Sharp as a Tack
Organ motion occuring during acquisition of medical images can cause motion blur artifacts, thus posing a major problem for many commonly employed modalities. Therefore, compensating for that motion during image reconstruction has been a focus of research for several years. However, objectively comparing the quality of different motion compensated reconstructions is no easy task. Often, intensity profiles across image edges are utilized to compare their sharpness. Manually positioning such a profile line is highly subjective and prone to bias. Expanding on this notion, we propose a robust, semi-automatic scheme for comparing edge sharpness using an ensemble of profiles. We study the behavior of our approach, which was implemented as an open-source tool, for synthetic data in the presence of noise and artifacts and demonstrate its practical use in respiratory motion-compensated MRI as well as cardiac motion-compensated C-arm CT.
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