Optical phase contrast imaging for absolute, quantitative measurements of ultrasonic fields with frequencies up to 20 MHz

Correction of a transcranial acoustic field using a transient ultrasound field visualization technique

Ultrasound, due to its noninvasive nature, has the potential to enhance or suppress neural activity, making it highly promising for regulating intractable brain disorders. Precise ultrasound stimulation is crucial for improving the efficiency of neural modulation and studying its mechanisms. However, the presence of the skull can cause distortion in the ultrasound field, thereby affecting the accuracy of stimulation. Existing correction methods primarily rely on magnetic resonance guidance and numerical simulation. Due to the large size and high cost, the MR-guided transcranial ultrasound is difficult to be widely applied in small animals. The numerical simulation usually requires further validation and optimization before application, and the most effective method is to visualize the excited ultrasound field. However, the ultrasound field correction methods based on acoustic field visualization are still lacking. Therefore, a shadowgraph-based transient ultrasonic field visualization system is developed, and an ex vivo transcranial ultrasound field correction is performed. By visualizing the ultrasound field with or without a rat skull and then calculating the time difference of each element's ultrasound wavefront, the parameters for ultrasound field correction can be achieved. The experimental results show that this method can improve both the shape and the size of the focal spot, as well as enhance the acoustic pressure at the focus. Overall, the results demonstrate that the ultrasonic field visualization technology can effectively improve the transcranial ultrasound focusing effect and provide a new tool for achieving precise ultrasonic neural modulation.

Computer-Assisted Quantitative Analysis of Skeletal Muscles of Snowboarding Parallel Giant Slalom Athletes after Exercise Based on Artificial Intelligence and Complex Networks

The snowboarding project has the characteristics of high risk and high technical level. The current publicity level is not high, and the number of participants is also very limited. Another potential advantage medal breakthrough project that is expected to be achieved in the Winter Olympics has received a lot of attention, creating favorable opportunities for the promotion and development of this project in China. The event requires good special physical support, skeletal muscle contraction is the body to produce motor function, and special physical training and recovery are key factors for athletes to obtain excellent results in the competition. This article is aimed at performing ultrasonic quantitative analysis on the skeletal muscles of skiers after exercise based on artificial intelligence and complex networks and at studying the skeletal muscle conditions of snowboarders after exercise, so as to provide a certain theoretical basis for coaches in future scientific training. Based on a large amount of literature, this paper uses variational optical flow calculation and split Bregman method to solve the typical HS model, L1-L2 model, and L1-high-order model, respectively, and uses the motion estimation method to describe the movement of muscles. An experiment was designed to collect ultrasound images of the gastrocnemius and quadriceps muscles during contraction. In addition, a motion target positioning algorithm was used to obtain some motion parameters, which provided direct help for athletes in rationally arranging training load and scientific training. The experimental results in this paper show that the muscle motion features extracted from the ultrasound sequence images can quantitatively express a lot of important information about the skeletal muscle motion form and function and have potential practical application value. And the different invariants of each type of ski trajectory vary greatly, floating between 1.5429 and 7.6759.