title: Regularization Design and Control of Change Admission in Prior-Image-based Reconstruction.
abstract: Nearly all reconstruction methods are controlled through various parameter selections. Traditionally, such parameters are used to specify a particular noise and resolution trade-off in the reconstructed image volumes. The introduction of reconstruction methods that incorporate prior image information has demonstrated dramatic improvements in dose utilization and image quality, but has complicated the selection of reconstruction parameters including those associated with balancing information used from prior images with that of the measurement data. While a noise-resolution tradeoff still exists, other potentially detrimental effects are possible with poor prior image parameter values including the possible introduction of false features and the failure to incorporate sufficient prior information to gain any improvements. Traditional parameter selection methods such as heuristics based on similar imaging scenarios are subject to error and suboptimal solutions while exhaustive searches can involve a large number of time-consuming iterative reconstructions. We propose a novel approach that prospectively determines optimal prior image regularization strength to accurately admit specific anatomical changes without performing full iterative reconstructions. This approach leverages analytical approximations to the implicitly defined prior image-based reconstruction solution and predictive metrics used to estimate imaging performance. The proposed method is investigated in phantom experiments and the shift-variance and data-dependence of optimal prior strength is explored. Optimal regularization based on the predictive approach is shown to agree well with traditional exhaustive reconstruction searches, while yielding substantial reductions in computation time. This suggests great potential of the proposed methodology in allowing for prospective patient-, data-, and change-specific customization of prior-image penalty strength to ensure accurate reconstruction of specific anatomical changes.
authors.affiliation: Departments of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
authors.forename: Hao
authors.initials: H
authors.lastname: Dang
authors.affiliation: Departments of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
authors.forename: Jeffrey H
authors.initials: JH
authors.lastname: Siewerdsen
authors.affiliation: Departments of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
authors.forename: J Webster
authors.initials: JW
authors.lastname: Stayman
keywords: CT reconstruction; Dose reduction; Noise-resolution tradeoff; Penalized-likelihood; Prior-image reconstruction; Regularization; Shift variance; Sparse data