Personalized computed tomography – Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence

Abstract

Purpose The aim of this study was to develop an algorithm for automated estimation of patient height and weight during computed tomography (CT) and to evaluate its accuracy in everyday clinical practice.

Materials and methods Depth images of 200 patients were recorded with a 3D camera mounted above the patient table of a CT scanner. Reference values were obtained using a calibrated scale and a measuring tape to train a machine learning algorithm that fits a patient avatar into the recorded patient surface data. The resulting algorithm was prospectively used on 101 patients in clinical practice and the results were compared to the reference values and to estimates by the patient himself, the radiographer and the radiologist. The body mass index was calculated from the collected values for each patient using the WHO formula. A tolerance level of 5 kg was defined in order to evaluate the impact on weight-dependent contrast agent dosage in abdominal CT.

Results Differences between values for height, weight and BMI were non-significant over all assessments (p > 0.83). The most accurate values for weight were obtained from the patient information (R² = 0.99) followed by the automated estimation via 3D camera (R² = 0.89). Estimates by medical staff were considerably less precise (radiologist: R² = 0.78, radiographer: R² = 0.77). A body-weight dependent dosage of contrast agent using the automated estimations matched the dosage using the reference measurements in 65 % of the cases. The dosage based on the medical staff estimates would have matched in 49 % of the cases.

Conclusion Automated estimation of height and weight using a digital twin model from 3D camera acquisitions provide a high precision for protocol design in computer tomography.

Key points: 

• Machine learning can calculate patient-avatars from 3D camera acquisitions.

• Height and weight of the digital twins are comparable to real measurements of the patients.

• Estimations by medical staff are less precise.

• The values can be used for calculation of contrast agent dosage.

Citation Format

• Geissler F, Heiß R, Kopp M et al. Personalized computed tomography – Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence. Fortschr Röntgenstr 2021; 193: 437 – 445

Key words

CT - machine learning - digital twin - 3D camera - automatic data collection

Publikationsverlauf

Eingereicht: 05. Mai 2020

Angenommen: 26. August 2020

Artikel online veröffentlicht:
03. November 2020

• References

• 1 Bundesamt für Strahlenschutz. Röntgendiagnostik: Häufigkeit und Strahlenexposition. https://www.bfs.de/DE/themen/ion/anwendung-medizin/diagnostik/roentgen/haeufigkeit-exposition.html
• 2 Petritsch B, Kosmala A, Gassenmaier T. et al. Diagnostik der akuten Lungenarterienembolie: Vergleich von Single-Source CT und Dritt-Generation Dual-Source CT unter Einsatz eines Dual-Energy Protokolls – Bildqualität und Strahlenexposition. Fortschr Röntgenstr 2017; DOI: 10.1055/s-0043-103089.
• 3 May MS, Eller A, Stahl C. et al. Dose reduction in computed tomography of the chest: image quality of iterative reconstructions at a 50% radiation dose compared to filtered back projection at a 100% radiation dose. Fortschr Röntgenstr 2014; DOI: 10.1055/s-0033-1356254.
• 4 Do TD, Sutter R, Skornitzke S. et al. CT- und MRT-Bildgebung bei orthopädischen Implantaten. Fortschr Röntgenstr 2018; DOI: 10.1055/s-0043-118127.
• 5 Schäfer SB, Rudolph C, Kolodziej M. et al. Optimierung von Ganzkörper-CT-Untersuchungen an Polytraumatisierten anhand des Vergleichs mit den aktuellen diagnostischen Referenzwerten. Fortschr Röntgenstr 2019; DOI: 10.1055/a-0881-3113.
• 6 George AJ, Manghat NE, Hamilton MCK. Comparison between a fixed-dose contrast protocol and a weight-based contrast dosing protocol in abdominal CT. Clin Radiol 2016; DOI: 10.1016/j.crad.2016.07.009.
• 7 Benbow M, Bull RK. Simple weight-based contrast dosing for standardization of portal phase CT liver enhancement. Clin Radiol 2011; DOI: 10.1016/j.crad.2010.12.022.
• 8 Feng ST, Zhu H, Peng Z. et al. An Individually Optimized Protocol of Contrast Medium Injection in Enhanced CT Scan for Liver Imaging. Contrast Media Mol Imaging 2017; DOI: 10.1155/2017/7350429.
• 9 Svensson A, Thor D, Fischer MA. et al. Dual source abdominal computed tomography: the effect of reduced X-ray tube voltage and intravenous contrast media dosage in patients with reduced renal function. Acta Radiol 2019; DOI: 10.1177/0284185118783213.
• 10 Megibow Alec J, Jacob G, Heiken JP. Quantitative and Qualitative Evaluation of Volume of Low Osmolality Contrast Medium Needed for Routine Helical Abdominal CT. Am J Roentgenol 2001; DOI: 10.2214/ajr.176.3.1760583.
• 11 Perrin E, Jackson M, Grant R. et al. Weight-adapted iodinated contrast media administration in abdomino-pelvic CT: Can image quality be maintained?. Radiography (Lond) 2018; DOI: 10.1016/j.radi.2017.08.011.
• 12 Pfitzner C, May S, Nüchter A. Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data. Sensors (Basel, Switzerland) 2018; DOI: 10.3390/s18051311.
• 13 Saltybaeva N, Schmidt B, Wimmer A. et al. Precise and Automatic Patient Positioning in Computed Tomography: Avatar Modeling of the Patient Surface Using a 3-Dimensional Camera. Invest Radiol 2018; DOI: 10.1097/RLI.0000000000000482.
• 14 Toth T, Ge Z, Daly MP. The influence of patient centering on CT dose and image noise. Med Phys 2007; DOI: 10.1118/1.2748113.
• 15 Statistisches Bundesamt. Körpermaße nach Altersgruppen und Geschlecht. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/liste-koerpermasse.html
• 16 Descoteaux M, Maier-Hein L, Franz A. et al. Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017. Cham: Springer International Publishing; 2017. DOI: 10.1007/978-3-319-66185-8
• 17 Kondo H, Kanematsu M, Goshima S. et al. Abdominal multidetector CT in patients with varying body fat percentages: estimation of optimal contrast material dose. Radiology 2008; DOI: 10.1148/radiol.2492080033.
• 18 Fleischmann D, Kamaya A. Optimal vascular and parenchymal contrast enhancement: the current state of the art. Radiol Clin North Am 2009; DOI: 10.1016/j.rcl.2008.10.009.
• 19 Boland GWL, Houghton MP, Marchione DG. et al. Maximizing outpatient computed tomography productivity using multiple technologists. Journal of the American College of Radiology: JACR 2008; DOI: 10.1016/j.jacr.2007.07.009.
• 20 Bégin A, Martel G, Lapointe R. et al. Accuracy of preoperative automatic measurement of the liver volume by CT-scan combined to a 3D virtual surgical planning software (3DVSP). Surg endosc 2014; DOI: 10.1007/s00464-014-3611-x.
• 21 Vauthey JN, Chaoui A, Do KA. et al. Standardized measurement of the future liver remnant prior to extended liver resection: methodology and clinical associations. 2000; DOI: 10.1067/msy.2000.105294.
• 22 Hoffmann RT, Jakobs TF, Tatsch K. et al. Selektive interne Radiotherapie bei fortgeschrittenen Lebertumoren und Metastasen. Dtsch Med Wochenschr 2008; DOI: 10.1055/s-0028-1091244.
• 23 Urata K, Kawasaki S, Matsunami H. et al. Calculation of child and adult standard liver volume for liver transplantation. Hepatology 1995; DOI: 10.1002/hep.1840210515.
• 24 Bundesamt für Strahlenschutz. Leitfaden zur Handhabung der diagnostischen Referenzwerte in der Röntgendiagnostik. https://www.bfs.de/SharedDocs/Downloads/BfS/DE/fachinfo/ion/leitfaden-drw-roe.pdf?__blob=publicationFile&v=11