitrates is given a few minutes prior to the scan (Abbara et al. 2009). Hypotension is to be expected, but as the procedure is performed in a supine position, this is generally safe. Contraindications to nitrates include erectile dysfunction medications: sildenafil, vardenafil, or tadalafil, or sildenafil taken for pulmonary hypertension. Other contraindications relate to deleterious consequences of systemic vasodilatation. These are inferior wall MI with right ventricle involvement, pronounced hypovolemia, raised intracranial pressure, cardiac tamponade, constrictive pericarditis, severe aortic stenosis, and hypertrophic obstructive cardiomyopathy. Breath-holding at end-inspiration is important with regard to minimizing respiratory motion; explicit instructions with a practice scan are recommended.
Asbestos-related pleural disease
Published in Dorsett D. Smith, The Health Effects of Asbestos, 2015
Asbestos-related pleuritis may rarely involve the pericardium and cause asbestos-related pericarditis, pericardial effusion, and constrictive pericarditis, as can a malignant mesothelioma. In my clinical experience, mesothelioma involving the pericardium is the most common cause of pericardial disease in the asbestos-exposed population and must always be excluded prior to making a diagnosis of a benign asbestos-related pericardial effusion. (Davies D, Andrews MI, Jones JS. Asbestos induced pericardial effusion and constrictive pericarditis. Thorax 1991;46(6):429–32; Abejie BA, Chung EH, Nesto RW, Kales SN. Grand rounds: Asbestos-related pericarditis in a boiler operator. Environ Health Perspect 2008;116(1):86–9; Roggeri A, Tomasi C, Cavazza A, Serra L, Zucchi L. Haemorrhagic pericardial effusion in an asbestos worker. Med Lav 2003;94(4):391–4; Fernandes R, Nosib S, Thomson D, Baniak N. A rare cause of heart failure with preserved ejection fraction: Primary pericardial mesothelioma masquerading as pericardial constriction. BMJ Case Rep 2014; Belli E, Landolfo K. Primary pericardial mesothelioma: A rare cause of constrictive pericarditis. Asian Cardiovasc Thorac Ann 2015;23(5):599–600.)
Pleural disease induced by drugs
Published in Philippe Camus, Edward C Rosenow, Drug-induced and Iatrogenic Respiratory Disease, 2010
Cabergoline, a long-acting tetracyclic ergot derivative agonist, acts through dopamine antagonism, and is commonly used in the treatment of parkinsonism. Rarely, cabergoline and other ergoline drugs have been reported to cause a pleuropulmonary inflammatory–fibrotic reaction.15,16 Subacute constrictive pericarditis requiring pericardiectomy has also been reported.17 Drug dosage appears to be an important factor for the inflammation and fibrosis related to the ergoline drugs.15,16 Long-term administration of ergolines appears to be a consistent finding in patients who develop the inflammatory–fibrotic reactions and appears to be unusual with less than 6 months of therapy. The mechanism of ergoline-induced pleural fibrosis is unclear; however, a serotonin-related mechanism has been suggested. Although the drug should be discontinued if pulmonary symptoms develop, worsening may still occur owing to the long half-life of the drug.
Artificial intelligence: a new clinical support tool for stress echocardiography
Published in Expert Review of Medical Devices, 2018
Maryam Alsharqi, Ross Upton, Angela Mumith, Paul Leeson
The application of artificial intelligence in the clinical practice of echocardiography has been less advanced than in some other areas of medical imaging. Every echocardiogram generates multifaceted and complex information within the image, which is mostly filtered by the eye of the operator when being interpreted or measured. Therefore, potentially useful data that could be used for quantification of cardiac structure and function, or used for diagnosis, may be missed or overlooked [8]. Recent applications of artificial intelligence in echocardiography have shown promise in the field of automated image selection and quantification. The left ventricle appears in multiple echocardiographic views and a deep learning model was able to recognize 15 major transthoracic echocardiography views accurately, including continuous and pulsed wave Doppler traces [12]. Automated quantification or border recognition of left and right ventricular function could then be possible using demonstrated techniques [13,14]. Valvular morphological quantification also appears to be possible using automated machine learning analysis of 3D transoesophageal echocardiography images of the mitral valve. From this analysis it was feasible to achieve reproducible measurements of mitral valve annulus without significant user intervention [15]. Image interpretation is a distinct task that may also be tackled with artificial intelligence approaches. Machine learning models have provided efficient differentiation of cardiovascular hypertrophic phenotypes including those with hypertrophic cardiomyopathy and athletes [8]. Classification of constrictive pericarditis and restrictive cardiomyopathy has been shown to be possible particularly when conventional echocardiography parameters were combined with parameters obtained using speckle tracking echocardiography [10].