Dr. Wei Xu
Assistant Scientist at
SPIE Involvement:
Author | Instructor
Publications (3)

PROCEEDINGS ARTICLE | September 7, 2017
Proc. SPIE. 10389, X-Ray Nanoimaging: Instruments and Methods III
KEYWORDS: Light sources, X-rays, Fluorescence spectroscopy, Nanoprobes, Synchrotrons, Hard x-rays, X-ray fluorescence spectroscopy

PROCEEDINGS ARTICLE | February 3, 2014
Proc. SPIE. 9017, Visualization and Data Analysis 2014
KEYWORDS: Visualization, Signal attenuation, Spectroscopy, X-rays, Copper, X-ray microscopy, Imaging spectroscopy, X-ray imaging, Hard x-rays, Absorption

PROCEEDINGS ARTICLE | February 24, 2010
Proc. SPIE. 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling
KEYWORDS: Signal to noise ratio, Human-machine interfaces, Switches, CT reconstruction, Visualization, Clouds, Computer science, Computed tomography, Reconstruction algorithms, Computer security

Course Instructor
SC829: MIC-GPU: High-Performance Computing for Medical Imaging on Programmable Graphics Hardware (GPU)
Advanced graphics boards have become a standard ingredient in any mid-range and high-end PC, and aside from enabling stunning interactive graphics effects in computer games, their rich programmability allows speedups (over CPU-based code) of 1-2 orders of magnitude also in general-purpose computations. This course explains, in gentle ways, how to exploit this powerful computing platform to accelerate various popular medical imaging applications, such as CT, MRI, image processing, and data visualization. It begins by introducing the basic GPU architecture and its programming model, which establishes a solid understanding on how general computing tasks must be structured and implemented on the GPU to achieve the desired high speedups. Next, it examines a number of standard 2D and 3D medical imaging operators, such as filtering, sampling, statistical analysis, transforms, projectors, etc, and explains how these can be effectively accelerated on the GPU. Finally, it puts this all together by describing the full GPU-accelerated computing pipeline for a representative set of medical imaging applications, such as analytical and iterative CT, MRI, image enhancement chains, and volume visualization.
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