Motion Correction

Motion correction in medical imaging aims to remove artifacts caused by patient movement during image acquisition, improving diagnostic accuracy and treatment planning. Current research heavily utilizes deep learning, employing architectures like UNets and convolutional neural networks, often incorporating novel loss functions or implicit neural representations to enhance accuracy and robustness across diverse imaging modalities (MRI, CT, PET, ultrasound). These advancements are crucial for improving the reliability and clinical utility of various medical imaging techniques, particularly in applications where motion is a significant challenge, such as fetal imaging and cardiac MRI.