Every year, 1 million people in the US will experience a heart attack or sudden cardiac death. A large percentage of these patients have no prior symptoms of any kind but suffer from silent heart disease (ischemia), which may cause a heart attack at any time. Currently there is no reliable screening method to identify people who may have silent heart disease and therefore are at risk of """"""""heart attack"""""""". We seek to extract additional data from a non-invasive Computed Tomography (CT) scan of the heart (commonly used in most imaging centers), which will better identify patients who are """"""""vulnerable"""""""" to sudden heart attack. These images are acquired without contrast agents, as a common screening test for measuring coronary calcium, another clinical marker of coronary artery disease. We will prove that additional information about cardiovascular risk can be obtained by quantifying pericardial fat surrounding the heart in these cardiac CT images. Although this information exists in the cardiac CT scans acquired today, it is ignored in clinical practice because currently there are no tools to measure it automatically and reliably, and no information regarding its significance for a given patient. We will develop fully automated, accurate, new computer software for quantitation of pericardial fat from cardiac CT images acquired for routine assessment of coronary calcium. We plan to achieve complete automation and accuracy, by applying a combination of robust and effective algorithms in machine learning with anatomical knowledge.
We aim to prove that the developed automatic software will be as accurate, and more reproducible than experienced imaging physicians in quantifying pericardial fat, and will accomplish the measurement in a fraction of the time. Such software could be immediately made available worldwide to standard imaging workstations. For the first time, this will allow its use as a standard practical imaging tool for cardiovascular risk assessment, without requiring additional time, imaging or radiation risk to the patient. We will also show that this new quantitative information has important additional value compared to other clinical cardiovascular risk factors and information from blood samples (such as cholesterol, glucose and CRP), and the amount of calcium in the patient's coronary arteries seen on CT scans, and establish its significance in the prediction of silent heart disease.
This new research will allow accurate prediction of who might be more likely to have heart disease which may ultimately cause a heart attack. It will allow doctors to identify patients for whom appropriate treatment could be prescribed to avoid a heart attack, or more extensive testing, could be recommended, to confirm or rule out heart disease.
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