Introduction: In daily practice, at interventional radiology (IR), percutaneous needle placement (e.g. biopsy and ablation) is generally performed under real time guidance of morphological imaging (US or CT). However, in some cases obtaining representative specimen can be challenging causing repeated biopsies or shift to open procedures. And so alternatively, needle placement can occur using computer-assisted needle navigation strategies that rely on pre-interventional images such as (PET-) CT scans. To unravel the impact of needle navigation strategies in addition to the use of ultrasound guidance, we recorded and analyzed the needle- and US-movements during exercises performed in a custom abdominal biopsy phantom.

Methods: custom abdominal biopsy phantom was generated using synthetic ballistic gel, bone-like structures, spherical targets and fiducials for both electromagnetic (EM) and optical tracking. Following administration of a mixture containing 18F-FDG and 99mTcO4- and prior to the needle interventions, the phantom was subjected to PET-CT and SPECT-CT imaging. After presenting them with the 3D imaging data-sets interventions were performed by both novices and experts (n = 20 in total). The exercise comprised of three consecutive steps: 1) US guided biopsy, 2) US guided PET-CT-navigated biopsy (US + Nav), 3) US guided PET-CT-navigated biopsy + virtual needle guidance (US + Nav + NG). Each step was performed on a different target and the exercise continued until the participant was successful. In all cases, we recorded the traveled paths determined in 3D (i.e., x, y, and z) of both the needle and the ultrasound probe. Tracking was realized by using a fiducial-based optical tracking system. Following the visualization of the traveled paths in MATLAB® (the MathWorks, Inc), we used custom software to analyze movement features such as speed, acceleration, jerkiness, straightness index, angular dispersion and curvature and related these dexterity (Dx) scores. We also analyzed missteps such as corrections and retractions and combined these with dexterity features to create decision making (DM) scores.

Results: Analysis of the needle trajectories indicated a decrease of jerkiness for the novices as a result of the additional navigational technologies (US vs Us + Nav + NG; p = 0.033). In this particular group US + Nav influenced both the dexterity and decision making more positively than US + Nav + NG (Dx80(US) = 20.0 (US) vs Dx80(US+Nav) = 5.19 vs Dx80(US+Nav+NG) = 9.73 and Dm80(US) = 3.03 vs Dm80(US+Nav) = 0.446 vs Dm80(US+Nav+NG) = 1.24). For experts, the navigational technologies negatively influenced the dexterity of experts similar but reversed with scores of Dx80(US) = 2.92 vs Dx80(US+Nav) = 12.1 vs Dx80(US+Nav+NG) = 9.33 and negatively influenced their decision making by increasing total pathlength, corrections and retractions (US vs US + Nav; p = 0.035, p = 0.001 and p = 0.006, respectively), thus worsening decision making scores (Dm80(US) = 0.0259 vs Dm80(US+Nav) =1.06 vs Dm80(US+Nav+NG) = 1.26).

Conclusion: By recording and analyzing the movement paths of the ultrasound probe and biopsy needle trajectories it has become possible to make an objective value-assessment on the performance enhancement created by computers-assisted navigation strategies. These preliminary findings suggest that technological refinements have a different impact on novices and experts and that increasing the technological complexity can reduce its impact.