Accelerating advanced mri reconstructions on gpus

Stone, Samuel S.; Haldar, Justin P.; Tsao, Stephanie C.; Hwu, Wen-mei W.; Liang, Zhi‐Pei; Sutton, Bradley P.

doi:10.1145/1366230.1366276

Cited by 78 publications

(32 citation statements)

References 26 publications

(35 reference statements)

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“…It was shown that, using 200 iterations, the processing times were around 2 h for this dataset. As most of the postprocessing is dominated by the 3D gridding algorithm, we believe it is possible to speed up each iteration using more efficient algorithms, or even graphical processing units (24).…”

Section: Figmentioning

confidence: 99%

Hybrid prospective and retrospective head motion correction to mitigate cross‐calibration errors

Aksoy

Forman

Straka

et al. 2011

Magnetic Resonance in Med

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Utilization of external motion tracking devices is an emerging technology in head motion correction for MRI. However, cross-calibration between the reference frames of the external tracking device and the MRI scanner can be tedious and remains a challenge in practical applications. In this study, we present two hybrid methods, which both combine prospective, optical-based motion correction with retrospective entropy-based autofocusing in order to remove residual motion artifacts. Our results revealed that in the presence of cross-calibration errors between the optical tracking device and the MR scanner, application of retrospective correction on prospectively corrected data significantly improves image quality. As a result of this hybrid prospective & retrospective motion correction approach, the requirement for a high-quality calibration scan can be significantly relaxed, even to the extent that it is possible to perform external prospective motion tracking without any prior cross-calibration step if a crude approximation of cross-calibration matrix exists. Moreover, the motion tracking system, which is used to reduce the dimensionality of the autofocusing problem, benefits the retrospective approach at the same time.

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Section: Figmentioning

confidence: 99%

Hybrid prospective and retrospective head motion correction to mitigate cross‐calibration errors

Aksoy

Forman

Straka

et al. 2011

Magnetic Resonance in Med

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“…Since the advent of general purpose usage of GPUs, medical computing [6], [7], energy sciences [8], image/video processing [9], [10], finance [11] and many other problems in different areas have been ported on to GPU platforms in order to gain performance. In this paper we propose an implementation of Lempel-Ziv-Storer-Szymanski(LZSS) lossless data compression on NVIDIA GPUs (CULZSS).…”

Section: Introductionmentioning

confidence: 99%

CULZSS: LZSS Lossless Data Compression on CUDA

Özsoy

Swany

2011

2011 IEEE International Conference on Cluster Computing

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Increasing needs in efficient storage management and better utilization of network bandwidth with less data transfer lead the computing community to consider data compression as a solution. However, compression introduces an extra overhead and performance can suffer. The key elements in making the decision to use compression are execution time and compression ratio. Due to negative performance impact, compression is often neglected.General purpose computing on graphic processing units (GPUs) introduces new opportunities where parallelism is available. Our work targets the use of the opportunities in GPU based systems by exploiting the parallelism in compression algorithms. In this paper we present an implementation of Lempel-Ziv-Storer-Szymanski(LZSS) lossless data compression algorithm by using NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework. Our implementation of the LZSS algorithm on GPUs significantly improves the performance of the compression process compared to CPU based implementation without any loss in compression ratio which can support GPU based clusters to solve bandwidth problems. Our system outperforms the serial CPU LZSS implementation by up to 18x, the parallel threaded version up to 3x and the BZIP2 program by up to 6x in terms of compression time, showing the promise of CUDA systems in lossless data compression. To give the programmers an easy to use tool, our work also provides an API for in memory compression without the need for reading from and writing to files, in addition to the version involving I/O.

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“…Therefore, dedicated hardware solutions have been developed in the past [1,2] in order to process images in real-time. However, with the overwhelming development of graphics processing units (GPUs) in the last decade also graphics cards became a serious alternative and were consequently deployed as accelerators for image processing [3].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Efficient Mapping of Multiresolution Image Filtering Algorithms on Graphics Processors

Membarth

Hannig

Dutta

et al. 2009

Lecture Notes in Computer Science

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Abstract. In the last decade, there has been a dramatic growth in research and development of massively parallel commodity graphics hardware both in academia and industry. Graphics card architectures provide an optimal platform for parallel execution of many number crunching loop programs from fields like image processing, linear algebra, etc. However, it is hard to efficiently map such algorithms to the graphics hardware even with detailed insight into the architecture. This paper presents a multiresolution image processing algorithm and shows the efficient mapping of this type of algorithms to the graphics hardware. Furthermore, the impact of execution configuration is illustrated and a method is proposed to determine the best configuration offline in order to use it at run-time. Using CUDA as programming model, it is demonstrated that the image processing algorithm is significantly accelerated and that a speedup of up to 33x can be achieved on NVIDIA's Tesla C870 compared to a parallelized implementation on a Xeon Quad Core.

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Accelerating advanced mri reconstructions on gpus

Cited by 78 publications

References 26 publications

Hybrid prospective and retrospective head motion correction to mitigate cross‐calibration errors

Hybrid prospective and retrospective head motion correction to mitigate cross‐calibration errors

CULZSS: LZSS Lossless Data Compression on CUDA

Efficient Mapping of Multiresolution Image Filtering Algorithms on Graphics Processors

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