Prof. Klaus D. Mueller
Associate Professor at Stony Brook Univ
SPIE Involvement:
Author | Instructor
Publications (12)

Proceedings Article | 10 September 2019 Paper
Proc. SPIE. 11113, Developments in X-Ray Tomography XII
KEYWORDS: X-ray computed tomography, Data modeling, Denoising, Germanium, Computer programming, Computer simulations, Medical imaging, Gallium nitride, Neural networks, Computed tomography

Proceedings Article | 28 May 2019 Paper
Proc. SPIE. 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
KEYWORDS: Image processing, Medical imaging, Computed tomography

Proceedings Article | 5 April 2016 Paper
Proc. SPIE. 9783, Medical Imaging 2016: Physics of Medical Imaging
KEYWORDS: Optical imaging, Electronics, X-ray computed tomography, Defect detection, Surgery, Ultrasonography, Data hiding, Metals, Magnetic resonance imaging, Image restoration, Medical imaging, Image quality, Computed tomography, Reconstruction algorithms, Spine, Printed circuit board testing, Single photon emission computed tomography

Proceedings Article | 31 March 2016 Paper
Proc. SPIE. 9783, Medical Imaging 2016: Physics of Medical Imaging
KEYWORDS: X-ray computed tomography, CT reconstruction, Data modeling, Signal attenuation, Metals, Image segmentation, Computer vision technology, Machine vision, Computed tomography, Particle swarm optimization, Reconstruction algorithms, Data analysis

Proceedings Article | 6 March 2013 Paper
Proc. SPIE. 8668, Medical Imaging 2013: Physics of Medical Imaging
KEYWORDS: Visual analytics, X-ray computed tomography, CT reconstruction, Visualization, Image processing, X-rays, Medical imaging, Computed tomography, Reconstruction algorithms, Computer security

Showing 5 of 12 publications
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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