Motion Artifact

Motion artifacts, unwanted distortions in images or signals caused by movement, significantly degrade data quality across various imaging modalities, hindering accurate diagnosis and analysis. Current research focuses on developing sophisticated algorithms and deep learning models, including convolutional neural networks (CNNs), autoencoders, diffusion models, and recurrent neural networks, to detect and correct these artifacts, often employing techniques like masked autoencoding, implicit neural representations, and wavelet packet decomposition. These advancements are crucial for improving the reliability of medical imaging (MRI, CT, echocardiography), video analysis, and radar-based vital sign monitoring, ultimately leading to more accurate diagnoses and improved patient care.