Multifrequency Magnetic Induction Tomography for Hemorrhagic Stroke Detection Using an Adaptive Threshold Split Bregman Algorithm

Magnetic induction tomography (MIT) is viewed as a promising method for brain imaging. Most MIT studies are based on time-difference imaging, which cannot be used for detecting hemorrhagic stroke. However, for many years, multifrequency MIT (mfMIT) has been proposed for the early detection of hemorrhagic stroke, but the focus has primarily been on theoretical analysis and simulations. mfMIT has not been applied in clinical research due to issues such as inadequate imaging algorithms and mfMIT systems. A weighted frequency difference adaptive thresholding split Bregman (WFD–ATSB) algorithm is proposed here and is validated for the first time using a 16-channel mfMIT system developed by our group. The imaging results of a single-hemorrhagic model were evaluated using the total image reconstruction error [as the sum of the position error, area error, and image noise (IN)], and the imaging results of a double-hemorrhagic model were evaluated by calculating the correlation coefficient (CC). Simulation results showed that WFD–ATSB can reduce the overall image reconstruction error by 52% and improve the CC by 35% compared with that of the traditional algorithm. In phantom experiments, the total image reconstruction error was reduced by 27% and the CC was improved by 15%. The results confirmed that WFD–ATSB is a more practical and stable algorithm for mfMIT compared with traditional methods, which is promising for the rapid detection of cerebral hemorrhage.