Accelerating ordered-subsets X-ray CT image reconstruction using the linearized augmented Lagrangian framework

Nien, Hung; Fessler, Jeffrey A.

doi:10.1117/12.2042686

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Optimization-based reconstruction (Delaney et al 1998, Elbakri and Fessler 2002, Sidky and Pan 2008, Tang et al 2009, Pan et al 2009, Bian et al 2010, Stsepankou et al 2012, Nien and Fessler 2014, Wang et al 2014 remains the focus of current research on CBCT algorithms. Evidence seems to suggest that appropriately designed optimization-based algorithms may be less susceptible to data inconsistency and insufficiency than analytic algorithms such as the FDK algorithm, thus possibly yielding reconstructions with reduced artifacts (Parker 1982, Dennerlein et al 2008.…”

Section: Introductionmentioning

confidence: 99%

Artifact reduction in short-scan CBCT by use of optimization-based reconstruction

et al. 2016

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Increasing interest in optimization-based reconstruction in research on, and applications of, cone-beam computed tomography (CBCT) exists because it has been shown to have to potential to reduce artifacts observed in reconstructions obtained with the Feldkamp–Davis–Kress (FDK) algorithm (or its variants), which is used extensively for image reconstruction in current CBCT applications. In this work, we carried out a study on optimization-based reconstruction for possible reduction of artifacts in FDK reconstruction specifically from short-scan CBCT data. The investigation includes a set of optimization programs such as the image-total-variation (TV)-constrained data-divergency minimization, data-weighting matrices such as the Parker weighting matrix, and objects of practical interest for demonstrating and assessing the degree of artifact reduction. Results of investigative work reveal that appropriately designed optimization-based reconstruction, including the image-TV-constrained reconstruction, can reduce significant artifacts observed in FDK reconstruction in CBCT with a short-scan configuration.

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Section: Introductionmentioning

confidence: 99%

Artifact reduction in short-scan CBCT by use of optimization-based reconstruction

et al. 2016

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“…300-900) of uniformly distributed projection views, and analytic algorithms such as the FDK (Feldkamp et al 1984) or its variant are used for image reconstruction (Johnson et al 1998, Grass et al 1999, Wiesent et al 2000. There is also rapidly growing interest in developing optimization-based reconstruction for CBCT (Delaney and Bresler 1998, Elbakri and Fessler 2002, Sidky et al 2006, Pan et al 2009, Tang et al 2009, Stsepankou et al 2012, Nien and Fessler 2014, Wang et al 2014 especially in the context of OBI (Keall 2004, Yoo et al 2006, Bissonnette et al 2008, Wang et al 2008, Klein et al 2009, Choi et al 2010, Kim et al 2010, Tian et al 2011, Yan et al 2012, because it may possess some potential advantages over analytic reconstruction such as FDK in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy

et al. 2015

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Kilo-voltage (KV) cone-beam computed tomography (CBCT) unit mounted onto a linear accelerator treatment system, often referred to as on-board imager (OBI), plays an increasingly important role in image-guide radiation therapy. While the FDK algorithm is used currently for reconstructing images from clinical OBI data, optimization-based reconstruction has also been investigated for OBI CBCT. An optimization-based reconstruction involves numerous parameters, which can significantly impact reconstruction properties (or utility). The success of an optimization-based reconstruction for a particular class of practical applications thus relies strongly on appropriate selection of parameter values. In the work, we focus on tailoring the constrained-TV-minimization-based reconstruction, an optimization-based reconstruction previously shown of some potential for CBCT imaging conditions of practical interest, to OBI imaging through appropriate selection of parameter values. In particular, for given real data of phantoms and patient collected with OBI CBCT, we first devise utility metrics specific to OBI-quality-assurance tasks and then apply them to guiding the selection of parameter values in constrained-TV-minimization-based reconstruction. The study results show that the reconstructions are with improvement, relative to clinical FDK reconstruction, in both visualization and quantitative assessments in terms of the devised utility metrics.

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Super-Resolution and Sparse View CT Reconstruction

Zang

Aly

Idoughi

et al. 2018

Computer Vision – ECCV 2018

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We present a flexible framework for robust computed tomography (CT) reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. To reconstruct such structures at resolutions below the Nyquist limit of the CT image sensor, we devise a new 3D structure tensor prior, which can be incorporated as a regularizer into more traditional proximal optimization methods for CT reconstruction. As a second, smaller contribution, we also show that when using such a proximal reconstruction framework, it is beneficial to employ the Simultaneous Algebraic Reconstruction Technique (SART) instead of the commonly used Conjugate Gradient (CG) method in the solution of the data term proximal operator. We show empirically that CG often does not converge to the global optimum for tomography problem even though the underlying problem is convex. We demonstrate that using SART provides better reconstruction results in sparse-view settings using fewer projection images. We provide extensive experimental results for both contributions on both simulated and real data. Moreover, our code will also be made publicly available.

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Accelerating ordered-subsets X-ray CT image reconstruction using the linearized augmented Lagrangian framework

Cited by 4 publications

References 15 publications

Artifact reduction in short-scan CBCT by use of optimization-based reconstruction

Artifact reduction in short-scan CBCT by use of optimization-based reconstruction

Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy

Super-Resolution and Sparse View CT Reconstruction

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