# Real-Time Simulation of Wave Phenomena in Lung Ultrasound Imaging
## 1 Introduction  
Lung ultrasonography (LUS), also known as lung ultrasound or chest ultrasound, is a non-invasive imaging technique that allows real-time assessment of pulmonary structures. Research evidence indicates the importance of this technique, which may be a significant tool for a wide range of lung diseases, including both diagnosis and treatment considerations. There is a steady increase in the prevalence of LUS, and it has been shown to be effective in conditions including pneumonia [1], pleural effusion, lung consolidation [2], pneumothorax [3] and much more common etiologies of respiratory failure. Lung ultrasonography is recommended as a \"point of care\" examination in patients with dyspnea, chest pain, and any symptoms in the chest [4]. Moreover, LUS played a significant role during the COVID-19 pandemic [5, 6]. In [7], authors present machine learning-based diagnostic tools for COVID-19 detection that are among other factors based on LUS examination. Despite the fact that ultrasonography methods offer many inestimable advantages, such as portability (bedside usage), no ionizing radiation exposure, efficiency, and low-cost [8], its main limitation--operator-dependency, should also be mentioned. The appropriate interpretation of lung ultrasound images requires adequate training, experience and expertise to ensure the accuracy and reliability of the results [9]. Lung ultrasound has been shown to be an easy-to-learn technique by personnel without previous experience in a relatively short period of time [10; 11; 12]. [PERSON] et al. found that after a brief 30-min training, clinicians achieved a 95% success rate in obtaining adequate images and demonstrated comparable B-line interpretation sensitivity and specificity to expert sonographers [11]. In order to achieve effective training, several approaches are used. There are traditional ways of education based on lectures and seminars [13], in addition, e-learning courses with online lectures [14; 15; 16] and hands-on sessions involving healthy live models [17; 16] or phantoms [17; 18]. Each method has certain advantages; for example, learning from online lectures is distinguished by high accessibility, flexibility, and affordability, while hands-on learning on phantoms simulates the process of a real examination without exposing the patient to risks [19]. A promising solution that combines a number of advantages is a web-based LUS simulator, which visualizes US images on the screen, allows users to manipulate the probe, and presents different disease scenarios. The development of an LUS simulator requires a comprehensive understanding of the fundamentals of ultrasound physics and the phenomena used in lung ultrasound diagnosis. LUS is achieved by interpreting ultrasound reverberation artifacts, generally referred to as A and B lines. A-lines are horizontal, hyperechoic (bright) lines that are evenly spaced and parallel to the pleural line. These artifacts are caused by subpleural air, resulting in large differences in the acoustic impedance between the chest wall and alveoli of the lung. The sound waves travel through the lung and reflect between the pleural line and the transducer, producing a sequence of horizontal lines that are typical findings in air-filled tissues. The B-lines (known as ultrasound lung comets) are vertical, hyperechoic lines that originate from the pleural lines and extend without fading to the bottom of the screen. They replace normal A-lines and are synchronized with the lung sliding. B-lines are the consequence of interlobular septal thickening covered by air-filled alveoli, creating a significant acoustic impedance gradient that produces the reverberation artifacts. B-lines are associated with various lung diseases, including pulmonary edema, interstitial lung disease and pneumonia [20; 21; 22; 23]. In this work, we presented a web-based LUS simulator that was created to provide a comprehensive learning tool that visualizes ultrasound images, allows probe manipulation, and presents various disease scenarios.