title: A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT.
abstract: Task-based image quality assessment using model observers is promising to provide an efficient, quantitative, and objective approach to CT dose optimization. Before this approach can be reliably used in practice, its correlation with radiologist performance for the same clinical task needs to be established. Determining human observer performance for a well-defined clinical task, however, has always been a challenge due to the tremendous amount of efforts needed to collect a large number of positive cases. To overcome this challenge, we developed an accurate projection-based insertion technique. In this study, we present a virtual clinical trial using this tool and a low-dose simulation tool to determine radiologist performance on lung-nodule detection as a function of radiation dose, nodule type, nodule size, and reconstruction methods. The lesion insertion and low-dose simulation tools together were demonstrated to provide flexibility to generate realistically-appearing clinical cases under well-defined conditions. The reader performance data obtained in this virtual clinical trial can be used as the basis to develop model observers for lung nodule detection, as well as for dose and protocol optimization in lung cancer screening CT.
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Yu
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Qiyuan
authors.initials: Q
authors.lastname: Hu
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Chi Wan
authors.initials: CW
authors.lastname: Koo
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Edwin A
authors.initials: EA
authors.lastname: Takahashi
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: David L
authors.initials: DL
authors.lastname: Levin
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Tucker F
authors.initials: TF
authors.lastname: Johnson
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Megan J
authors.initials: MJ
authors.lastname: Hora
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Shane
authors.initials: S
authors.lastname: Dirks
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Baiyu
authors.initials: B
authors.lastname: Chen
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Kyle
authors.initials: K
authors.lastname: McMillan
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Shuai
authors.initials: S
authors.lastname: Leng
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: J G
authors.initials: JG
authors.lastname: Fletcher
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN.
authors.forename: Cynthia H
authors.initials: CH
authors.lastname: McCollough
keywords: Computed tomography (CT); dose optimization; image quality; model observer; observer study