title: Modeling the respiratory motion of solitary pulmonary nodules and determining the impact of respiratory motion on their detection in SPECT imaging.
abstract: The objectives of this investigation were to model the respiratory motion of solitary pulmonary nodules (SPN) and then use this model to determine the impact of respiratory motion on the localization and detection of small SPN in SPECT imaging for four reconstruction strategies. The respiratory motion of SPN was based on that of normal anatomic structures in the lungs determined from breath-held CT images of a volunteer acquired at two different stages of respiration. End-expiration (EE) and time-averaged (Frame Av) non-uniform-B-spline cardiac torso (NCAT) digital-anthropomorphic phantoms were created using this information for respiratory motion within the lungs. SPN were represented as 1 cm diameter spheres which underwent linear motion during respiration between the EE and end-inspiration (EI) time points. The SIMIND Monte Carlo program was used to produce SPECT projection data simulating Tc-99m depreotide (NeoTect) imaging. The projections were reconstructed using 1) no correction (NC), 2) attenuation correction (AC), 3) resolution compensation (RC), and 4) attenuation correction, scatter correction, and resolution compensation (AC_SC_RC). A human-observer localization receiver operating characteristics (LROC) study was then performed to determine the difference in localization and detection accuracy with and without the presence of respiratory motion. The LROC comparison determined that respiratory motion degrades tumor detection for all four reconstruction strategies, thus correction for SPN motion would be expected to improve detection accuracy. The inclusion of RC in reconstruction improved detection accuracy for both EE and Frame Av over NC and AC. Also the magnitude of the impact of motion was least for AC_SC_RC.
authors.affiliation: Department of Radiology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655.
authors.forename: Mark S
authors.initials: MS
authors.lastname: Smyczynski
authors.affiliation: Biomedical Engineering Dept., University of Houston, Houston, TX.
authors.forename: Howard C
authors.initials: HC
authors.lastname: Gifford
authors.affiliation: Department of Radiology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655.
authors.forename: Andre
authors.initials: A
authors.lastname: Lehovich
authors.affiliation: Signature Science LLC, Egg Harbor Township, NJ.
authors.forename: Joseph E
authors.initials: JE
authors.lastname: McNamara
authors.affiliation: Duke Advanced Imaging Laboratories, Duke University Medical Center, Durham, NC.
authors.forename: W Paul
authors.initials: WP
authors.lastname: Segars
authors.affiliation: Department of Radiology, University of Iowa, Iowa City, IA.
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
authors.affiliation: Department of Radiology, Johns Hopkins University, Baltimore, MD.
authors.forename: Benjamin M W
authors.initials: BM
authors.lastname: Tsui
authors.affiliation: Department of Radiology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655 (telephone: 508-856-4255, michael.king@umassmed.edu ).
authors.forename: Michael A
authors.initials: MA
authors.lastname: King
keywords: SPECT data quantification and correction methods; image generation; image quality assessment; simulation