In:
Journal of Computer Assisted Tomography, Ovid Technologies (Wolters Kluwer Health), Vol. 44, No. 1 ( 2020-1), p. 138-144
Abstract:
The aim of this study was to determine the influence of virtual monoenergetic images (vMEIs) on renal cortex volumetry (RCV) and estimation of split-renal function. Methods Twenty-five patients (mean ± SD, 64.7 ± 9.9 years) underwent a contrast-enhanced dual-layer spectral detector computed tomography. Images were reconstructed with a reference standard (iterative model reconstruction, IMR Ref ), a newly spectral detector computed tomography algorithm (SP con ) and vMEI at 40, 60, 80, 100, and 120 keV. Two blinded independent readers performed RCV on all data sets with a semiautomated tool. Results Total kidney volume was up to 15% higher in vMEI at 40/60 keV compared with IMR Ref ( P 〈 0.001). Total kidney volume with vMEI at 80/100 keV was similar to IMR Ref ( P 〈 0.001). Split-renal function was similar in all reconstructions at approximately 50% ± 3%. Bland-Altman analysis showed no significant differences ( P 〉 0.05), except for 40 keV versus SP con ( P 〈 0.05). The time required to perform RCV was reasonable, approximately 4 minutes, and showed no significant differences among reconstructions. Interreader agreement was greatest with vMEI at 80 keV ( r = 0.68; 95% confidence interval, 0.39–0.85; P 〈 0.0002) followed by IMR Ref images ( r = 0.67; 95% confidence interval, 0.37–0.84; P 〈 0.0003). IMR Ref showed the highest mean Hounsfield unit for cortex/medulla of 223.4 ± 73.7/62.5 ± 19.7 and a ratio of 3.7. Conclusions Semiautomated RCV performed with vMEI and IMR Ref /SP con is feasible and showed no clinically relevant differences with regard to split-renal function. Low–kiloelectron volt vMEI showed greater tissue contrast and total kidney volume but no benefit for RCV. Moderate–kiloelectron volt vMEI (80 keV) results were similar to IMR Ref with a faster postprocessing time.
Type of Medium:
Online Resource
ISSN:
1532-3145
,
0363-8715
DOI:
10.1097/RCT.0000000000000952
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2020
detail.hit.zdb_id:
2039772-0
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