13 March 2018 Needle deflection in thermal ablation procedures of liver tumors: a CT image analysis
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Abstract
Introduction: Accurate needle placement is crucial in image-guided needle interventions. A targeting error may be introduced due to undesired needle deflection upon insertion through tissue, caused by e.g. patient breathing, tissue heterogeneity, or asymmetric needle tip geometries. This paper aims to quantify needle deflection in thermal ablation procedures of liver tumors by means of a CT image analysis. Methods: Needle selection was done by using all clinical CT data that were made during thermal ablation procedures of the liver, ranging from 2008-2016, in the Erasmus MC, the Netherlands. The 3D needle shape was reconstructed for all selected insertions using manual segmentation. Subsequently, a straight line was computed between the entry point of the needle into the body and the needle tip. The maximal perpendicular distance between this straight line and the actual needle was used to calculate needle deflection. Results: In total, 365 needles were included in the analysis ranging from 14G to 17G in diameter. Average needle insertion depth was 95mm (range: 32 mm – 182 mm). Needle deflection was on average 1.3 mm (range: 0.0 mm – 6.5 mm). 54% of the needles (n=196) had a needle deflection of more than one millimeter, whereas 7% of the needles (n=25) showed a large needle deflection of more than three millimeters. Conclusions: Needle deflection in interventional radiology occurs in more than half of the needle insertions. Therefore, deflection should be taken into account when performing procedures and when defining design requirements for novel needles. Further, needle insertion models need to be developed that account for needle deflection.
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Tonke L. de Jong, Camiel Klink, Adriaan Moelker, Jenny Dankelman, John J. van den Dobbelsteen, "Needle deflection in thermal ablation procedures of liver tumors: a CT image analysis", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105761L (13 March 2018); doi: 10.1117/12.2292884; https://doi.org/10.1117/12.2292884
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