As drug carriers, magnetic nanoparticles can specifically bind to tumors and have the potential for targeted therapy. It is of great significance to explore non-invasive imaging methods that can detect the distribution of magnetic nanoparticles. Based on the mechanism that magnetic nanoparticles can generate ultrasonic waves through the pulsed magnetic field excitation, the sound pressure wave equation containing the concentration information of magnetic nanoparticles was derived. Using the finite element method and the analytical solution, the consistent transient pulsed magnetic field was obtained. A three-dimensional simulation model was constructed for the coupling calculation of electromagnetic field and sound field. The simulation results verified that the sound pressure waveform at the detection point reflected the position of magnetic nanoparticles in biological tissue. Using the sound pressure data detected by the ultrasonic transducer, the B-scan imaging of the magnetic nanoparticles was achieved. The maximum error of the target area position was 1.56%, and the magnetic nanoparticles regions with different concentrations were distinguished by comparing the amplitude of the boundary signals in the image. Studies in this paper indicate that B-scan imaging can quickly and accurately obtain the dimensional and positional information of the target region and is expected to be used for the detection of magnetic nanoparticles in targeted therapy.
Real-time acquisition of pulmonary ventilation and perfusion information through thoracic electrical impedance tomography (EIT) holds significant clinical value. This study proposes a novel method based on the rime (RIME) algorithm-optimized variational mode decomposition (VMD) to separate lung ventilation and perfusion signals directly from raw voltage data prior to EIT image reconstruction, enabling independent imaging of both parameters. To validate this approach, EIT data were collected from 16 healthy volunteers under normal breathing and inspiratory breath-holding conditions. The RIME algorithm was employed to optimize VMD parameters by minimizing envelope entropy as the fitness function. The optimized VMD was then applied to separate raw data across all measurement channels in EIT, with spectral analysis identifying relevant components to reconstruct ventilation and perfusion signals. Results demonstrated that the structural similarity index (SSIM) between perfusion images derived from normal breathing and breath-holding states averaged approximately 84% across all 16 subjects, significantly outperforming traditional frequency-domain filtering methods in perfusion imaging accuracy. This method offers a promising technical advancement for real-time monitoring of pulmonary ventilation and perfusion, holding significant value for advancing the clinical application of EIT in the diagnosis and treatment of respiratory diseases.