Alberto A Perez1, Perry J Pickhardt2,3, Daniel C Elton4, Veit Sandfort4, Ronald M Summers4. 1. The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA. 2. The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA. ppickhardt2@uwhealth.org. 3. Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI, 53792-3252, USA. ppickhardt2@uwhealth.org. 4. Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Abstract
PURPOSE: Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures. MATERIALS AND METHODS: Initial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r2) was generated for each comparison. RESULTS: Mean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm2, 18.8 ± 6.0 HU, - 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r2 = 0.72), MA (pre = 0.98 × post; r2 = 0.92), M-HU (pre = 0.75 × post + 5.7; r2 = 0.75), and V/S (pre = 1.11 × post; r2 = 0.94); p < 0.001 for all r2 values. There were no significant trends according to patient age or gender that required further correction. CONCLUSION: Fully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.
PURPOSE: Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures. MATERIALS AND METHODS: Initial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r2) was generated for each comparison. RESULTS: Mean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm2, 18.8 ± 6.0 HU, - 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r2 = 0.72), MA (pre = 0.98 × post; r2 = 0.92), M-HU (pre = 0.75 × post + 5.7; r2 = 0.75), and V/S (pre = 1.11 × post; r2 = 0.94); p < 0.001 for all r2 values. There were no significant trends according to patient age or gender that required further correction. CONCLUSION: Fully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.
Authors: Robin F Gohmann; Batuhan Temiz; Patrick Seitz; Sebastian Gottschling; Christian Lücke; Christian Krieghoff; Christian Blume; Matthias Horn; Matthias Gutberlet Journal: Quant Imaging Med Surg Date: 2021-10
Authors: Perry J Pickhardt; Alberto A Perez; John W Garrett; Peter M Graffy; Ryan Zea; Ronald M Summers Journal: AJR Am J Roentgenol Date: 2021-08-18 Impact factor: 6.582
Authors: Perry J Pickhardt; Thang Nguyen; Alberto A Perez; Peter M Graffy; Samuel Jang; Ronald M Summers; John W Garrett Journal: Radiol Artif Intell Date: 2022-08-31
Authors: Perry J Pickhardt; Ronald M Summers; Hima Tallam; Daniel C Elton; Sungwon Lee; Paul Wakim Journal: Radiology Date: 2022-04-05 Impact factor: 29.146
Authors: Perry J Pickhardt; Peter M Graffy; Alberto A Perez; Meghan G Lubner; Daniel C Elton; Ronald M Summers Journal: Radiographics Date: 2021 Mar-Apr Impact factor: 5.333
Authors: Alberto A Perez; Victoria Noe-Kim; Meghan G Lubner; Peter M Graffy; John W Garrett; Daniel C Elton; Ronald M Summers; Perry J Pickhardt Journal: Radiology Date: 2021-10-26 Impact factor: 11.105
Authors: Brian A Derstine; Sven A Holcombe; Brian E Ross; Nicholas C Wang; Stewart C Wang; Grace L Su Journal: Sci Rep Date: 2022-02-11 Impact factor: 4.379