Patient Motion

Patient motion during medical imaging and therapeutic procedures presents a significant challenge, impacting image quality and treatment accuracy. Current research focuses on developing advanced motion modeling techniques, such as implicit neural representations and score-based likelihoods, to compensate for these artifacts and improve the reliability of medical data. These methods leverage machine learning, including deep learning and optical flow analysis, to estimate and correct for patient movement, enabling more precise diagnoses and personalized treatments. The resulting improvements in image quality and motion quantification have broad implications for various clinical applications, from radiotherapy planning to the objective assessment of motor function in neuromuscular disorders.