Comparing implementations of penalized weighted least-squares sinogram restoration

Journal Article · Mon Nov 15 04:00:00 EST 2010 · Medical Physics

DOI:https://doi.org/10.1118/1.3490476· OSTI ID:22096821

Forthmann, Peter; Koehler, Thomas; Defrise, Michel; La Riviere, Patrick ^[1]

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1. Philips Research Europe, Roentgenstrasse 24-26, 22315 Hamburg (Germany)

Purpose: A CT scanner measures the energy that is deposited in each channel of a detector array by x rays that have been partially absorbed on their way through the object. The measurement process is complex and quantitative measurements are always and inevitably associated with errors, so CT data must be preprocessed prior to reconstruction. In recent years, the authors have formulated CT sinogram preprocessing as a statistical restoration problem in which the goal is to obtain the best estimate of the line integrals needed for reconstruction from the set of noisy, degraded measurements. The authors have explored both penalized Poisson likelihood (PL) and penalized weighted least-squares (PWLS) objective functions. At low doses, the authors found that the PL approach outperforms PWLS in terms of resolution-noise tradeoffs, but at standard doses they perform similarly. The PWLS objective function, being quadratic, is more amenable to computational acceleration than the PL objective. In this work, the authors develop and compare two different methods for implementing PWLS sinogram restoration with the hope of improving computational performance relative to PL in the standard-dose regime. Sinogram restoration is still significant in the standard-dose regime since it can still outperform standard approaches and it allows for correction of effects that are not usually modeled in standard CT preprocessing. Methods: The authors have explored and compared two implementation strategies for PWLS sinogram restoration: (1) A direct matrix-inversion strategy based on the closed-form solution to the PWLS optimization problem and (2) an iterative approach based on the conjugate-gradient algorithm. Obtaining optimal performance from each strategy required modifying the naive off-the-shelf implementations of the algorithms to exploit the particular symmetry and sparseness of the sinogram-restoration problem. For the closed-form approach, the authors subdivided the large matrix inversion into smaller coupled problems and exploited sparseness to minimize matrix operations. For the conjugate-gradient approach, the authors exploited sparseness and preconditioned the problem to speed up convergence. Results: All methods produced qualitatively and quantitatively similar images as measured by resolution-variance tradeoffs and difference images. Despite the acceleration strategies, the direct matrix-inversion approach was found to be uncompetitive with iterative approaches, with a computational burden higher by an order of magnitude or more. The iterative conjugate-gradient approach, however, does appear promising, with computation times half that of the authors' previous penalized-likelihood implementation. Conclusions: Iterative conjugate-gradient based PWLS sinogram restoration with careful matrix optimizations has computational advantages over direct matrix PWLS inversion and over penalized-likelihood sinogram restoration and can be considered a good alternative in standard-dose regimes.

• OSTI ID: 22096821
• Journal Information: Medical Physics, Journal Name: Medical Physics Journal Issue: 11 Vol. 37; ISSN 0094-2405; ISSN MPHYA6
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
62 RADIOLOGY AND NUCLEAR MEDICINE
71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
ACCELERATION
ALGORITHMS
APPROXIMATIONS
BIOLOGICAL RECOVERY
COMPUTERIZED TOMOGRAPHY
IMAGE PROCESSING
IMAGE SCANNERS
IMAGES
ITERATIVE METHODS
LEAST SQUARE FIT
NOISE
OPTIMIZATION
PERFORMANCE
RADIATION DOSES
STANDARDS
VELOCITY
X RADIATION