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Electromagnetic Tracking for Deep Liver Targets: A preliminary feasibility study

Abstract : Electromagnetic tracking is a core platform technology in the navigation and visualisation of image-guided procedures. The technology provides high tracking accuracy in non-line-of-sight environments, allowing instrument navigation in locations where optical tracking is not feasible. EMT can be beneficial in applications such as percutaneous radiofrequency ablation for the treatment of hepatic lesions where the needle tip may be obscured due to difficult liver environments (e.g subcutaneous fat or ablation artefacts). Advances in the field of EMT include novel methods of improving tracking system accuracy, precision and error compensation capabilities, though such system-level improvements cannot be readily incorporated in current therapy applications due to the ‘blackbox’ nature of commercial tracking solving algorithms. This paper defines a software framework to allow novel EMT designs, and improvements become part of the global design process for image-guided interventions. An exemplary framework is implemented in the Python programming language and demonstrated with the open-source Anser EMT system. The framework is applied in the preclinical setting though targeted liver ablation therapy on an animal model. The developed framework was tested with the Anser EMT electromagnetic tracking platform. Liver tumour targeting was performed using the tracking framework with the CustusX navigation platform using commercially available electromagnetically tracked needles. Ablation of two tumours was performed with a commercially available ablation system. Necropsy of the tumours indicated ablations within 5 mm of the tumours.
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Submitted on : Monday, May 6, 2019 - 9:47:28 PM
Last modification on : Saturday, July 23, 2022 - 3:52:57 AM


Electromagnetic Tracking for D...
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Stephen Hinds, Herman Alexander Jaeger, Richard Burke, Brodie O’sullivan, Joseph Keane, et al.. Electromagnetic Tracking for Deep Liver Targets: A preliminary feasibility study. International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2019, ⟨10.1007/s11548-019-01983-5⟩. ⟨hal-02121900⟩



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