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A Concurrent-wavelength Reconstruction Algorithm (CRA) based on multi-wavelength information fusion is proposed in this paper that aims to further improve the accuracy of Fluorescence Molecular Tomography (FMT) reconstruction. Combining multi-spectral data with FMT technology, the information of 650 and 750 nm wavelengths near-infrared was used to increase the feature information of the dominant 850 nm wavelength near-infrared effectively. Principal component analysis, which can remove redundant information and achieve data dimensionality reduction, was then utilized to extract the feature information. Finally, tomographic reconstruction of the anomalies was performed based on the stacked auto-encoder neural network model. The comparison results of numerical experiments showed that the reconstruction effect of CRA was superior to the performance of the single wavelength model. The correlation coefficient between CRA reconstructed anomalies’ fluorescence yield values and the real fluorescence yield values remained at 0.95 or more under the noise of different levels of signal-to-noise ratios. Therefore, the CRA proposed in this paper could effectively improve on the ill-posedness of the inverse problem, which could further enhance the accuracy of FMT reconstruction.

Magnetic induction tomography (MIT) is a promising approach in rapid diagnosis and continuous monitoring of cerebral hemorrhage. A new algorithm for the reconstruction of intracerebral hemorrhage with MIT, including the location and volume of hemorrhage, is proposed in this study. First, 2D magnetic resonance imaging and computed tomography images of patients with cerebral hemorrhage were used for the development of simulation models. The Stacked Autoencoder (SAE) network was then used to predict the location and volume of hemorrhage by conductivity reconstruction. Finally, the one-dimensional quantitative monitoring index is proposed as an auxiliary diagnostic indicator for assessment of real-time intracranial electrical characteristics. The 2D simulation results showed that the SAE was able to quickly image the location and volume of the hemorrhages. Compared with the back-projection algorithm, the prediction speed of each frame was improved 15-fold, and the accuracy improved by 90.53%. The extracted one-dimensional quantitative monitoring indicators can describe the bleeding status. The diagnostic accuracy and the imaging speed of cerebral hemorrhage were both improved by constructing a realistic head section model and using the proposed SAE network. This research provides a new alternative for dynamic monitoring of hemorrhages and shows the potential advantages of MIT in noninvasive detection.

Magnetic induction tomography (MIT) is a non-invasive and non-contact imaging technology, which can be used in medical diagnosis by reconstructing the electrical distribution of biological tissues. Unlike other large medical imaging equipment, the device of MIT is with small size and low cost. The theoretical basis of MIT is by measuring the phase difference of magnetic flux density generated around the imaging objects, analyzing the eddy current distribution, and then using the reconstruction algorithms to obtain the electrical characteristic distribution of the object. This review introduces the development of imaging systems and the reconstruction algorithms of MIT as a medical assisted diagnostic technology, including the optimal design of the sensors, the excitation methods of the system, the calculation methods of the eddy current, and the improved methods of different reconstruction algorithms.

The commonly used respiratory monitoring methods in clinical medicine are chest belt or ventilators, which are not easy to carry and cumbersome to operate. In order to solve this problem, a low-cost and portable magnetic induction phase detection system for respiratory monitoring is proposed. In this study, a magnetic induction tomography system for respiration detection is established, and the phase sensitivity of the system to conductive object is evaluated through a series of experiments. At the same time, phantom experiments are carried out to simulate the respiratory process, and the phase monitoring indicators are collected to describe different respiratory states. The experimental results show that the phase detection results are consistent with the changes in the respiratory cycle. The signal-to-noise ratio of the system is 79 dB. It proves the feasibility of using magnetic induction phase measurement in respiratory monitoring and provides a new detection method of respiratory monitoring.