One of the main technical challenges when integrating magnetic resonance imaging (MRI) systems with medical linear accelerator is the strong interference of fringe magnetic fields from the MRI system with the electron beams of linear accelerator, making the linear accelerator not to work properly. In order to minimize the interference of magnetic fields, a magnetic shielding cylinder with an open structure made of high permeability materials is designed. ANSYS Maxwell was used to simulate Helmholtz coil which generate uniform magnetic field instead of the fringe magnetic fields which affect accelerator gun. The parameters of shielding tube, such as permeability, radius, length, side thickness, bottom thickness and fringe magnetic fields strength are simulated, and the data is processed by MATLAB to compare the shielding performance. This article gives out a list of magnetic shielding effectiveness with different side thickness and bottom thickness under the optimal radius and length, which showes that this design can meet the shielding requirement for the MRI-linear accelerator system.
For refractory epilepsy requiring surgical treatment in clinic, precise preoperative positioning of the epileptogenic zone is the key to improving the success rate of clinical surgical treatment. Although the use of electrical stimulation to locate epileptogenic zone has been widely carried out in many medical centers, the preoperative implantation evaluation of stereoelectroencephalography (SEEG) and the interpretation of electrical stimulation induced EEG activity are still not perfect and rigorous. Especially, there are still technological limitations and unknown areas regarding electrode implantation mode, stimulation parameters design, and surgical prognosis correlation. In this paper, the clinical background, application status, technical progress and development trend of SEEG-based stereo-electric stimulation-induced cerebral electrical activity in the evaluation of refractory epilepsy are reviewed, and applications of this technology in clinical epileptogenic zone localization and cerebral cortical function evaluation are emphatically discussed. Additionally, the safety during both of high-frequency and low-frequency electrical stimulations which are commonly used in clinical evaluation of refractory epilepsy are also discussed.
It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.