30 March 2016 Reduction of artifacts in computer simulation of breast Cooper's ligaments
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Abstract
Anthropomorphic software breast phantoms have been introduced as a tool for quantitative validation of breast imaging systems. Efficacy of the validation results depends on the realism of phantom images. The recursive partitioning algorithm based upon the octree simulation has been demonstrated as versatile and capable of efficiently generating large number of phantoms to support virtual clinical trials of breast imaging. Previously, we have observed specific artifacts, (here labeled “dents”) on the boundaries of simulated Cooper’s ligaments. In this work, we have demonstrated that these “dents” result from the approximate determination of the closest simulated ligament to an examined subvolume (i.e., octree node) of the phantom. We propose a modification of the algorithm that determines the closest ligament by considering a pre-specified number of neighboring ligaments selected based upon the functions that govern the shape of ligaments simulated in the subvolume. We have qualitatively and quantitatively demonstrated that the modified algorithm can lead to elimination or reduction of dent artifacts in software phantoms. In a proof-of concept example, we simulated a 450 ml phantom with 333 compartments at 100 micrometer resolution. After the proposed modification, we corrected 148,105 dents, with an average size of 5.27 voxels (5.27nl). We have also qualitatively analyzed the corresponding improvement in the appearance of simulated mammographic images. The proposed algorithm leads to reduction of linear and star-like artifacts in simulated phantom projections, which can be attributed to dents. Analysis of a larger number of phantoms is ongoing.
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David D. Pokrajac, David D. Pokrajac, Adam Kuperavage, Adam Kuperavage, Andrew D. A. Maidment, Andrew D. A. Maidment, Predrag R. Bakic, Predrag R. Bakic, } "Reduction of artifacts in computer simulation of breast Cooper's ligaments", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97832Q (30 March 2016); doi: 10.1117/12.2217160; https://doi.org/10.1117/12.2217160
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