Three-dimensional printed flow phantom model of the carotid artery in preterm infants for training and research
DOI:
https://doi.org/10.14738/jbemi.86.11434Keywords:
3-dimensional, 3D printed, flow volume measurements, flow phantom, carotid artery, preterm infant, ultrasoundAbstract
The aim of this study was to perform flow volume measurements with Doppler ultrasound using novel 3D printed flow phantom models of carotid artery in preterm infants with varying characteristics.
Clinical data from cerebral blood flow measurements using Doppler ultrasound of the right common carotid artery from premature newborn infants were used to produce a 3D printed Doppler flow phantom model with three different vessel diameters; 0.158 cm, 0.196 cm and 0.244 cm. Leading edge to centre was used to measure vessel diameter. Two observers performed flow volume measurements using continuous and pulsatile flow. Agreement between observers was examined using Bland-Altman plots.
24 measurements were performed. 18 (75%) measurements were performed using continuous flow. Pulsatile flow measurements were performed on lumen diameter of 0.244 cm only using physiological rates. Bland-Altman analysis for continuous flow measurements for observer 1 and 2 were -0.007 (95%LOA -4.3 to 4.3) ml/min and 3.2 (95%LOA -2.7 to 9.1) ml/min. Bias for pulsatile flow measurements for observer 1 and 2 were 1.5 (95%LOA -0.8 to 3.8) ml/min and 4.6 (0.7 to 8.5) ml/min respectively. Inter and intra-observer reliability was excellent for majority of measurements. The mean coefficient of variation for inter observer diameter measurements was 1.2% and intra observer measurements were between 1.5% to 3.9% for both observers.
Flow volume measurements performed using 3D printed materials resulted in realistic echogenicities mimicking biological tissues. Validity and reliability studies, within and between, observers showed acceptable results. Researchers and clinicians can use this model for further training and simulation.
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Copyright (c) 2021 Sujith Pereira, Jonathan Reeves, Malcolm Birch, Ahmed Ali, Ajay Sinha, Stephen Kempley
This work is licensed under a Creative Commons Attribution 4.0 International License.
