Anthropomorphic dual-lattice voxel models for optimizing image quality and dose

Nina Petoussi-Henss; Helmut Schlattl; Janine Becker; Matthias Greiter; Maria Zankl; Christoph Hoeschen

doi:10.1117/1.JMI.4.1.013509

30 March 2017 Anthropomorphic dual-lattice voxel models for optimizing image quality and dose

Nina Petoussi-Henss, Helmut Schlattl, Janine Becker, Matthias Greiter, Maria Zankl, Christoph Hoeschen

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 1, 013509 (March 2017). https://doi.org/10.1117/1.JMI.4.1.013509

Abstract

Using numerical simulations, the influence of various imaging parameters on the resulting image can be determined for various imaging technologies. To achieve this, visualization of fine tissue structures needed to evaluate the image quality with different radiation quality and dose is essential. The present work examines a method that employs simulations of the imaging process using Monte Carlo methods and a combination of a standard and higher resolution voxel models. A hybrid model, based on nonlinear uniform rational B-spline and polygon mesh surfaces, was constructed from an existing voxel model of a female patient of a resolution in the range of millimeters. The resolution of the hybrid model was 500 μm, i.e., substantially finer than that of the original model. Furthermore, a high resolution lung voxel model [(0.11 mm)3 voxel volume, slice thickness: 114 μm] was developed from the specimen of a left lung lobe. This has been inserted into the hybrid model, substituting its left lung lobe and resulting in a dual-lattice geometry model. “Dual lattice” means, in this context, the combination of voxel models with different resolutions. Monte Carlo simulations of radiographic imaging were performed and the fine structure of the lung was easily recognizable.

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Nina Petoussi-Henss, Helmut Schlattl, Janine Becker, Matthias Greiter, Maria Zankl, and Christoph Hoeschen "Anthropomorphic dual-lattice voxel models for optimizing image quality and dose," Journal of Medical Imaging 4(1), 013509 (30 March 2017). https://doi.org/10.1117/1.JMI.4.1.013509

Received: 16 November 2016; Accepted: 14 March 2017; Published: 30 March 2017

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