title: Model-based PSF and MTF estimation and validation from skeletal clinical CT images.
abstract: OBJECTIVE
  A method was developed to correct for systematic errors in estimating the thickness of thin bones due to image blurring in CT images using bone interfaces to estimate the point-spread-function (PSF). This study validates the accuracy of the PSFs estimated using said method from various clinical CT images featuring cortical bones.
  
  
  METHODS
  Gaussian PSFs, characterized by a different extent in the z (scan) direction than in the x and y directions were obtained using our method from 11 clinical CT scans of a cadaveric craniofacial skeleton. These PSFs were estimated for multiple combinations of scanning parameters and reconstruction methods. The actual PSF for each scan setting was measured using the slanted-slit technique within the image slice plane and the longitudinal axis. The Gaussian PSF and the corresponding modulation transfer function (MTF) are compared against the actual PSF and MTF for validation.
  
  
  RESULTS
  The differences (errors) between the actual and estimated full-width half-max (FWHM) of the PSFs were 0.09 ± 0.05 and 0.14 ± 0.11 mm for the xy and z axes, respectively. The overall errors in the predicted frequencies measured at 75%, 50%, 25%, 10%, and 5% MTF levels were 0.06 ± 0.07 and 0.06 ± 0.04 cycles/mm for the xy and z axes, respectively. The accuracy of the estimates was dependent on whether they were reconstructed with a standard kernel (Toshiba's FC68, mean error of 0.06 ± 0.05 mm, MTF mean error 0.02 ± 0.02 cycles/mm) or a high resolution bone kernel (Toshiba's FC81, PSF FWHM error 0.12 ± 0.03 mm, MTF mean error 0.09 ± 0.08 cycles/mm).
  
  
  CONCLUSIONS
  The method is accurate in 3D for an image reconstructed using a standard reconstruction kernel, which conforms to the Gaussian PSF assumption but less accurate when using a high resolution bone kernel. The method is a practical and self-contained means of estimating the PSF in clinical CT images featuring cortical bones, without the need phantoms or any prior knowledge about the scanner-specific parameters.
authors.affiliation: Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada and Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3M2, Canada.
authors.forename: Amirreza
authors.initials: A
authors.lastname: Pakdel
authors.affiliation: Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada.
authors.forename: James G
authors.initials: JG
authors.lastname: Mainprize
authors.affiliation: Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada.
authors.forename: Normand
authors.initials: N
authors.lastname: Robert
authors.affiliation: Division of Plastic Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario M4N 3M5, Canada and Department of Surgery, University of Toronto, Toronto, Ontario M5S 3M2, Canada.
authors.forename: Jeffery
authors.initials: J
authors.lastname: Fialkov
authors.affiliation: Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada and Department of Surgery, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3M2, Canada.
authors.forename: Cari M
authors.initials: CM
authors.lastname: Whyne
mesh_terms: D001842:Bone and Bones / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D014057:Tomography, X-Ray Computed / Q000379:methods*