MRI-based motion estimation via scatter to volume registration

摘要: Respiratory motion is a major source of error in many image acquisition applications and image-guided interventions, estimation techniques have been widely applied to compensate for it. Existing respiratory methods typically reply on breathing models learned from certain training data. However, none these can effectively handle both intra-subject inter-subject variations motion. In this paper, we propose method that directly recovers fields sparsely spaced dynamic 2-D MRIs without model. We introduce scatter-to-volume registration algorithm register the with static 3-D MRI recover dense fields. The proposed was validated 4-D acquired 5 volunteers pattern variabilities, demonstrating significant improvements over state art modeling method.