Abstract
Computed tomography coronary angiography (CTCA) is a non-invasive, powerful image processing technique for assessing coronary artery disease. The aim of the paper is to evaluate the diagnostic role of CTCA using optimal scanning parameters and to investigate the effect of low kilovoltage CTCA on the qualitative and quantitative image parameters and radiation dose in overweight and obese patients. Consolidation of knowledge in medicine and image processing was used to achieve the aim, and performance was evaluated in a clinical setting. Elevated body mass index is one of the factors causing increased radiation dose to patients. This study examined the feasibility of 80-kV and 100-kV CTCA in overweight and obese adult patients, comparing radiation doses and image quality versus standardized 100-kV protocols in the group of overweight patients and 120-kV CTCA in the group of obese patients. Qualitative and quantitative image parameters were determined in proximal and distal segments of the coronary arteries. Quantitative assessment was determined by the contrast-to-noise ratio and signal-to-noise ratio. The results of the study showed that in overweight and obese patients, the low dose protocol affords radiation dose reduction of 35% and 41%, respectively. Image quality was found to be diagnostically acceptable in all cases.
Highlights
In recent years, there has been a rapid development of data analysis, computer science, and medicine
Our study demonstrates that in overweight and obese patients, low dose protocol affords radiation dose reduction of 35% and 41%, respectively
Our study perfectly demonstrates that in evaluated overweight and obese patients, the low dose protocol resulted in radiation dose reduction of 35% and 41%, while image quality was diagnostically acceptable in all cases
Summary
There has been a rapid development of data analysis, computer science, and medicine. New research areas and new trends are emerging, such as data mining, knowl-. Edge discovery, deep learning, and image processing. Data science is a multidisciplinary subject that includes data mining, big data, data analytics, machine learning, and data knowledge discovery. Data science combines three highly iterative research areas: mathematics/statistics/operational research, computer science, and digital technologies used to study and perceive data (Dzemyda, 2018). Data science is finding new applications: biotechnology, materials microscopy, geographic research, learning analytics, radiology, and many others. The multidisciplinary efforts of data scientists, medical doctors, regulators and health insurance organizations are increasingly required. Computational methods need to move forward to bring direct benefit to clinical practice
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