Penalized-likelihood sinogram smoothing for low-dose CT

• Published in: Medical physics (Lancaster) Vol. 32; no. 6; pp. 1676 - 1683
• Main Author: La Riviere, Patrick J.
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01-06-2005
• Subjects: ALGORITHMS; Computed radiography; Computed tomography; Computer Simulation; computerised tomography; COMPUTERIZED TOMOGRAPHY; Electric measurements; Humans; Image analysis; image denoising; Image Interpretation, Computer-Assisted; IMAGE PROCESSING; Image Processing, Computer-Assisted; Image quality; image reconstruction; Likelihood Functions; lung; LUNGS; Medical image noise; Medical image reconstruction; Medical imaging; Models, Statistical; Phantoms, Imaging; Photons; Poisson's equation; Probability theory; RADIATION DOSES; Radiographic Image Interpretation, Computer-Assisted; RADIOLOGY AND NUCLEAR MEDICINE; Radiometry; SCREENING; Sensitivity and Specificity; Signal Processing, Computer-Assisted; smoothing methods; Software; Time Factors; Tomography; Tomography, Emission-Computed; Tomography, X-Ray Computed - methods
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.1915015

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.