Distributed and hardware accelerated computing for clinical medical imaging using proton computed tomography (pCT)

Karonis, Nicholas T.; Duffin, Kirk L.; Ordoñez, C.E.; Erdélyi, Béla; Uram, Thomas D.; Olson, Eric C.; Coutrakon, G.; Papka, Michael E.

doi:10.1016/j.jpdc.2013.07.016

Cited by 28 publications

(34 citation statements)

References 27 publications

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“…FDK is the most frequently used algorithm for performing the 3D image reconstruction in the field of cone-beam computed tomography. Karonis et al 2013 [13] have proposed the use of distributed and hardware-accelerated computing methods for achieving fast image reconstruction using pCT(proton computed tomography). Steuwer et al 2013 [14] have presented SkelCL.…”

Section: Related Workmentioning

confidence: 99%

A comparative study – medical imaging techniques: gpu-based or parallelized?

2017

IJLTET

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Section: Related Workmentioning

confidence: 99%

A comparative study – medical imaging techniques: gpu-based or parallelized?

2017

IJLTET

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“…For example, as illustrated in a recent study by Karonis et al [34], the CARP method has shown great applicability in largescale settings if the number of equations is significantly larger (up to two orders of magnitude) than the number of voxels, because then the overhead from combining the current solutions of all threads after each iteration is significantly less costly.…”

Section: Platformmentioning

confidence: 99%

“…The importance of utilizing the memory hierarchy for data locality makes it more difficult to choose the most appropriate algebraic reconstruction algorithm for a given setup. This work rigorously investigates the multi-core computational performance of block algebraic iterative reconstruction methods using the OpenMP framework; see [34] for an MPI-based application on a heterogeneous computing cluster.…”

Section: Introduction Discretizations Of Tomographic Imaging Problemmentioning

confidence: 99%

Multicore Performance of Block Algebraic Iterative Reconstruction Methods

Sørensen¹,

Hansen²

2014

SIAM J. Sci. Comput.

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Abstract. Algebraic iterative methods are routinely used for solving the ill-posed sparse linear systems arising in tomographic image reconstruction. Here we consider the Algebraic Reconstruction Techniques (ART) and the Simultaneous Iterative Reconstruction Techniques (SIRT), both of which rely on semi-convergence. Block versions of these methods, based on a partitioning of the linear system, are able to combine the fast semi-convergence of ART with the better multi-core properties of SIRT. These block methods separate into two classes: those that, in each iteration, access the blocks in a sequential manner, and those that compute a result for each block in parallel and then combine these results before the next iteration. The goal of this work is to demonstrate which block methods are best suited for implementation on modern multi-core computers. To compare the performance of the different block methods we use a fixed relaxation parameter in each method, namely, the one that leads to the fastest semi-convergence. Computational results show that for multi-core computers, the sequential approach is preferable.

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“…Karonis et al [22] have developed parallel MPI codes that enable large, two billion proton history, images to be reconstructed within ten minutes on a dedicated HPC cluster. A detailed explanation of each pCT reconstruction phase is also presented their work.…”

Section: Proton Computed Tomographymentioning

confidence: 99%

Network Health and e-Science in Public Clouds

Chard

Bubendorfer

2014

2014 IEEE 10th International Conference on E-Science

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Abstract-Commercial cloud providers are increasingly offering high performance and GPU-enabled resources capable of facilitating e-Science applications. However, the limitations of a public cloud's internal network performance are well documented and can lead to the decision to use dedicated infrastructure over cloud resources for scientific applications. This paper explores the potential for improvement in the performance of eScience applications on public clouds through the examination of the network in more detail. We introduce health indicators and evaluate various tomographic techniques for their ability to infer information regarding the network connection between instances. We also propose and formulate a set of health markers and health metrics to efficiently assess the network over time in order to make informed deployment decisions. Finally, we evaluate our work through a real-world medical image reconstruction application.

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Distributed and hardware accelerated computing for clinical medical imaging using proton computed tomography (pCT)

Cited by 28 publications

References 27 publications

A comparative study – medical imaging techniques: gpu-based or parallelized?

A comparative study – medical imaging techniques: gpu-based or parallelized?

Multicore Performance of Block Algebraic Iterative Reconstruction Methods

Network Health and e-Science in Public Clouds

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