Data-Driven Motion Detection and Event-by-Event Correction for Brain PET: Comparison with Vicra.

ABSTRACT: Head motion degrades image quality and causes erroneous parameter estimates in tracer kinetic modeling in brain PET studies. Existing motion correction methods include frame-based image registration (FIR) and correction using real-time hardware-based motion tracking (HMT) information. However, FIR cannot correct for motion within 1 predefined scan period, and HMT is not readily available in the clinic since it typically requires attaching a tracking device to the patient. In this study, we propose a motion correction framework with a data-driven algorithm, that is, using the PET raw data itself, to address these limitations. Methods: We propose a data-driven algorithm, centroid of distribution (COD), to detect head motion. In COD, the central coordinates of the line of response of a

SUBMITTER: Lu Y 

PROVIDER: S-EPMC7456171 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature