Penalized-likelihood sinogram smoothing for low-dose CT.
We have developed a sinogram smoothing approach for low-dose computed tomography (CT) that seeks to estimate the line integrals needed for reconstruction from the noisy measurements by maximizing a penalized-likelihood objective function. The maximization is performed by an algorithm derived by use of the separable paraboloidal surrogates framework. The approach overcomes some of the computational limitations of a previously proposed spline-based penalized-likelihood sinogram smoothing approach, and it is found to yield better resolution-variance tradeoffs than this spline-based approach as well an existing adaptive filtering approach. Such sinogram smoothing approaches could be valuable when applied to the low-dose data acquired in CT screening exams, such as those being considered for lung-nodule detection.
La Rivière
Patrick J
PJ
Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA. pjlarivi@midway.uchicago.edu
D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D016013:Likelihood Functions; D015233:Models, Statistical; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted; D011874:Radiometry; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors; D014055:Tomography, Emission-Computed; D014057:Tomography, X-Ray Computed / Q000379:methods*