• Korean Journal of Computational Design and Engineering
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• v.16 no.4
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• pp.285-291
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• 2011

A pose estimation process from medical images is calculating locations and orientations of objects obtained from Computed Tomography (CT) volume data utilizing X-ray images from two directions. In this process, digitally reconstructed radiograph (DRR) images of spatially transformed objects are generated and compared to X-ray images repeatedly until reasonable transformation matrices of the objects are found. The DRR generation and image comparison take majority of the total time for this pose estimation. In this paper, a fast DRR generation technique based on GPU parallel computing is introduced. A volume ray-casting algorithm is explained with brief vector operations and a parallelization technique of the algorithm using Compute Unified Device Architecture (CUDA) is discussed. This paper also presents the implementation results and time measurements comparing to those from pure-CPU implementation and open source toolkit.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.91-96
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• 2007

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution within the interior of a body from measurements made on its surface. One recent application area of the EIT is the detection of breast cancer by imaging the conductivity and permittivity distribution inside the breast. The present standard for breast cancer detection is X-ray mammography, and it is desirable that EIT and X-ray mammography use the same geometry. A forward model of a simplified mammography geometry for EIT imaging was proposed earlier. In this paper, we propose an iterative algorithm for computing the current pattern that will be applied to the electrodes. The current pattern applied to the electrodes influences the voltages measured on the electrodes. Since the measured voltage data is going to be used in the impedance imaging computation, it is desirable to apply currents that result in the largest possible voltage signal. We compute the eigenfunctions for a homogenous medium that will be applied as current patterns to the electrodes. The algorithm for the computation of the eigenfunctions is presented. The convergence of the algorithm is shown by computing the eigencurrent of the simplified mammography geometry.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2759-2764
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• 2014

Aim: This study aims to investigate the manifestation of CT, MRI and dynamic enhanced scans for primary hepatic neuroendocrine cell carcinoma. Methods: CT or MRI arterial and venous phase scan images of 19 cases of pathologically confirmed PHNEC were retrospectively analyzed. Results: 14 cases (73.68%) with single lesion, 5 cases (26.3%) with multiple lesions, with an average diameter of 13.2 cm. Some 12 cases (63.16%) showed inhomogeneous enhancement, seven cases (36.8%) showed homogeneous enhancement, 13 cases (68.4%) demonstrated significant enhancement in the arterial phase, 13 cases (68.4%) had significantly enhanced portal venous phase including 7 cases (36.8 %) with portal venous phase density or signal above the arterial phase and 5 cases (26.3%) with the portal vein density or signal below the arterial phase. Seven cases (36.8%) had continued strengthened separate shadows in the center of the lesion. Thrombosis were not seen in portal veins. Conclusion: CT and MRI images of liver cell neuroendocrine carcinoma have certain characteristics that can provide valuable information for diagnosis and differential diagnosis.