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Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging.

Reimund Bayerlein1,2, Vivek Swarnakar2, Aaron Selfridge1

  • 1Department of Biomedical Engineering, University of California Davis, Davis, CA, United States of America.

Biomedical Physics & Engineering Express
|June 14, 2024
PubMed
Summary
This summary is machine-generated.

Cloud serverless computing significantly accelerates Monte Carlo (MC) simulations for nuclear medicine imaging. This approach offers a substantial speedup over traditional servers, making complex simulations more efficient for research.

Keywords:
AWS lambdaGATEMonte-Carlo simulationSimSETcloud-based computingcomputational efficiencynuclear medicine imaging

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Area of Science:

  • Medical Imaging
  • Computational Science
  • Cloud Computing

Background:

  • Monte Carlo (MC) simulations are computationally intensive for nuclear medicine imaging tasks.
  • Traditional multi-core servers struggle with the high computational burden of MC simulations.
  • Cloud computing offers potential for parallelization and acceleration of these simulations.

Purpose of the Study:

  • To investigate the efficacy of cloud-based serverless computing for accelerating MC simulations in nuclear medicine imaging.
  • To evaluate the performance of Amazon Web Service (AWS) Lambda for MC simulations of radioactive decays in positron emission tomography (PET).
  • To compare the computational performance of AWS Lambda against a modern on-premises multi-thread reconstruction server.

Main Methods:

  • Utilized AWS Lambda serverless platform to run MC simulations for PET imaging.
  • Deployed SimSET and GATE MC simulation frameworks within the AWS environment using containerized applications.
  • Orchestrated parallel runs on concurrent AWS Lambda instances and processed results via AWS Simple Storage Service (S3).
  • Measured execution times for simulations involving 10^5 to 2x10^10 simulated decays.

Main Results:

  • Cloud-based SimSET implementation outperformed local server computations by over an order of magnitude.
  • Simulating 10^9 decays with SimSET on AWS took approximately 5 minutes at a cost of $10.
  • The GATE implementation resulted in larger output files, highlighting internet connection speed as a data transfer bottleneck.
  • GATE simulations required over 100 minutes and incurred higher costs compared to SimSET.

Conclusions:

  • Cloud-based serverless computing architecture can significantly enhance processing times and workflow efficiency in medical imaging research.
  • SimSET on AWS Lambda provides a cost-effective and rapid solution for MC simulations.
  • Further research into optimized configurations and computational methods can unlock additional benefits of cloud-based architectures.