Why the study?
Tracking the biopsy needle trajectory and providing real-time positional guidance to physicians is needed to improve puncture accuracy, reduce reliance on CT imaging, and minimize procedural complications during lung biopsy.
A novel electromagnetic tracking model using a rectangular toroidal current distribution achieved sub-2 mm localization error in a simulated lung biopsy setup, potentially improving procedural accuracy.
May enhance real-time lung biopsy guidance; leaves open clinical validation and adoption.
Biopsy remains the gold standard for diagnosing lung cancer, with high-quality tissue samples being critical for accurate results. To improve puncture accuracy, reduce reliance on CT imaging, and minimize procedural complications, it is essential to address the challenges of tracking the biopsy needle’s trajectory and providing real-time positional guidance to physicians. In this study, we propose a tracking model based on a rectangular toroidal current distribution to determine the biopsy needle’s relative position within the electromagnetic tracking system. A printed circuit board (PCB) is used as the platform for generating the rectangular circulating magnetic field. An alternating electromagnetic field (~70 kHz) is modeled based on the Biot–Savart law. Induced voltages from multiple transmitting coils are processed using Fourier transform algorithms to separate frequencies, enabling the independent extraction of each coil’s signal. A least squares method is employed to solve the five-degree-of-freedom electromagnetic positioning equations for the receiving coils. The objective is to establish a precise and computationally efficient electromagnetic localization model for the biopsy needle. An experimental setup simulating lung biopsy procedures is implemented, utilizing the proposed rectangular toroidal current configuration. Results demonstrate an average localization error of less than 1.76 mm, validating the effectiveness of the system in addressing the challenges of real-time biopsy needle tracking.
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Pei et al. (2025) studied this question.
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