MR image reconstruction using the GPU

Schiwietz, Thomas; Chang, Tien‐Chien; Speier, Peter; Westermann, Riidiger

doi:10.1117/12.652223

Cited by 50 publications

(42 citation statements)

References 14 publications

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“…The complete technical details on compact binning can be found elsewhere (Obeid, 2010). Although there exist other GPU gridding implementations [e.g., (Schiwietz et al, 2006)], compact binning was selected for the required 3D gridding as it is well suited to the highly nonuniform sample density of sodium MR imaging performed with the flexTPI pulse sequence. Custom GPU processing kernels were developed for the remaining Linear Phase Correction and 3D Deapodization operations so as to perform the required processing for these two steps solely on the GPU.…”

Section: Methodsmentioning

confidence: 99%

“…High-performance computing with GPUs is an active research area in many areas of science and medicine. GPUs have previously been used for computationally intensive MR imaging applications including image reconstruction (Stone et al, 2008;Roujol et al, 2009), radio frequency pulse design (Deng et al, 2001), gridding (Schiwietz et al, 2006), image alignment (Huang et al, 2011), and more. This work adds to these results the use of a GPU to accelerate the most computationally intensive parts of computing a sodium bioscale.…”

Section: Introductionmentioning

confidence: 99%

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Rapid computation of sodium bioscales using gpu‐accelerated image reconstruction

Atkinson¹,

Li²,

Obeid³

et al. 2013

Int J Imaging Syst Tech

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Quantitative sodium magnetic resonance imaging permits noninvasive measurement of the tissue sodium concentration (TSC) bioscale in the brain. Computing the TSC bioscale requires reconstructing and combining multiple datasets acquired with a nonCartesian acquisition that highly oversamples the center of k-space. Even with an optimized implementation of the algorithm to compute TSC, the overall processing time exceeds the time required to collect data from the human subject. Such a mismatch presents a challenge for sustained sodium imaging to avoid a growing data backlog and provide timely results. The most computationally intensive portions of the TSC calculation have been identified and accelerated using a consumer graphics processing unit (GPU) in addition to a conventional central processing unit (CPU). A recently developed data organization technique called Compact Binning was used along with several existing algorithmic techniques to maximize the scalability and performance of these computationally intensive operations. The resulting GPU1CPU TSC bioscale calculation is more than 15 times faster than a CPU-only implementation when processing 256 3 256 3 256 data and 2.4 times faster when processing 128 3 128 3 128 data. This eliminates the possibility of a data backlog for quantitative sodium imaging. The accelerated quantification technique is suitable for general three-dimensional non-Cartesian acquisitions and may enable more sophisticated imaging techniques that acquire even more data to be used for quantitative sodium imaging.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid computation of sodium bioscales using gpu‐accelerated image reconstruction

Atkinson¹,

Li²,

Obeid³

et al. 2013

Int J Imaging Syst Tech

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“…Recently, image processing using the GPU of a computer has become more popular as the computational performance of GPUs has surpassed that of the CPU with the advent of powerful GPUs for computer games (Eklund et al, 2013). One report showed that the use of the GPU over the CPU accelerated reconstruction with the FBP algorithm by up to 100 times (Schiwietz et al, 2006). The average amount of time spent preprocessing, reconstructing and postprocessing images was recorded for each of the four programs (Table 1).…”

Section: Software Parameters For Image Calibrations and Artifact Removalmentioning

confidence: 99%

Assessment of freeware programs for the reconstruction of tomography datasets obtained with a monochromatic synchrotron-based X-ray source

Wolkowski

Snead

Wesolowski

et al. 2015

J Synchrotron Radiat

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Synchrotron-based in-line phase-contrast computed tomography (PC-CT) allows soft tissue to be imaged with sub-gross resolution and has potential to be used as a diagnostic tool. The reconstruction and processing of in-line PC-CT datasets is a computationally demanding task; thus, an efficient and user-friendly software program is desirable. Four freeware programs (NRecon, PITRE, H-PITRE and Athabasca Recon) were compared for the availability of features such as dark-and flat-field calibration, beam power normalization, ring artifact removal, and alignment tools for optimizing image quality. An in-line PC-CT projection dataset (3751 projections, 180 rotation, 10.13 mm Â 0.54 mm) was collected from a formalin-fixed canine prostate at the Biomedical Imaging and Therapy Bending Magnet (BMIT-BM) beamline of the Canadian Light Source. This dataset was processed with each of the four software programs and usability of the program was evaluated. Efficiency was assessed by how each program maximized computer processing power during computation. Athabasca Recon had the least-efficient memory usage, least user-friendly interface, and lacked a ring artifact removal feature. NRecon, PITRE and H-PITRE produced similar quality images, but the Athabasca Recon reconstruction suffered from the lack of a native ring remover algorithm. The 64-bit version of NRecon uses GPU (graphics processor unit) memory for accelerated processing and is userfriendly, but does not provide necessary parameters for in-line PC-CT data, such as dark-field and flat-field correction and beam power normalization. PITRE has many helpful features and tools, but lacks a comprehensive user manual and help section. H-PITRE is a condensed version of PITRE and maximizes computer memory for efficiency. To conclude, NRecon has fewer imaging processing tools than PITRE and H-PITRE, but is ideal for less experienced users due to a simple user interface. Based on the quality of reconstructed images, efficient use of computer memory and parameter availability, H-PITRE was the preferred of the four programs compared.

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“…Reconstruction by gridding, i.e. interpolation in k-space from a non-Cartesian grid to a Cartesian one followed by the standard inverse FFT, was proposed using the GPU by Schiwietz et al (2006) and further explored by Sorensen et al (2008). Interpolation of the k-space samples can be performed in parallel through a radial point approach or a Cartesian point approach, both having advantages and disadvantages for a GPU implementation.…”

Section: Mrimentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

344

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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MR image reconstruction using the GPU

Cited by 50 publications

References 14 publications

Rapid computation of sodium bioscales using gpu‐accelerated image reconstruction

Rapid computation of sodium bioscales using gpu‐accelerated image reconstruction

Assessment of freeware programs for the reconstruction of tomography datasets obtained with a monochromatic synchrotron-based X-ray source

Medical image processing on the GPU – Past, present and future

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