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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., (© 2022. American Society of Emergency Radiology.)

Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demonstrate the validity of fully automated, deep learning BC analysis from abdominal CT examinations, to define demographically adjusted BC reference curves, and to illustrate the advantage of use of these curves compared with standard methods, along with their biologic significance in predicting survival. Materials and Methods After external validation and equivalency testing with manual segmentation, a fully automated deep learning BC analysis pipeline was applied to a cross-sectional population cohort that included any outpatient without a cardiovascular disease or cancer who underwent abdominal CT examination at one of three hospitals in 2012. Demographically adjusted population reference curves were generated for each BC area. The z scores derived from these curves were compared with sex-specific thresholds for sarcopenia by using χ 2 tests and used to predict 2-year survival in multivariable Cox proportional hazards models that included weight and body mass index (BMI). Results External validation showed excellent correlation ( R = 0.99) and equivalency ( P < .001) of the fully automated deep learning BC analysis method with manual segmentation. With use of the fully automated BC data from 12 128 outpatients (mean age, 52 years; 6936 [57%] women), age-, race-, and sex-normalized BC reference curves were generated. All BC areas varied significantly with these variables ( P < .001 except for subcutaneous fat area vs age [ P = .003]). Sex-specific thresholds for sarcopenia demonstrated that age and race bias were not present if z scores derived from the reference curves were used ( P < .001). Skeletal muscle area z scores were significantly predictive of 2-year survival ( P = .04) in combined models that included BMI. Conclusion Fully automated body composition (BC) metrics vary significantly by age, race, and sex. The z scores derived from reference curves for BC parameters better capture the demographic distribution of BC compared with standard methods and can help predict survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

Purpose: To compare procedure duration and patient radiation dose in positron emission tomography/computed tomography (PET/CT) and CT-guided liver tumor ablation procedures., Materials and Methods: In this retrospective, case-control study, 275 patients underwent 368 image-guided ablation procedures to treat 537 tumors. Radiologists used PET/CT guidance for 117 procedures and CT guidance for 251 procedures. PET/CT-guided procedures were performed by one radiologist (C: P.B.S.). All 3 radiologists (A: J.G.S., B: a radiologist who is not an author on this article, and C: P.B.S.) performed CT-guided procedures. Potential confounders included patient demographics, clinical and tumor characteristics, and procedural variables., Results: The mean duration and estimated patient radiation dose of PET/CT-guided procedures performed by radiologist C were 21.5 ± 4.9 minutes longer and 0.7 ± 2.8 mSv higher than CT-guided procedures performed by all radiologists in an unadjusted comparison. Adjusting for confounding, mean duration and estimated dose of PET/CT-guided procedures performed by radiologist C were 28.3 ± 3.8 minutes longer (P < .0001) and 6.2 ± 2.9 mSv higher (P = .03) than CT-guided procedures performed by the same radiologist. Comparing CT-guided procedures performed by all 3 radiologists, adjusted mean durations and estimated patient doses of procedures by the least experienced radiologist, radiologist A, and the second most experienced radiologist, radiologist B, were 24.2 ± 5.1 (P < .0001) and 18.1 ± 8.9 (P = .04) minutes longer and 13.1 ± 3.7 (P < .001) and 14.5 ± 6.4 (P = .02) mSv higher, respectively, than procedures performed by the most experienced radiologist, radiologist C., Conclusions: PET/CT-guided liver ablations had a slightly longer duration with slightly higher estimated patient radiation dose than similar CT-guided liver ablations. Procedure duration and patient dose do not appear to be major impediments to the emerging field of PET/CT-guided tumor ablation., (Copyright © 2019 SIR. Published by Elsevier Inc. All rights reserved.)