Article ; Online: Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction.
Tomography (Ann Arbor, Mich.)
2023 Volume 9, Issue 3, Page(s) 1019–1028
Abstract: Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detailed images of the coronary arteries. Our work focuses on the optimization of the prospectively ECG-triggered scan technique, which delivers the radiation ... ...
| Abstract | Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detailed images of the coronary arteries. Our work focuses on the optimization of the prospectively ECG-triggered scan technique, which delivers the radiation efficiently only during a fraction of the R-R interval, matching the aim of reducing radiation dose in this increasingly used radiological examination. In this work, we analyzed how the median DLP (Dose-Length Product) values for CCTA of our Center decreased significantly in recent times mainly due to a notable change in the technology used. We passed from a median DLP value of 1158 mGy·cm to 221 mGy·cm for the whole exam and from a value of 1140 mGy·cm to 204 mGy·cm if considering CCTA scanning only. The result was obtained through the association of important factors during the dose imaging optimization: technological improvement, acquisition technique, and image reconstruction algorithm intervention. The combination of these three factors allows us to perform a faster and more accurate prospective CCTA with a lower radiation dose. Our future aim is to tune the image quality through a detectability-based study, combining algorithm strength with automatic dose settings. |
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| MeSH term(s) | Computed Tomography Angiography/methods ; Coronary Angiography/methods ; Prospective Studies ; Deep Learning ; Radiation Dosage ; Radiation Exposure/prevention & control ; Image Processing, Computer-Assisted |
| Language | English |
| Publishing date | 2023-05-16 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ISSN | 2379-139X |
| ISSN (online) | 2379-139X |
| DOI | 10.3390/tomography9030083 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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