3D Digital Breast Tomosynthesis Using Total Variation Regularization

Kastanis, Iason; Arridge, Simon; Stewart, Alex; Gunn, Spencer; Ullberg, Christer; Francke, T.

doi:10.1007/978-3-540-70538-3_86

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Given their infancy, most of these algorithms are not yet used in any system that is commercially available or close to this stage. Among the proposed algorithms, there are algorithms based on total variation regularization, 27,28 total p variation regularization, [29][30][31] joint entropy regularization, 32,33 adaptive diffusion regularization, 34 iterative penalized maximum likelihood (especially to improve microcalcification visibility), 35 iterative maximum a posteriori statistical reconstruction, 36 and Bayesian inference. 37 In a very different approach, Wu et al proposed a reconstruction algorithm that results in spherically symmetric "voxels" as opposed to the traditional cubic voxel.…”

Section: Iic Novel Reconstruction Methodsmentioning

confidence: 99%

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

2013

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Many important post-acquisition aspects of breast tomosynthesis imaging can impact its clinical performance. Chief among them is the reconstruction algorithm that generates the representation of the three-dimensional breast volume from the acquired projections. But even after reconstruction, additional processes, such as artifact reduction algorithms, computer aided detection and diagnosis, among others, can also impact the performance of breast tomosynthesis in the clinical realm. In this two part paper, a review of breast tomosynthesis research is performed, with an emphasis on its medical physics aspects. In the companion paper, the first part of this review, the research performed relevant to the image acquisition process is examined. This second part will review the research on the post-acquisition aspects, including reconstruction, image processing, and analysis, as well as the advanced applications being investigated for breast tomosynthesis.

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Section: Iic Novel Reconstruction Methodsmentioning

confidence: 99%

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

2013

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“…The algorithm adopted by IMS has been developed by Dexela [15] and is based on an iterative method that uses Total Variation regularisation. Iterative methods have been proposed as alternative to the common Filtered Back Projection methods in the case of a limited number of x-ray projections so as to reduce streaking artefacts and to increase the signal-to-noise ratio [16]. The projections are first converted into density images and then back projected into the volume which is expected to contain all the projected information.…”

Section: Software For Tomosynthesis Reconstructionmentioning

confidence: 99%

A novel approach to digital breast tomosynthesis for simultaneous acquisition of 2D and 3D images

et al. 2010

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“…al. [19] implemented a total variation optimization approach and Chen and Barner [6] use a Markov random fields regularization function. For the polyenergetic case, Elbakri and Fessler [11] used a convex, edgepreserving Huber penalty for its desirable properties.…”

Section: Iterative Reconstruction Algorithmsmentioning

confidence: 99%

Numerical Algorithms for Polyenergetic Digital Breast Tomosynthesis Reconstruction

Chung¹,

Nagy²,

Sechopoulos³

2010

SIAM J. Imaging Sci.

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Digital tomosynthesis imaging is becoming increasingly significant in a variety of medical imaging applications. Tomosynthesis imaging involves the acquisition of a series of projection images over a limited angular range, which, after reconstruction, results in a pseudo-3D representation of the imaged object. The partial separation of features in the third dimension improves the visibility of lesions of interest by reducing the effect of the superimposition of tissues. In breast cancer imaging, tomosynthesis is a viable alternative to standard mammography; however, current algorithms for image reconstruction do not take into account the polyenergetic nature of the x-ray source beam entering the object. This results in inaccuracies in the reconstruction, making quantitative analysis challenging and allowing for beam hardening artifacts. In this paper, we develop a mathematical framework based on a polyenergetic model and develop statistically based iterative methods for digital tomosynthesis reconstruction for breast imaging. By applying our algorithms to simulated data, we illustrate the success of our methods in suppressing beam hardening artifacts, and thus improving the quality of the reconstruction.

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3D Digital Breast Tomosynthesis Using Total Variation Regularization

Cited by 10 publications

References 10 publications

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

A novel approach to digital breast tomosynthesis for simultaneous acquisition of 2D and 3D images

Numerical Algorithms for Polyenergetic Digital Breast Tomosynthesis Reconstruction

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