| Title |
Image quality in computed tomography coronary angiography and radiation dose reduction / |
| Authors |
Golubickas, Domas ; Lukoševičius, Saulius ; Tamakauskas, Vytenis ; Dobrovolskienė, Laima ; Basevičienė, Ingrida ; Grib, Liviu ; Ragaišytė, Nijolė ; Leonavičius, Rytis ; Medvedev, Viktor ; Veikutis, Vincentas |
| DOI |
10.15388/21-INFOR464 |
| Full Text |
|
| Is Part of |
Informatica.. Vilnius : Vilniaus universitetas. 2021, vol. 32, iss. 4, p. 741-757.. ISSN 0868-4952. eISSN 1822-8844 |
| Keywords [eng] |
image quality ; data analysis ; computed tomography ; coronary angiography ; radiation dose ; optimal scanning parameters |
| Abstract [eng] |
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. |
| Published |
Vilnius : Vilniaus universitetas |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
