Automated CT reformations reduce time and variability in trauma panscan exam completion.
Emerg Radiol
; 29(3): 461-469, 2022 Jun.
Article
en En
| MEDLINE
| ID: mdl-35237937
PURPOSE: To test the hypothesis that an automated post-processing workflow reduces trauma panscan exam completion times and variability. METHODS: One-hundred-fifty consecutive trauma panscans performed between June 2018 and December 2019 were included, half before and half after implementation of an automated software-driven post-processing workflow. Acquisition and reconstruction timestamps were used to calculate total examination time (first acquisition to last reformation), setup time (between the non-contrast and contrast-enhanced acquisitions), and reconstruction time (for the contrast-enhanced reconstructions and reformations). The performing technologist was recorded and accounted for in analyses using linear mixed models to assess differences between the pre- and post-intervention groups. RESULTS: Exam, setup, and recon times were (mean ± standard deviation) 33.5 ± 4.6, 9.2 ± 2.4, and 23.6 ± 4.7 min before and 27.8 ± 1.5, 8.9 ± 1.4, and 18.9 ± 1.7 min after intervention. These reductions of 5.7 and 4.7 min in the mean exam and recon times were statistically significant (p < 0.001) while the setup time was not (p = 0.49). The reductions in standard deviation were statistically significant for exam and recon times (p < 0.0001) but not for setup time (p = 0.13). All automated panscans were completed within 36 min, versus 65% with the traditional workflow. CONCLUSION: Automation of image reconstruction workflow significantly decreased mean exam and reconstruction times as well as variability between exams, thus facilitating a consistently rapid imaging assessment, and potentially reducing delays in critical management decisions.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Tomografía Computarizada por Rayos X
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Emerg Radiol
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos