Mr. Thomas K. Schiwietz Profile

Thomas K. Schiwietz

at Siemens Corporate Research

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Proceedings Article | 16 March 2007 Paper

A fast and high-quality cone beam reconstruction pipeline using the GPU

Thomas Schiwietz, Supratik Bose, Jonathan Maltz, Rüdiger Westermann

Proceedings Volume 6510, 65105H (2007) https://doi.org/10.1117/12.707598

KEYWORDS: Image filtering, Reconstruction algorithms, Linear filtering, Visualization, Digital filtering, Nonlinear filtering, Computer programming, Image resolution, Graphics processing units, Image quality

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Cone beam scanners have evolved rapidly in the past years. Increasing sampling resolution of the projection images and the desire to reconstruct high resolution output volumes increases both the memory consumption and the processing time considerably. In order to keep the processing time down new strategies for memory management are required as well as new algorithmic implementations of the reconstruction pipeline. In this paper, we present a fast and high-quality cone beam reconstruction pipeline using the Graphics Processing Unit (GPU). This pipeline includes the backprojection process and also pre-filtering and post-filtering stages. In particular, we focus on a subset of five stages, but more stages can be integrated easily. In the pre-filtering stage, we first reduce the amount of noise in the acquired projection images by a non-linear curvature-based smoothing algorithm. Then, we apply a high-pass filter as required by the inverse Radon transform. Next, the backprojection pass reconstructs a raw 3D volume. In post-processing, we first filter the volume by a ring artifact removal. Then, we remove cupping artifacts by our novel uniformity correction algorithm. We present the algorithm in detail. In order to execute the pipeline as quickly as possible we take advantage of GPUs that have proven to be very fast parallel processors for numerical problems. Unfortunately, both the projection images and the reconstruction volume are too large to fit into 512 MB of GPU memory. Therefore, we present an efficient memory management strategy that minimizes the bus transfer between main memory and GPU memory. Our results show a 4 times performance gain over a highly optimized CPU implementation using SSE2/3 commands. At the same time, the image quality is comparable to the CPU results with an average per pixel difference of 10^-5.

Proceedings Article | 2 March 2006 Paper

MR image reconstruction using the GPU

Thomas Schiwietz, Ti-chiun Chang, Peter Speier, Rüdiger Westermann

Proceedings Volume 6142, 61423T (2006) https://doi.org/10.1117/12.652223

KEYWORDS: Magnetic resonance imaging, Image restoration, Reconstruction algorithms, Visualization, Image quality, Computer programming, Fourier transforms, Surgery, Nickel, Image acquisition

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Magnetic resonance (MR) image reconstruction has reached a bottleneck where further speed improvement from the algorithmic perspective is difficult. However, some clinical practices such as real-time surgery monitoring demand faster reconstruction than what is currently available. For such dynamic imaging applications, radial sampling in k-space (i.e. projection acquisition) recently revives due to fast image acquisition, relatively good signal-to-noise ratio, and better resistance to motion artifacts, as compared with the conventional Cartesian scan. Concurrently, using the graphic processing unit (GPU) to improve algorithm performance has become increasingly popular. In this paper, an efficient GPU implementation of the fast Fourier transform (FFT) will first be described in detail, since the FFT is an important part of virtually all MR image reconstruction algorithms. Then, we evaluate the speed and image quality for the GPU implementation of two reconstruction algorithms that are suited for projection acquisition. The first algorithm is the look-up table based gridding algorithm. The second one is the filtered backprojection method which is widely used in computed tomography. Our results show that the GPU implementation is up to 100 times faster than a conventional CPU implementation with comparable image quality.

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