Ultrasound-guided needle insertions at the site of the internal jugular vein (IJV) are routinely performed to access the central venous system. Ultrasound-guided insertions maintain high rates of carotid artery puncture, as clinicians rely on 2D information to perform a 3D procedure. The limitations of 2D ultrasound-guidance motivated the research question: “Do 3D ultrasound-based environments improve IJV needle insertion accuracy”. We addressed this by developing advanced surgical navigation systems based on tracked surgical tools and ultrasound with various visualizations. The point-to-line ultrasound calibration enables the use of tracked ultrasound. We automated the fiducial localization required for this calibration method such that fiducials can be automatically localized within 0.25 mm of the manual equivalent. The point-to-line calibration obtained with both manual and automatic localizations produced average normalized distance errors less than 1.5 mm from point targets. Another calibration method was developed that registers an optical tracking system and the VIVE Pro head-mounted display (HMD) tracking system with sub-millimetre and sub-degree accuracy compared to ground truth values. This co-calibration enabled the development of an HMD needle navigation system, in which the calibrated ultrasound image and tracked models of the needle, needle trajectory, and probe were visualized in the HMD. In a phantom experiment, 31 clinicians had a 96 % success rate using the HMD system compared to 70 % for the ultrasound-only approach (p= 0.018). We developed a machine-learning-based vascular reconstruction pipeline that automatically returns accurate 3D reconstructions of the carotid artery and IJV given sequential tracked ultrasound images. This reconstruction pipeline was used to develop a surgical navigation system, where tracked models of the needle, needle trajectory, and the 3D z-buffered vasculature from a phantom were visualized in a common coordinate system on a screen. This system improved the insertion accuracy and resulted in 100 % success rates compared to 70 % under ultrasound-guidance (p=0.041) across 20 clinicians during the phantom experiment. Overall, accurate calibrations and machine learning algorithms enable the development of advanced 3D ultrasound systems for needle navigation, both in an immersive first-person perspective and on a screen, illustrating that 3D US environments outperformed 2D ultrasound-guidance used clinically
