Respiratory Motion

Respiratory motion, the movement of internal organs during breathing, significantly impacts medical imaging and treatment. Current research focuses on accurately modeling and compensating for this motion using various techniques, including deep learning architectures like recurrent neural networks (RNNs), generative adversarial networks (GANs), and graph neural networks (GNNs), often applied to 4D computed tomography (4DCT) and ultrasound data. These advancements aim to improve the accuracy of medical image analysis, particularly in radiotherapy planning and diagnosis, by mitigating motion artifacts and enabling more precise treatment delivery. The ultimate goal is to enhance the quality and reliability of medical imaging and improve patient outcomes.