title: Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging.
abstract: OBJECTIVE
  This work applies a cascaded systems model for cone-beam CT imaging performance to the design and optimization of a system for musculoskeletal extremity imaging. The model provides a quantitative guide to the selection of system geometry, source and detector components, acquisition techniques, and reconstruction parameters.
  
  
  METHODS
  The model is based on cascaded systems analysis of the 3D noise-power spectrum (NPS) and noise-equivalent quanta (NEQ) combined with factors of system geometry (magnification, focal spot size, and scatter-to-primary ratio) and anatomical background clutter. The model was extended to task-based analysis of detectability index (d') for tasks ranging in contrast and frequency content, and d' was computed as a function of system magnification, detector pixel size, focal spot size, kVp, dose, electronic noise, voxel size, and reconstruction filter to examine trade-offs and optima among such factors in multivariate analysis. The model was tested quantitatively versus the measured NPS and qualitatively in cadaver images as a function of kVp, dose, pixel size, and reconstruction filter under conditions corresponding to the proposed scanner.
  
  
  RESULTS
  The analysis quantified trade-offs among factors of spatial resolution, noise, and dose. System magnification (M) was a critical design parameter with strong effect on spatial resolution, dose, and x-ray scatter, and a fairly robust optimum was identified at M ∼ 1.3 for the imaging tasks considered. The results suggested kVp selection in the range of ∼65-90 kVp, the lower end (65 kVp) maximizing subject contrast and the upper end maximizing NEQ (90 kVp). The analysis quantified fairly intuitive results-e.g., ∼0.1-0.2 mm pixel size (and a sharp reconstruction filter) optimal for high-frequency tasks (bone detail) compared to ∼0.4 mm pixel size (and a smooth reconstruction filter) for low-frequency (soft-tissue) tasks. This result suggests a specific protocol for 1 × 1 (full-resolution) projection data acquisition followed by full-resolution reconstruction with a sharp filter for high-frequency tasks along with 2 × 2 binning reconstruction with a smooth filter for low-frequency tasks. The analysis guided selection of specific source and detector components implemented on the proposed scanner. The analysis also quantified the potential benefits and points of diminishing return in focal spot size, reduced electronic noise, finer detector pixels, and low-dose limits of detectability. Theoretical results agreed quantitatively with the measured NPS and qualitatively with evaluation of cadaver images by a musculoskeletal radiologist.
  
  
  CONCLUSIONS
  A fairly comprehensive model for 3D imaging performance in cone-beam CT combines factors of quantum noise, system geometry, anatomical background, and imaging task. The analysis provided a valuable, quantitative guide to design, optimization, and technique selection for a musculoskeletal extremities imaging system under development.
authors: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
authors: P
authors: P
authors: Prakash
authors: W
authors: W
authors: Zbijewski
authors: G J
authors: GJ
authors: Gang
authors: Y
authors: Y
authors: Ding
authors: J W
authors: JW
authors: Stayman
authors: J
authors: J
authors: Yorkston
authors: J A
authors: JA
authors: Carrino
authors: J H
authors: JH
authors: Siewerdsen
mesh_terms: D000465:Algorithms; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008953:Models, Anatomic; D015233:Models, Statistical; D008962:Models, Theoretical; D015999:Multivariate Analysis; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results