title: Reducing respiratory motion artifacts in positron emission tomography through retrospective stacking.
abstract: Respiratory motion artifacts in positron emission tomography (PET) imaging can alter lesion intensity profiles, and result in substantially reduced activity and contrast-to-noise ratios (CNRs). We propose a corrective algorithm, coined "retrospective stacking" (RS), to restore image quality without requiring additional scan time. Retrospective stacking uses b-spline deformable image registration to combine amplitude-binned PET data along the entire respiratory cycle into a single respiratory end point. We applied the method to a phantom model consisting of a small, hot vial oscillating within a warm background, as well as to 18FDG-PET images of a pancreatic and a liver patient. Comparisons were made using cross-section visualizations, activity profiles, and CNRs within the region of interest. Retrospective stacking was found to properly restore the lesion location and intensity profile in all cases. In addition, RS provided CNR improvements up to three-fold over gated images, and up to five-fold over ungated data. These phantom and patient studies demonstrate that RS can correct for lesion motion and deformation, while substantially improving tumor visibility and background noise.
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305, USA. thorndyb@stanford.edu
authors.forename: Brian
authors.initials: B
authors.lastname: Thorndyke
authors.forename: Eduard
authors.initials: E
authors.lastname: Schreibmann
authors.forename: Albert
authors.initials: A
authors.lastname: Koong
authors.forename: Lei
authors.initials: L
authors.lastname: Xing
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003627:Data Interpretation, Statistical; D006801:Humans; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D009038:Motion; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D012119:Respiration; D012984:Software