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A Highly Accelerated Parallel Multi-GPU based Reconstruction Algorithm for Generating Accurate Relative Stopping Powers

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posted on 2024-11-15, 19:57 authored by Paniz Karbasi, Ritchie Cai, Blake Schultze, Hanh Nguyen, Jones Reed, Patrick Hall, Valentina Giacometti, Vladimir Bashkirov, Robert P Johnson, Nick Karonis, Jeffrey Olafsen, Caesar Ordonez, Keith Schubert, Reinhard W Schulte
Low-dose Proton Computed Tomography (pCT) is an evolving imaging modality that is used in proton therapy planning which addresses the range uncertainty problem. The goal of pCT is generating a 3D map of relative stopping power measurements with high accuracy within clinically required time frames. Generating accurate relative stopping power values within the shortest amount of time is considered a key goal when developing an image reconstruction software. The existing image reconstruction softwares have successfully met this time frame and even exceeded this time goal, but require clusters with hundreds of processors. This paper describes a novel reconstruction technique using two graphics processing unit devices. The proposed reconstruction technique is tested on both simulated and experimental datasets and on two different systems namely Nvidia K40 and P100 graphics processing units from IBM and Cray. The experimental results demonstrate that our proposed reconstruction method meets both the timing and accuracy with the benefit of having reasonable cost and efficient use of power.

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Karbasi, P., Cai, R., Schultze, B., Nguyen, H., Reed, J., Hall, P., Giacometti, V., Bashkirov, V., Johnson, R., Karonis, N., Olafsen, J., Ordonez, C., Schubert, K. & Schulte, R. (2017). A Highly Accelerated Parallel Multi-GPU based Reconstruction Algorithm for Generating Accurate Relative Stopping Powers. 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings (pp. 1738-1741). United States: IEEE.

Parent title

2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings

Language

English

RIS ID

132356

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