Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

Research output: Contribution to journalArticlepeer-review


Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according toFessler [Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography, IEEE Trans. Image Process. 5(3),

Original languageEnglish (US)
Article number081902
JournalMedical physics
Issue number8
StatePublished - 2014


  • cascaded systems analysis
  • cone-beam CT
  • detectability index
  • image quality
  • imaging task
  • modulation transfer function
  • noise-power spectrum
  • nonstationary noise
  • penalized-likelihood reconstruction
  • statistical reconstruction

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation'. Together they form a unique fingerprint.

Cite this