title: Lesion Insertion in Projection Domain for Computed Tomography Image Quality Assessment.
abstract: To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way to achieve this objective is to create hybrid images that combine patient images with simulated lesions. Because conventional hybrid images generated in the image-domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Liver lesion models were forward projected according to the geometry of a commercial CT scanner to acquire lesion projections. The lesion projections were then inserted into patient projections (decoded from commercial CT raw data with the assistance of the vendor) and reconstructed to acquire hybrid images. To validate the accuracy of the forward projection geometry, simulated images reconstructed from the forward projections of a digital ACR phantom were compared to physically acquired ACR phantom images. To validate the hybrid images, lesion models were inserted into patient images and visually assessed. Results showed that the simulated phantom images and the physically acquired phantom images had great similarity in terms of HU accuracy and high-contrast resolution. The lesions in the hybrid image had a realistic appearance and merged naturally into the liver background. In addition, the inserted lesion demonstrated reconstruction-parameter-dependent appearance. Compared to conventional image-domain approach, our method enables more realistic hybrid images for image quality assessment.
authors.affiliation: Radiology Department, Mayo Clinic, Rochester, MN 55905 USA.
authors.forename: Baiyu
authors.initials: B
authors.lastname: Chen
authors.affiliation: Radiology Department, Mayo Clinic, Rochester, MN 55905 USA.
authors.forename: Zhicong
authors.initials: Z
authors.lastname: Yu
authors.affiliation: Radiology Department, Mayo Clinic, Rochester, MN 55905 USA.
authors.forename: Shuai
authors.initials: S
authors.lastname: Leng
authors.affiliation: Radiology Department, Mayo Clinic, Rochester, MN 55905 USA.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Yu
authors.affiliation: Radiology Department, Mayo Clinic, Rochester, MN 55905 USA.
authors.forename: Cynthia
authors.initials: C
authors.lastname: McCollough
keywords: Computed tomography (CT); Hybrid images; Image quality assessment; Lesion insertion