title: Respiratory trace feature analysis for the prediction of respiratory-gated PET quantification.
abstract: The benefits of respiratory gating in quantitative PET/CT vary tremendously between individual patients. Respiratory pattern is among many patient-specific characteristics that are thought to play an important role in gating-induced imaging improvements. However, the quantitative relationship between patient-specific characteristics of respiratory pattern and improvements in quantitative accuracy from respiratory-gated PET/CT has not been well established. If such a relationship could be estimated, then patient-specific respiratory patterns could be used to prospectively select appropriate motion compensation during image acquisition on a per-patient basis. This study was undertaken to develop a novel statistical model that predicts quantitative changes in PET/CT imaging due to respiratory gating. Free-breathing static FDG-PET images without gating and respiratory-gated FDG-PET images were collected from 22 lung and liver cancer patients on a PET/CT scanner. PET imaging quality was quantified with peak standardized uptake value (SUV(peak)) over lesions of interest. Relative differences in SUV(peak) between static and gated PET images were calculated to indicate quantitative imaging changes due to gating. A comprehensive multidimensional extraction of the morphological and statistical characteristics of respiratory patterns was conducted, resulting in 16 features that characterize representative patterns of a single respiratory trace. The six most informative features were subsequently extracted using a stepwise feature selection approach. The multiple-regression model was trained and tested based on a leave-one-subject-out cross-validation. The predicted quantitative improvements in PET imaging achieved an accuracy higher than 90% using a criterion with a dynamic error-tolerance range for SUV(peak) values. The results of this study suggest that our prediction framework could be applied to determine which patients would likely benefit from respiratory motion compensation when clinicians quantitatively assess PET/CT for therapy target definition and response assessment.
authors: Department of Industrial and Systems Engineering, 3900 Stevens Way, Seattle, WA 98195, USA. Integrated Brain Imaging Center, 1959 NE Pacific St, Seattle, WA 98195, USA.
authors: Shouyi
authors: S
authors: Wang
authors: Stephen R
authors: SR
authors: Bowen
authors: W Art
authors: WA
authors: Chaovalitwongse
authors: George A
authors: GA
authors: Sandison
authors: Thomas J
authors: TJ
authors: Grabowski
authors: Paul E
authors: PE
authors: Kinahan
mesh_terms: D019788:Fluorodeoxyglucose F18; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D015233:Models, Statistical; D049268:Positron-Emission Tomography / Q000379:methods*; D012119:Respiration; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed
chemical_list: D019788:Fluorodeoxyglucose F18