Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.

Autor: Toia GV; University of Wisconsin School of Medicine and Public Health, Madison, USA. GToia@uwhealth.org., Garret JW; University of Wisconsin School of Medicine and Public Health, Madison, USA., Rose SD; The University of Texas Health Science Center at Houston, Houston, USA., Szczykutowicz TP; University of Wisconsin School of Medicine and Public Health, Madison, USA., Pickhardt PJ; University of Wisconsin School of Medicine and Public Health, Madison, USA.
Jazyk: angličtina
Zdroj: Abdominal radiology (New York) [Abdom Radiol (NY)] 2024 Dec 07. Date of Electronic Publication: 2024 Dec 07.
DOI: 10.1007/s00261-024-04733-7
Abstrakt: Purpose: To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).
Methods: This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100 keV). Fully automated algorithms for quantifying CT number (HU) in abdominal fat (subcutaneous and visceral), skeletal muscle, bone, calcium (abdominal Agatston score), and organ size (area or volume) were applied. Biomarker median difference relative to 70 keV and interquartile range were reported by energy level to characterize variation. Linear regression was performed to calibrate non-70 keV data and to estimate their equivalent 70 keV biomarker attenuation values.
Results: Relative to 70 keV, absolute median differences in attenuation-based biomarkers (excluding Agatston score) ranged 39-358, 12-102, 5-48, 9-75 HU for 40, 55, 85, 100 keV, respectively. For area-based biomarkers, differences ranged 6-15, 3-4, 2-7, 0-5 cm 2 for 40, 55, 85, 100 keV. For volume-based biomarkers, differences ranged 12-34, 8-68, 12-52, 1-57 cm 3 for 40, 55, 85, 100 keV. Agatston score behavior was more spurious with median differences ranging 70-204 HU. In general, VMI < 70 keV showed more variation in median biomarker measurement than VMI > 70 keV.
Conclusion: This study characterized the behavior of a fully automated AI CT biomarker toolkit across varying VMI levels obtained with DECT. The data showed relatively little biomarker value change when measured at or greater than 70 keV. Lower VMI datasets should be avoided due to larger deviations in measured value as compared to 70 keV, a level considered equivalent to conventional 120 kVp exams.
Competing Interests: Declarations. Competing interests: GVT: Consultant for GE Healthcare and Canon Medical. JWG: Advisor to RadUnity. SDR: None. TPS: Receives research support from Canon Medical Systems and GE HealthCare, consulting fees from Alara Imaging, Imalogix, Aidoc; royalties from Medical Physics Publishing and royalties related to intellectual property from Qaelum; founder of RadUnity. PJP: Advisor to Nanox-AI, Bracco Diagnostics, and GE HealthCare.
(© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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