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Relevant bibliographies by topics / Giga-Voxel

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Journal articles

Academic literature on the topic 'Giga-Voxel'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Giga-Voxel"

1

Aage, Niels, Erik Andreassen, Boyan S. Lazarov, and Ole Sigmund. "Giga-voxel computational morphogenesis for structural design." Nature 550, no. 7674 (2017): 84–86. http://dx.doi.org/10.1038/nature23911.

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2

Lopes, Pedro Cortez Fetter, Federico Semeraro, André Maués Brabo Pereira, and Ricardo Leiderman. "Enabling FEM-based absolute permeability estimation in giga-voxel porous media with a single GPU." Computer Methods in Applied Mechanics and Engineering 434 (February 2025): 117559. http://dx.doi.org/10.1016/j.cma.2024.117559.

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3

Subochev, Pavel, Florentin Spadin, Valeriya Perekatova, et al. "Toward Real-Time Giga-Voxel Optoacoustic/Photoacoustic Microscopy: GPU-Accelerated Fourier Reconstruction with Quasi-3D Implementation." Photonics 9, no. 1 (2021): 15. http://dx.doi.org/10.3390/photonics9010015.

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We propose a GPU-accelerated implementation of frequency-domain synthetic aperture focusing technique (SAFT) employing truncated regularized inverse k-space interpolation. Our implementation achieves sub-1s reconstruction time for data sizes of up to 100 M voxels, providing more than a tenfold decrease in reconstruction time as compared to CPU-based SAFT. We provide an empirical model that can be used to predict the execution time of quasi-3D reconstruction for any data size given the specifications of the computing system.

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4

Shruthishree, S.H, and Tiwari Harshvardhan. "A REVIEW PAPER ON MEDICAL IMAGE PROCESSING." International Journal of Research - Granthaalayah 5, no. 4 RACSIT (2017): 21–29. https://doi.org/10.5281/zenodo.572290.

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Biomedical image processing has experienced dramatic expansion, and has been an interdisciplinary research field attracting expertise from applied mathematics, computer sciences, engineering, statistics, physics, biology and medicine. Computer-aided diagnostic processing has already become an important part of clinical routine. Accompanied by a rush of new development of high technology and use of various imaging modalities, more challenges arise; for example, how to process and analyze a significant volume of images so that high quality information can be produced for disease diagnoses and tr

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5

Soldevila, Fernando, Armin Lenz, Alberto Ghezzi, Andrea Farina, Cosimo D'Andrea, and Enrique Tajahuerce. "Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion." Optics Letters, July 30, 2021. http://dx.doi.org/10.1364/ol.434127.

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6

Hirabayashi, Yu, Haruka Iga, Hiroki Ogawa, Shinnosuke Tokuta, Yusuke Shimada, and Akiyasu Yamamoto. "Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials." npj Computational Materials 10, no. 1 (2024). http://dx.doi.org/10.1038/s41524-024-01226-5.

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AbstractThe microstructure is a critical factor governing the functionality of ceramic materials. Meanwhile, microstructural analysis of electron microscopy images of polycrystalline ceramics, which are geometrically complex and composed of countless crystal grains with porosity and secondary phases, has generally been performed manually by human experts. Objective pixel-based analysis (semantic segmentation) with high accuracy is a simple but critical step for quantifying microstructures. In this study, we apply neural network-based semantic segmentation to secondary electron images of polycr

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