학술논문

A novel signal processing method based on the frequency modality for intra-body medical instrument tracking
Document Type
Conference
Source
2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST) Modern Circuits and Systems Technologies (MOCAST), 2016 5th International Conference on. :1-4 May, 2016
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Signal Processing and Analysis
Transmitters
Smoothing methods
Frequency measurement
Oscilloscopes
Receivers
Data processing
Position tracking
invasive medical instrument
PLL
LOWESS
Lomb-Scargle periodogram
Language
Abstract
Measuring the exact position of a medical instrument inside the human body can be performed with various methods, at various levels of invasiveness and accuracy. This paper presents a novel approach which does not require added invasive procedures, yet can achieve a comparatively high (sub-millimeter) level of accuracy. It exploits the phase shift of the signal originating from a transmitter embedded into the tip of the medical instrument, concluding its displacement with respect to a set of stationary receivers. The aforementioned phase shift is converted to a low frequency voltage with the use of a Phased Locked Loop (PLL). This voltage can subsequently be converted into a displacement in space, providing an estimate of the position of the medical instrument using trilateration. The instrument's displacement can be defined against either the time or frequency domain. This paper presents displacement measurement data from a transmitter moving at a constant velocity, filtered through the Locally Weighted Scatter-Plot Smoothing (LOWESS) curve fitting method or a Lomb-Scargle periodogram. The Lomb-Scargle periodogram is based on the least-squares power spectrum and can be used instead of waveform smoothing and measurement into the time domain, providing more precise and accurate measurement results as compared to the LOWESS curve fitting method.