Ultrasound Image Quality

Improving ultrasound image quality is crucial for accurate medical diagnosis and treatment, as current techniques suffer from noise, artifacts, and operator dependence. Research focuses on developing advanced image processing techniques, including diffusion models, deep learning architectures (like convolutional neural networks and variational autoencoders), and Bayesian optimization, to enhance image clarity, reduce noise, and automate quality assessment. These advancements aim to improve diagnostic accuracy, reduce workload for sonographers, and enable more efficient and reliable ultrasound-guided procedures. Ultimately, this research strives to make ultrasound imaging more robust, objective, and accessible.