title: LROC Investigation of Three Strategies for Reducing the Impact of Respiratory Motion on the Detection of Solitary Pulmonary Nodules in SPECT.
abstract: The objective of this investigation was to determine the effectiveness of three motion reducing strategies in diminishing the degrading impact of respiratory motion on the detection of small solitary pulmonary nodules (SPN) in single photon emission computed tomographic (SPECT) imaging in comparison to a standard clinical acquisition and the ideal case of imaging in the absence of respiratory motion. To do this non-uniform rational B-spline cardiac-torso (NCAT) phantoms based on human-volunteer CT studies were generated spanning the respiratory cycle for a normal background distribution of Tc-99m NeoTect. Similarly, spherical phantoms of 1.0 cm diameter were generated to model small SPN for each of 150 uniquely located sites within the lungs whose respiratory motion was based on the motion of normal structures in the volunteer CT studies. The SIMIND Monte Carlo program was used to produce SPECT projection data from these. Normal and single-lesion containing SPECT projection sets with a clinically realistic Poisson noise level were created for the cases of: 1) the end-expiration (EE) frame with all counts, 2) respiration-averaged motion with all counts, 3) one-fourth of the 32 frames centered around EE (Quarter-Binning), 4) one-half of the 32 frames centered around EE (Half-Binning), and 5) eight temporally binned frames spanning the respiratory cycle. Each of the sets of combined projection data were reconstructed with RBI-EM with system spatial-resolution compensation (RC). Based on the known motion for each of the 150 different lesions, the reconstructed volumes of respiratory bins were shifted so as to superimpose the locations of the SPN onto that in the first bin (Reconstruct and Shift). Five human-observers performed localization receiver operating characteristics (LROC) studies of SPN detection. The observer results were analyzed for statistical significance differences in SPN detection accuracy among the three correction strategies, the standard acquisition, and the ideal case of the absence of respiratory motion. Our human-observer LROC determined that Quarter-Binning and Half-Binning strategies resulted in SPN detection accuracy statistically significantly below (P < 0.05) that of standard clinical acquisition, whereas the Reconstruct and Shift strategy resulted in a detection accuracy not statistically significantly different from that of the ideal case. This investigation demonstrates that tumor detection based on acquisitions associated with less than all the counts which could potentially be employed may result in poorer detection despite limiting the motion of the lesion. The Reconstruct and Shift method results in tumor detection that is equivalent to ideal motion correction.
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 Physics & Astronomy, Louisiana State University, LA.
authors.forename: Joyoni
authors.initials: J
authors.lastname: Dey
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 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