title: Spatial frequency spectrum of the x-ray scatter distribution in CBCT projections.
abstract: OBJECTIVE
  X-ray scatter is a source of significant image quality loss in cone-beam computed tomography (CBCT). The use of Monte Carlo (MC) simulations separating primary and scattered photons has allowed the structure and nature of the scatter distribution in CBCT to become better elucidated. This work seeks to quantify the structure and determine a suitable basis function for the scatter distribution by examining its spectral components using Fourier analysis.
  
  
  METHODS
  The scatter distribution projection data were simulated using a CBCT MC model based on the EGSnrc code. CBCT projection data, with separated primary and scatter signal, were generated for a 30.6 cm diameter water cylinder [single angle projection with varying axis-to-detector distance (ADD) and bowtie filters] and two anthropomorphic phantoms (head and pelvis, 360 projections sampled every 1°, with and without a compensator). The Fourier transform of the resulting scatter distributions was computed and analyzed both qualitatively and quantitatively. A novel metric called the scatter frequency width (SFW) is introduced to determine the scatter distribution's frequency content. The frequency content results are used to determine a set basis functions, consisting of low-frequency sine and cosine functions, to fit and denoise the scatter distribution generated from MC simulations using a reduced number of photons and projections. The signal recovery is implemented using Fourier filtering (low-pass Butterworth filter) and interpolation. Estimates of the scatter distribution are used to correct and reconstruct simulated projections.
  
  
  RESULTS
  The spatial and angular frequencies are contained within a maximum frequency of 0.1 cm(-1) and 7/(2π) rad(-1) for the imaging scenarios examined, with these values varying depending on the object and imaging setup (e.g., ADD and compensator). These data indicate spatial and angular sampling every 5 cm and π/7 rad (~25°) can be used to properly capture the scatter distribution, with reduced sampling possible depending on the imaging scenario. Using a low-pass Butterworth filter, tuned with the SFW values, to denoise the scatter projection data generated from MC simulations using 10(6) photons resulted in an error reduction of greater than 85% for the estimating scatter in single and multiple projections. Analysis showed that the use of a compensator helped reduce the error in estimating the scatter distribution from limited photon simulations by more than 37% when compared to the case without a compensator for the head and pelvis phantoms. Reconstructions of simulated head phantom projections corrected by the filtered and interpolated scatter estimates showed improvements in overall image quality.
  
  
  CONCLUSIONS
  The spatial frequency content of the scatter distribution in CBCT is found to be contained within the low frequency domain. The frequency content is modulated both by object and imaging parameters (ADD and compensator). The low-frequency nature of the scatter distribution allows for a limited set of sine and cosine basis functions to be used to accurately represent the scatter signal in the presence of noise and reduced data sampling decreasing MC based scatter estimation time. Compensator induced modulation of the scatter distribution reduces the frequency content and improves the fitting results.
authors: Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Radiation Medicine Program, Princess Margaret Hospital, Toronto, Ontario M5G 2M9, Canada.
authors: G J
authors: GJ
authors: Bootsma
authors: F
authors: F
authors: Verhaegen
authors: D A
authors: DA
authors: Jaffray
mesh_terms: D000465:Algorithms; D000886:Anthropometry; D003198:Computer Simulation; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D005583:Fourier Analysis; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D009010:Monte Carlo Method; D010388:Pelvis / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D017785:Photons; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012984:Software; D014965:X-Rays