As a tumor thermal ablation technology in cancer therapy, HIFU (High Intensity Focused Ultrasound) has been developed rapidly in recent years. With the technology becoming more and more mature, it's clinical application is becoming more and more widely. In HIFU therapy, the high-intensity ultrasound energy is focused in the target tumor tissue, generating heat within very short time, causing coagulation necrosis, so that the effect of the treatment is achieved. To ensure safe and therapeutic efficacy, HIFU therapy needs to be properly monitored by medical imaging, and temperature in the target has to be precisely measured, this article is based on the current domestic and foreign detection methods of the focal region temperature.
Diagnostic Imaging ; High-Intensity Focused Ultrasound Ablation ; Humans ; Neoplasms ; therapy ; Temperature ; Ultrasonic Therapy

Objective To study the efficiency of the compound active carbon filter in reducing the content of free radicals in the smoke of cigarettes. Methods 9 trademarks of cigarettes installed the compound active carbon filters were collected and ESR was used to analyze the content of free radicals in the particle and gas phases. Results The results showed that the efficiency of the compound active carbon filters to reduce the content of free radicals in the cigarette smoke was not identical,some samples even showed an inverse result. Conclusion Based on the results of the present paper,it is not considered that the compound active carbon filter can always reduce the free radicals in the smoke of cigarettes.

Recently, deep neural networks (DNNs) have been widely used in the field of electrocardiogram (ECG) signal classification, but the previous models have limited ability to extract features from raw ECG data. In this paper, a deep residual network model based on pyramidal convolutional layers (PC-DRN) was proposed to implement ECG signal classification. The pyramidal convolutional (PC) layer could simultaneously extract multi-scale features from the original ECG data. And then, a deep residual network was designed to train the classification model for arrhythmia detection. The public dataset provided by the physionet computing in cardiology challenge 2017(CinC2017) was used to validate the classification experiment of 4 types of ECG data. In this paper, the harmonic mean of classification accuracy and recall was selected as the evaluation indexes. The experimental results showed that the average sequence level ( ) of PC-DRN was improved from 0.857 to 0.920, and the average set level ( ) was improved from 0.876 to 0.925. Therefore, the PC-DRN model proposed in this paper provided a promising way for the feature extraction and classification of ECG signals, and provided an effective tool for arrhythmia classification.
Arrhythmias, Cardiac ; Disease Progression ; Electrocardiography ; Humans ; Neural Networks, Computer