Background: Pulsed electric field (PEF) ablation has recently been applied by researchers in the treatment of atrial fibrillation (AF), which is able to utilize high-voltage electric fields to produce damage to the myocardium for the purpose of treating AF. The effect of epicardial fat layer on the ablation effect has not been systematically studied. The purpose of our study was to establish a computer simulation model to rationally simplify the human organ, which was used to evaluate the effect of the fat layer on the ablation damage area. Methods: Firstly, by building a computational simulation model, different tissues of the heart were simplified and a three-dimensional computational model containing only the ablation device of interest was built, the ablation damage range was assessed in the post-processing interface using an electric field threshold of 400v/cm, and finally, the thickness of the fat layer was varied in order to assess the effect of the fat layer on the ablation area. Results: The epicardial fat layer had a weakening effect on PFA, and the total ablation depth decreased when the thickness of the fat layer increased, and when the fat layer reached 1.5 mm, the total ablation depth stabilized and the ablation depth of the myocardial layer decreased. In addition, we found a strong linear relationship between the total ablation depth and pulse voltage.
For the implementation of sonothrombolysis, the acoustic pressure inside the blood vessel should be revealed according to the ultrasound burst and biological tissue conditions. The objective of this study is to measure the magnitude of the acoustic pressure inside blood vessel exposed to high-intensity focused ultrasound (HIFU), and to reveal its changing characteristics according to the ultrasound parameters (power and frequency) and tissue configurations. The tissue mimicking phantom with HIFU exposure was modeled to simulate the acoustic pressure. The results showed that for a biological tissue system composed of skin, fat, muscle, and blood, the peak pressure at the focus with blood zone increased as the insonation frequency increased (0.5-2 MHz). Pressure attenuation with respect to blood vessel depth(10-30 mm) intensified according to increment of HIFU power and frequency. Greater attenuation was observed when the frequency surpassed 1.1 MHz, varying with skin (1-5 mm) and fat tissue (2-7 mm) thicknesses. The results suggest that at frequencies below 1.1 MHz, identical HIFU power can be utilized for different individuals and lesions, there by achieving similar outcomes in clinical treatment.
Deep learning has the advantages of high efficiency, high speed, high accuracy, and strong objectivity, and is widely used in the fields of pathology and laboratory diagnosis. The diagnostic techniques of traditional Chinese medicine are world-famous, and the four basic methods for diagnosing diseases, namely inspection, auscultation- olfaction, inquiry, and palpation, are collectively referred to as ”four diagnostics”. Tongue diagnosis is an important part of inspection, and it is also an effective diagnosis and treatment method for doctors to understand the changes of the patient’s body through the tongue image. In order to realize automatic tongue diagnosis, one of the important tasks is to implement the automatic segmentation of tongue images. However, using feature engineering to segment tongue images requires a lot of work, and only hand-crafted features cannot represent the features of the tongue well. Therefore, this paper designs a tongue segmentation network (TSN). TSN consists of three parts: feature encoding extraction module, context-aware module and feature decoding module. This model can fully extract tongue feature vector and perform information fusion through context-aware module, so that Effectively segment the tongue from the image. Compared with various deep learning image segmentation methods, the TSN proposed in this paper achieves the best performance results with 97.20% mean intersection over union (mIoU) and 98.83% pixel accuracy (PA).
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