Abstract
The purpose of this study was to assess whether habitus and organ enhancement influence iodine subtraction and should be incorporated into spectral subtraction algorithms. This study included 171 patients. In the unenhanced phase, MDCT was performed with single-energy acquisition (120 kVp, 250 mAs) and in the parenchymal phase with dual-energy acquisitions (80 kVp, 499 mAs; 140 kVp, 126 mAs). Habitus was determined by measuring trunk diameters and calculating circumference. Iodine subtraction was performed with input parameters individualized to muscle, fat, and blood ratio. Attenuation of the liver, pancreas, spleen, kidneys, and aorta was assessed in truly and virtually unenhanced image series. Pearson analysis was performed to correlate habitus with the input parameters. Analysis of truly unenhanced and virtually unenhanced images was performed with the Student t test; magnitude of variation was evaluated with Bland-Altman plots. Correction strategies were derived from organ-specific regression analysis of scatterplots of truly unenhanced and virtually unenhanced attenuation and implemented in a pixel-by-pixel approach. Analysis of individual organ correction and truly unenhanced attenuation was performed with the Student t test. The correlations between habitus and blood ratio (r = 0.694) and attenuation variation of fat at 80 kVp (r = -0.468) and 140 kV (r = -0.454) were confirmed. Although overall mean attenuation differed by no more than 10 HU between truly and virtually unenhanced scans overall, these differences varied by organ and were large in individual patients. Paired comparisons of truly and virtually unenhanced measurements differed significantly for liver, spleen, pancreas, kidneys, and aortic blood pool (p < 0.001 for all comparisons), but paired comparisons of truly unenhanced and individually organ-corrected measurements did not differ when organ- and habitus-based correction strategies were applied (p > 0.38 for all comparisons). Habitus and organ enhancement influence virtually unenhanced imaging and should be incorporated into spectral subtraction algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.