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Towards Omni-Tomography—Grand Fusion of Multiple Modalities for Simultaneous Interior Tomography

Overview of attention for article published in PLOS ONE, June 2012
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Title
Towards Omni-Tomography—Grand Fusion of Multiple Modalities for Simultaneous Interior Tomography
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0039700
Pubmed ID
Authors

Ge Wang, Jie Zhang, Hao Gao, Victor Weir, Hengyong Yu, Wenxiang Cong, Xiaochen Xu, Haiou Shen, James Bennett, Mark Furth, Yue Wang, Michael Vannier

Abstract

We recently elevated interior tomography from its origin in computed tomography (CT) to a general tomographic principle, and proved its validity for other tomographic modalities including SPECT, MRI, and others. Here we propose "omni-tomography", a novel concept for the grand fusion of multiple tomographic modalities for simultaneous data acquisition in a region of interest (ROI). Omni-tomography can be instrumental when physiological processes under investigation are multi-dimensional, multi-scale, multi-temporal and multi-parametric. Both preclinical and clinical studies now depend on in vivo tomography, often requiring separate evaluations by different imaging modalities. Over the past decade, two approaches have been used for multimodality fusion: Software based image registration and hybrid scanners such as PET-CT, PET-MRI, and SPECT-CT among others. While there are intrinsic limitations with both approaches, the main obstacle to the seamless fusion of multiple imaging modalities has been the bulkiness of each individual imager and the conflict of their physical (especially spatial) requirements. To address this challenge, omni-tomography is now unveiled as an emerging direction for biomedical imaging and systems biomedicine.

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Geographical breakdown

Country Count As %
Denmark 2 4%
United Kingdom 1 2%
United States 1 2%
Brazil 1 2%
Unknown 52 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 28%
Student > Ph. D. Student 12 21%
Professor > Associate Professor 7 12%
Student > Master 7 12%
Professor 3 5%
Other 8 14%
Unknown 4 7%
Readers by discipline Count As %
Engineering 15 26%
Physics and Astronomy 11 19%
Medicine and Dentistry 8 14%
Computer Science 6 11%
Agricultural and Biological Sciences 5 9%
Other 6 11%
Unknown 6 11%