LESSON (Less-On)

Medical physics beginning with the discovery of X-rays in the 1890s is a field of applied physics that utilizes physics principles, knowledge, and methods to the prevention, diagnosis, and treatment of human diseases to enhance human health and well-being in practice and research. Medical physics has been closely associated with X-ray photon and electron radiation therapy on linear accelerators. To help improve therapeutic accuracy, it deals with a variety of medical imaging modalities, such as computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), and ultrasound. It is believed that medical physics is entering a renaissance era with the rise of particle radiation therapy and artificial intelligence (AI) - machine learning and deep learning. The rapid transition requires medical physicists and researchers to have a concrete understanding of complicated techniques and knowledge behind such advances. Contouring normal organs and target volume is important, but labor-intensive. Deep learning-based autosegmentation recently helped boost efficiency in the preparation for radiation therapy. We have been interested in the normal organ autosegmentation, which are expanded to the target volume segmentation throughout the state-of-art network algorihtms such as vision transformer. It is important to note that the learning-based approach including machine learning and deep learning is based on the optimizing algorihtms, especially the iterative methods. Image reconstruction for medical imaging modalities and treatment planning for radiation therapy also work out through solving the inverse optimization problems. Our group is, in particular, interested in attaning sparse signal recovery with the Lp-norm (p≤1), and applying those to the treat