Data Consistency Based Motion Artifact Reduction for Head CT

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DESCRIPTION (provided by applicant): Most of routine CT methods assume that a subject being imaged is stationary during the scan. In important clinical applications and biomedical researches, such as pediatric head CT, the patient-motion is often unavoidable, leading to significant motion artifacts. The long-term goal of this project is to develop effective CT methods for motion estimation based image reconstruction, improve the diagnostic performance, and enable new clinical applications. Because motion artifact reduction has been a major problem in CT, our proposed work may have a significant impact on the healthcare by producing better image quality and extracting physiological and pathological features more accurately. In this R03 project, we will focus on the circular scanning trajectory and in-plane head motion, which is not only a satisfactory model in the cases we are interested but also a solid basis for our future study on more complicated motion patterns and scan modes. The specific aims are to (1) develop motion models and estimate the corresponding parameters from fan-beam/multi-slice data collected along a circular locus to describe the in-plane motion precisely; (2) adapt generalized cone-beam reconstruction methods to reconstruct a moving object in the fan-beam/multi-slice geometry; and (3) evaluate the proposed methods in numerical simulations, phantom experiments and retrospective patient studies. On completion, the proposed techniques will have been optimized, and their superior performance demonstrated in numerical simulation, phantom experiments and clinical applications. Using our proposed methods, the motion-induced blurring will have been reduced down to at least 30% of that associated with the current commercial methods of choice in the representative cases. This project will generate pilot data for a follow-up NIH R01 proposal targeting dynamic CT with noncircular scanning and general subject motion.Project Narrative: Motion artifact reduction has been a major problem in CT. Our project will develop a novel and effective solution to this long-standing problem, and may produce significantly better image quality to extract physiological and pathological features more accurately and more robustly. Hence, the proposed techniques promise to be translated for clinical use and should generate great healthcare benefits, especially for children.