A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes.

Background: In intracranial pathologic conditions of intracranial pressure (ICP) disturbance or hemodynamic instability, maintaining appropriate ICP may reduce the risk of ischemic brain injury. The change of ICP is often accompanied by the change of intracranial blood status. As a non-invasive functional imaging technique, the sensitivity of electrical impedance tomography (EIT) to cerebral hemodynamic changes has been preliminarily confirmed. However, no team has conducted a feasibility study on the dynamic detection of ICP by EIT technology from the perspective of non-invasive whole-brain blood perfusion monitoring. In this study, human brain EIT image sequence was obtained by in vivo measurement, from which a variety of indicators that can reflect the tidal changes of the whole brain impedance were extracted, in order to establish a new method for non-invasive monitoring of ICP changes from the level of cerebral blood perfusion monitoring. Methods: Valsalva maneuver (VM) was used to temporarily change the cerebral blood perfusion status of volunteers. The electrical impedance information of the brain during this process was continuously monitored by EIT device and real-time imaging was performed, and the hemodynamic indexes of bilateral middle cerebral arteries were monitored by transcranial Doppler (TCD). The changes in monitoring information obtained by the two techniques were compared and observed. Results: The EIT imaging results indicated that the image sequence showed obvious tidal changes with the heart beating. Perfusion indicators of vascular pulsation obtained from EIT images decreased significantly during the stabilization phase of the intervention (PAC: 242.94 ± 100.83, p < 0.01); perfusion index which reflects vascular resistance increased significantly in the stable stage of intervention (PDT: 79.72 ± 18.23, p < 0.001). After the intervention, the parameters gradually returned to the baseline level before compression. The changes of EIT indexes in the whole process are consistent with the changes of middle cerebral artery velocity related indexes shown in TCD results. Conclusion: The EIT image combined with the blood perfusion index proposed in this paper can reflect the decrease of cerebral blood flow under the condition of increased ICP in real time and intuitively. With the advantages of high time resolution and high sensitivity, EIT provides a new idea for non-invasive bedside measurement of ICP.

Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax.

Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results.

Inverse problem of magneto-acoustic concentration tomography for magnetic nanoparticles with magnetic induction in a saturation magnetization state based on the least squares QR factorization method-trapezoidal method.

In order to improve the imaging quality of magneto-acoustic concentration tomography for magnetic nanoparticles (MNPs) with magnetic induction (MACT-MI) and overcome the boundary singularity, this paper built a matrix model which shows the relationship between the partial derivative distribution of MNP concentration and the ultrasound signals, and focused on proposing a concentration reconstruction method based on the least squares QR factorization (LSQR) method-trapezoidal method. Firstly, simulation models with different shapes were established. Secondly, the magnetic and acoustic field simulation data was substituted into the inverse problem method based on LSQR-trapezoidal method for concentration reconstruction. Finally, the reconstructed images were analyzed and the effect of MNP cluster radius on the reconstruction was investigated. Considering the diffusely asymptotic concentration distribution of MNPs in actual biological tissue environment, an asymptotic concentration model was established and the reconstructed images were analyzed. The simulation results show that under the same conditions, compared with the reconstruction method based on the method of moments (MoM), LSQR-trapezoidal method has clearer image boundaries, more stable imaging results, and faster imaging speed. Compared with the uniform concentration model, LSQR-trapezoidal method is more applicable to the asymptotic concentration model. This study provides a basis for further reconstruction of the accuracy of experimental research.

Simulation research on magneto-acoustic concentration tomography of magnetic nanoparticles based on truncated singular value decomposition (TSVD).

The existing magneto-acoustic concentration tomography with magnetic induction (MACT-MI) inverse problem algorithm has some problems such as the singularity of reconstructed boundary and poor anti-noise performance, which make it difficult to be applied to recognition of early breast cancer tumor. Therefore, a system matrix linking the concentration distribution information of magnetic nanoparticles (MNPs) to the ultrasonic signal was built in this paper, and a truncated singular value decomposition (TSVD) based MNPS concentration reconstruction algorithm was proposed. Firstly, a simulation model was established. Secondly, the magnetic field and acoustic field simulation data were substituted into the inverse problem algorithm based on TSVD for concentration reconstruction. Finally, the effects of the number of singular values, SNR and radius of MNPs on the reconstruction results were studied. The simulation results show that, the inverse problem algorithm based on TSVD proposed in this paper can maximize the use of ultrasonic signals, and has a good reconstruction effect on 1 mm small-radius MNPs, high resolution reconstructed images can also be obtained under the condition of low SNR, which can effectively promote the clinical application of this imaging method.

Implementation method for magneto-acoustic concentration tomography with magnetic induction (MACT-MI) based on the method of moments.

Magneto-acoustic concentration tomography of magnetic nanoparticles with magnetic induction (MACT-MI) is a new imaging technology that combines the advantages of the high sensitivity of magnetic particle imaging and the high resolution of ultrasonic imaging. This technique has broad application prospects in the biomedical and molecular imaging fields. In this study, a reconstruction algorithm based on the method of moments (MoM) is proposed for the MACT-MI inverse problem. Image reconstructions of the acoustic source and superparamagnetic nanoparticle (SPN) concentration were performed using different shape models, and the reconstructed images were analyzed. In addition, the effect of the radius of the tissue region loaded with SPNs on the quality of the reconstructed images was evaluated. The results demonstrated that the new method could reconstruct the SPN concentration distribution well, and a negative correlation existed between the radius of the imaging model and reconstructed image quality. The finding of this research can potentially contribute to the development of MACT-MI in medicine.

[Simulation research on magnetoacoustic B-scan imaging of magnetic nanoparticles].

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.

Perovskite Quantum-Dot-in-Host for Detection of Ionizing Radiation.

The concept of quantum-dot-in-perovskite solids pioneered by Ning and co-workers introduces a useful class of solution-processed type I heterostructures for optoelectronics applications. Concurrent searches for solution-processable detectors of ionizing radiation have focused on lead-halide perovskites. As described in this issue of ACS Nano, Cao et al. examined CsPbBr3 nanocrystals imbedded in Cs4PbBr6 as a wider gap host and determined its performance and possibilities as a scintillator for X-ray imaging. In this Perspective, we describe issues and research opportunities on ionizing radiation imaging and spectroscopy based on the CsPbBr3@Cs4PbBr6 composite and other perovskite-dot-in-host combinations in which the dot may be of lower dimensionality than 3, and we explore ionizing radiation detectors using halide perovskites.

Simulation research on magneto-acoustic concentration tomography of magnetic nanoparticles with magnetic induction.

BACKGROUND: Magnetic nanoparticles (MNPs) have been proposed as drug carriers for targeted therapy. Noninvasive imaging methods that can compute the distribution of MNPs have also attracted much attention. METHOD: Based on the Langevin theory, the theoretical relationship between the magnetic force and the concentration of MNPs was derived. The acoustic pressure wave equation containing the concentration of MNPs was established. RESULT: The acoustic pressure waveform reflected the dimension and position of the MNPs region. From reconstructed images, MNPs regions with different concentrations and different sizes were clearly distinguished. CONCLUSION: The concentration of MNPs can be parsed from the acoustic signals generated by particles vibrations. This conclusion indicates that magneto-acoustic concentration tomography of magnetic nanoparticles with magnetic induction (MACT-MI) has potential to detect and reconstruct the concentration of MNPs in biological tissue.

The influence on acoustic frequency characteristics of conductivity gradual-varying tissue in magnetoacoustic tomography (MAT).

BACKGROUND: As a functional imaging technology, magneto acoustic tomography (MAT) has broad application prospect in early tumor diagnosis and image monitoring during treatment. METHOD: The influence on the acoustic field characteristics of the gradual change in conductivity was studied in magneto-acoustic tomography with current injection (MAT-CI) in this article. RESULT: Theoretical analysis showed that the value of electro-acoustic conversion ratio (E-ACR) was different in different source frequencies under the same conductivity gradual-varying boundary. CONCLUSION: The frequency characteristics of the acoustic pressure tend to shift towards the low frequency region. This conclusion provides a theoretical foundation for the MA signal detection and processing system optimization in the area of conductivity gradual-varying.