3D Magnetopneumography Magnetic Dipole Model and Its Application Using Fluxgate Gradiometers

Abstract

Magnetopneumography (MPG) as a non‐invasive method for pneumoconiosis diagnosis has been developed to evaluate the load and spatial distribution of particles within the human lungs through scanning of remanent magnetic fields after magnetization of the subject in a strong direct current field. The measurement of particle spatial distribution is very important for pneumoconiosis diagnosis because localized deposits may be associated with some pathological changes such as pulmonary fibrosis. Previous research found that loads of magnetite particles were proportional to their magnetic dipole moments. The three‐dimensional (3D) MPG magnetic dipole model (MDM) proposed in this paper and based on Biot–Savart Law and matrix manipulation provides a means of precise measurement of the particle distribution and load amount. A styrofoam + magnetite powder phantom with magnetization was laid on a nonmagnetic board. Two first‐order fluxgate gradiometers with 10–12 T sensitivity were coaxially applied over and under the phantom and used for scanning remanent magnetic fields. This paper provides validation results using 3D MPG MDM through two experiments. The overall error of the simulation results is 0.2–2.7% in the center and 7.28–9.42% in the corners of the subject. Finally, this paper gives clinical experiments with a welder suffering stage‐II pneumoconiosis and states that the 3D MPG MDM shows similar results to X‐ray chest films in pneumoconiosis diagnosis. The results suggest that the 3D MPG MDM is potentially a reasonable and accurate algorithmic model to inversely track the load amount and distribution of magnetite particles within the lungs. Bioelectromagnetics. 2019;40:472–487. © 2019 Wiley Periodicals, Inc

Position and Direction Tracking of a Magnetic Object Based on an Mx-Atomic Magnetometer

“…One of them is called tissue heating (or specific absorption rate (SAR)) and the other one is peripheral nerve stimulation (PNS) 6 . In order to reduce these health effects, a low-frequency magnetic sensor should be used such as giant magneto resistive sensors (GMRs) 7,8 , integrated giant magnetic impedance sensors (GMIs) 9 , tunneling magneto resistive sensors (TMRs) 10 , fluxgates 11 , superconducting quantum interference devices (SQUIDs) 12,13 , and atomic magnetometers 14 . The main drawback of the GMR, GMI, TMR, magnetoelectric sensor, and fluxgates is that due to their low sensitivity, they need to be close to the magnetic objects.…”