title: Lesion insertion in the projection domain: Methods and initial results.
abstract: OBJECTIVE
  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 of achieving this objective is to create hybrid images that combine patient images with inserted 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.
  
  
  METHODS
  Lesions were segmented from patient images and forward projected to acquire lesion projections. The forward-projection geometry was designed according to a commercial CT scanner and accommodated both axial and helical modes with various focal spot movement patterns. The energy employed by the commercial CT scanner for beam hardening correction was measured and used for the forward projection. The lesion projections were inserted into patient projections decoded from commercial CT projection data. The combined projections were formatted to match those of commercial CT raw data, loaded onto a commercial CT scanner, and reconstructed to create the hybrid images. Two validations were performed. First, to validate the accuracy of the forward-projection geometry, images were reconstructed from the forward projections of a virtual ACR phantom and compared to physically acquired ACR phantom images in terms of CT number accuracy and high-contrast resolution. Second, to validate the realism of the lesion in hybrid images, liver lesions were segmented from patient images and inserted back into the same patients, each at a new location specified by a radiologist. The inserted lesions were compared to the original lesions and visually assessed for realism by two experienced radiologists in a blinded fashion.
  
  
  RESULTS
  For the validation of the forward-projection geometry, the images reconstructed from the forward projections of the virtual ACR phantom were consistent with the images physically acquired for the ACR phantom in terms of Hounsfield unit and high-contrast resolution. For the validation of the lesion realism, lesions of various types were successfully inserted, including well circumscribed and invasive lesions, homogeneous and heterogeneous lesions, high-contrast and low-contrast lesions, isolated and vessel-attached lesions, and small and large lesions. The two experienced radiologists who reviewed the original and inserted lesions could not identify the lesions that were inserted. The same lesion, when inserted into the projection domain and reconstructed with different parameters, demonstrated a parameter-dependent appearance.
  
  
  CONCLUSIONS
  A framework has been developed for projection-domain insertion of lesions into commercial CT images, which can be potentially expanded to all geometries of CT scanners. Compared to conventional image-domain methods, the authors' method reflected the impact of scan and reconstruction parameters on lesion appearance. Compared to prior projection-domain methods, the authors' method has the potential to achieve higher anatomical complexity by employing clinical patient projections and real patient lesions.
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Baiyu
authors.initials: B
authors.lastname: Chen
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Shuai
authors.initials: S
authors.lastname: Leng
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Yu
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Zhicong
authors.initials: Z
authors.lastname: Yu
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Chi
authors.initials: C
authors.lastname: Ma
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Cynthia
authors.initials: C
authors.lastname: McCollough
mesh_terms: D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D015898:Tomography Scanners, X-Ray Computed; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*