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A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Determining the optimal dose size and dosing frequency in pharmacotherapy is crucial for achieving therapeutic effectiveness while minimizing adverse effects. This article explores the methodologies employed in determining these parameters, focusing on their significance and interplay to tailor dosing regimens.Dose Size: Dose size refers to the amount of a drug administered in a single dose. It is determined based on the drug's pharmacodynamics and pharmacokinetics properties and...
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Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
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Analytical modeling and feasibility study of a multi-GPU cloud-based server (MGCS) framework for non-voxel-based dose

J Neylon1, Y Min2, P Kupelian2

  • 1Department of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, CA, 90095, USA. jneylon@mednet.ucla.edu.

International Journal of Computer Assisted Radiology and Surgery
|August 26, 2016
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A multi-GPU cloud-based server framework accelerates radiotherapy dose calculations, proving feasible for online adaptive therapies. This remote computing solution enhances speed and accessibility for advanced treatment planning.

Keywords:
Cloud computingGPUMulti-GPURadiotherapy

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Area of Science:

  • Radiotherapy
  • Computational Physics
  • Cloud Computing

Background:

  • Radiotherapy requires computationally intensive dose calculations.
  • Online adaptive therapies demand faster treatment planning.
  • Existing hardware limitations hinder advanced computational methods.

Purpose of the Study:

  • To present a multi-GPU cloud-based server (MGCS) framework for radiotherapy dose calculations.
  • To explore remote computing for parallelization and acceleration of radiotherapy tasks.
  • To assess the feasibility of MGCS for online adaptive therapies.

Main Methods:

  • Developed an analytical model for MGCS performance prediction and workload distribution.
  • Conducted numerical studies using 14 GPUs across 4 servers (1 Gbps network).
  • Optimized inter-process communication to minimize data transfer and facilitate resource distribution.

Main Results:

  • Analytical predictions closely matched experimental observations (1-5% difference).
  • MGCS performance scaled with the number of GPUs for compute-intensive tasks.
  • Accurate dose computations were reproduced with negligible differences.

Conclusions:

  • A cloud-based computation engine is a feasible solution for rapid dose calculations in advanced radiotherapy.
  • The MGCS framework offers significant acceleration for computationally intensive tasks, outperforming local machines.
  • This approach democratizes access to advanced radiotherapy technology and computational methods for clinics.