Iterative reconstruction for transmission tomography on GPU using Nvidia CUDA

Vintache, Damien; Humbert, Bernard; Brasse, David

doi:10.1016/s1007-0214(10)70002-x

Cited by 16 publications

(15 citation statements)

References 13 publications

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“…GPU and parallel programming techniques have been explored for medical image analyses in positron emission tomography [19], magnetic induction tomography [20], and transmission tomography [21]. Our previous work is the first such approach for pCT [5].…”

Section: Related Workmentioning

confidence: 99%

Scalable pCT Image Reconstruction Delivered as a Cloud Service

Chard

Madduri

Karonis

et al. 2018

IEEE Trans. Cloud Comput.

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We describe a cloud-based medical image reconstruction service designed to meet a real-time and daily demand to reconstruct thousands of images from proton cancer treatment facilities worldwide. Rapid reconstruction of a three-dimensional Proton Computed Tomography (pCT) image can require the transfer of 100GB of data and use of approximately 120 GPU-enabled compute nodes. The nature of proton therapy means that demand for such a service is sporadic and comes from potentially hundreds of clients worldwide. We thus explore the use of a commercial cloud as a scalable and cost-efficient platform for pCT reconstruction. To address the high performance requirements of this application we leverage Amazon Web Services' GPU-enabled cluster resources that are provisioned with high performance networks between nodes. To support episodic demand, we develop an on-demand multi-user provisioning service that can dynamically provision and resize clusters based on image reconstruction requirements, priorities, and wait times. We compare the performance of our pCT reconstruction service running on commercial cloud resources with that of the same application on dedicated local high performance computing resources. We show that we can achieve scalable and on-demand reconstruction of large scale pCT images for simultaneous multi-client requests, processing images in less than ten minutes for less than $10 per image.

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Section: Related Workmentioning

confidence: 99%

Scalable pCT Image Reconstruction Delivered as a Cloud Service

Chard

Madduri

Karonis

et al. 2018

IEEE Trans. Cloud Comput.

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“…Both solved the problem by dividing the data into smaller blocks. Since these early works, the field of GPU-accelerated CT reconstruction has exploded (Noel et al, 2010;Okitsu et al, 2010;Xu et al, 2010a;Vintache et al, 2010). Recent examples include several papers on using multiple GPUs to further accelerate reconstruction (Jang et al, 2009;Liria et al, 2012;Zhang et al, 2012;Zhu et al, 2012), including the incorporation of scatter correction into the reconstruction process (Sisniega et al, 2011), SART based reconstruction combined with motion compensation (Pang et al, 2011), CT reconstruction with OpenCL ) and an up-to-date and thorough comparison of different hardware implementations (CPU, GPU, FPGA and the Cell Broadband Architecture) of FBP (Scherl et al, 2012).…”

Section: Ctmentioning

confidence: 99%

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

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

351

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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“…Many implementations of these algorithms have been ported to GPUs considerably accelerating computations [6,7]. However, less attention has been paid to post-tomographic computations such as granulometry.…”

Section: Practical Interestmentioning

confidence: 99%

DFG implementation on multi GPU cluster with computation-communication overlap

Huet

Boulos²,

Fristot³

et al. 2011

Proceedings of the 2011 Conference on Design &Amp; Architectures for Signal &Amp; Image Processing (DASIP)

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Nowadays, it is possible to build a multi-GPU supercomputer, well suited for implementation of digital signal processing algorithms, for a few thousand dollars. However, to achieve the highest performance with this kind of architecture, the programmer has to focus on inter-processor communications, tasks synchronization . . . In this paper, we propose a design flow allowing an efficient implementation of a Digital Signal Processing (DSP) application specified as a Data Flow Graph (DFG) on a multi GPU computer cluster. We focus particularly on the effective implementation of communications by automating the computation-communication overlap, which can lead to significant speedups as shown in the presented benchmark. The approach is validated on a 3D granulometry application developed for research on materials.

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Iterative reconstruction for transmission tomography on GPU using Nvidia CUDA

Cited by 16 publications

References 13 publications

Scalable pCT Image Reconstruction Delivered as a Cloud Service

Scalable pCT Image Reconstruction Delivered as a Cloud Service

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

DFG implementation on multi GPU cluster with computation-communication overlap

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