Multi-frequency electrical impedance tomography (mfEIT) offers a nondestructive imaging technology that reconstructs the distribution of electrical characteristics within a subject based on the impedance spectral differences among biological tissues. However, the technology faces challenges in imaging multi-class lesion targets when the conductivity of background tissues is frequency-dependent. To address these issues, we propose a spatial-frequency cross-fusion network (SFCF-Net) imaging algorithm, built on a multi-path fusion structure. This algorithm uses multi-path structures and hyper-dense connections to capture both spatial and frequency correlations between multi-frequency conductivity images, which achieves differential imaging for lesion targets of multiple categories through cross-fusion of information. According to both simulation and physical experiment results, the proposed SFCF-Net algorithm shows an excellent performance in terms of lesion imaging and category discrimination compared to the weighted frequency-difference, U-Net, and MMV-Net algorithms. The proposed algorithm enhances the ability of mfEIT to simultaneously obtain both structural and spectral information from the tissue being examined and improves the accuracy and reliability of mfEIT, opening new avenues for its application in clinical diagnostics and treatment monitoring.

Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed. Results of numerical simulations and head phantom experiments indicate that FISTA-C reduces IN by 63.2%, 47.2%, and 29.9% and 54.4%, 44.7%, and 22.7%, respectively, when compared with the damped least-squares algorithm, the split Bergman, and the FISTA algorithms. When the signal-to-noise ratio of the measurements is 80–50 dB, FISTA-C can reduce IN by 83.3%, 72.3%, and 68.7% on average when compared with the three algorithms, respectively. Both simulation and phantom experiments suggest that FISTA-C produces the best image resolution and can identify the two closest targets. Moreover, FISTA-C is more practical for clinical application because it does not require excessive parameter adjustments. This technology can provide better reconstruction performance and significantly outperforms the traditional algorithms in terms of IN and resolution and is expected to offer a general algorithm for brain injury monitoring imaging via EIT.

Objective. This study aims to propose and validate a novel electrically controlled field-free line (FFL) magnetic particle imaging (MPI) system to address several aspects of existing electronically rotated FFL-MPI designs, including limitations in gradient enhancement, structural complexity of the drive system, and coil coupling effects. Methods . We employ a dynamic ring-shaped magnetic-field-converging gradient array, consisting of multiple pairs of electromagnets symmetrically arranged along the radial and axial directions. This configuration ensures that the magnetic field direction of the generated FFL remains consistently perpendicular to both the gradient direction and the imaging plane. Tomographic imaging uses only one set of drive coils arranged orthogonally to the gradient coils. Three-dimensional FFL scanning is realized by dynamically modulating the current phase of the radial coil pairs in conjunction with differential current control of the axial coil pairs. In addition, we conduct electromagnetic and image reconstruction simulations to evaluate the FFL generation characteristics and reconstruction performance under magnetic configurations with different numbers of pole pairs. Results . Simulation results demonstrate that the proposed system can achieve efficient and stable FFL rotation and axial translation, supporting rapid 3D tomographic scanning. In addition, we systematically analyzed how different pole-pair configurations affect the quality of the generated FFL and the resulting image resolution. These findings offer concrete guidance on how pole-pair scaling influences gradient strength, field uniformity, and system complexity. Significance . The proposed MagRing-MPI system enhances magnetic field gradients and imaging quality, simplifies the drive-coil configuration to reduce system complexity, and minimizes electromagnetic coupling and feedthrough effects. These improvements provide a promising foundation for the development of scalable and high-performance MPI systems.

Electrical impedance tomography (EIT) can be used for real-time bedside monitoring of pathological changes in human brain tissue and dynamic imaging. However, EIT measurement signals are easily disturbed by noise, tremor in stroke patients, and other factors in clinical practice, which result in significant artifacts in the reconstructed images. These artifacts degrade the image quality and affect the detection of focal targets. To address this problem, we propose a multiscale one-dimensional residual convolutional network (MS-1DResCNN) to remove chaotic noise and interference from the conductivity distribution data and retain the target feature data, thereby improving the robustness of the reconstruction algorithm to noise and interference. The structural similarity (SSIM) and normalized mean square error (NMSE) were used to evaluate the performance of the proposed method in suppressing image reconstruction artifacts under noise-free conditions, different noise simulation levels, and varying vibration interference frequencies. The results of physical experiments showed that, under 10 Hz vibration interference, the reconstructed images using the MS-1DResCNN method improved the SSIM by 40.4%, 22.0%, 17.4%, and 11.7% and reduced the NMSE by 74.3%, 72.4%, 65.2%, and 29.5%, compared to the damped least squares, artificial neural network, Ec-Net, and U-Net methods, respectively. This method effectively improves the anti-interference ability of conditional algorithms, significantly reduces artifacts in EIT images, and makes the perturbation target clearer and more precise, thereby providing stable and reliable algorithmic support for the clinical application of EIT.