Back to Search
Start Over
Determining the Optimal Energy Level of Virtual Monoenergetic Images in Dual-Source CT for Diagnosis of Bowel Obstruction and Colitis
- Source :
- Diagnostics, Vol 13, Iss 23, p 3491 (2023)
- Publication Year :
- 2023
- Publisher :
- MDPI AG, 2023.
-
Abstract
- Images from 64 patients undergoing an enhanced abdominal-pelvis scan at portal phase in dual-energy CT mode for the diagnosis of colitis or bowel obstruction were retrospectively analyzed. Acquisitions were performed on a third-generation dual-source CT (DSCT) 100/Sn150 kVp. Mixed images were generated, as well as virtual monoenergetic images (VMIs) at 40/50/60/70 keV. Objective image quality was assessed on VMIs and mixed images by measuring contrast, noise and contrast-to-noise ratio (CNR). Noise, smoothing and overall image quality were subjectively analyzed by two radiologists using Likert scales. For both patient groups, the noise decreased significantly according to the energy level from 40 to 60 keV by −47.2 ± 24.0% for bowel obstruction and −50.4 ± 18.2% for colitis. It was similar between 60 and 70 keV (p = 0.475 and 0.059, respectively). Noise values were significantly higher in VMIs than in mixed images, except for 70 keV (p = 0.53 and 0.071, respectively). Similar results were observed for contrast values, with a decrease between 40 and 70 keV of −56.3 ± 7.9% for bowel obstruction −56.2 ± 10.9% for colitis. The maximum CNR value was found at 60 keV compared to other energy levels and mixed images, but there was no significant difference with the other energy levels apart from 70 keV (−9.7 ± 9.8%) for bowel obstruction and 40 keV (−6.6 ± 8.2%) and 70 keV (−5.8 ± 9.2%) for colitis. The VMIs at 60 keV presented higher scores for all criteria for bowel obstruction and colitis, with no significant difference in smoothing score compared to mixed images (p = 0.119 and p = 0.888, respectively).
Details
- Language :
- English
- ISSN :
- 13233491 and 20754418
- Volume :
- 13
- Issue :
- 23
- Database :
- Directory of Open Access Journals
- Journal :
- Diagnostics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.9b9543a04eaf4e3e8ae15c29540f558f
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/diagnostics13233491