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The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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Related Experiment Video

Updated: May 16, 2026

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
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Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

VQ-DoseNet: A vector quantized model for stochastic radiotherapy dose prediction.

Dong Yang1, Yao Xu1, Zihan Sun2

  • 1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.

Medical Image Analysis
|May 14, 2026
PubMed
Summary
This summary is machine-generated.

We developed VQ-DoseNet, a novel stochastic model for radiotherapy dose prediction. This approach enhances treatment planning by generating accurate, variable dose distributions, improving clinical efficiency and patient care.

Keywords:
Deep learningDose predictionKullback-Leibler divergenceRadiotherapyVector quantization

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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

Area of Science:

  • Medical Physics
  • Artificial Intelligence in Medicine
  • Radiotherapy Research

Background:

  • Radiotherapy treatment planning is complex and time-consuming.
  • Current deep learning dose prediction models lack variability, limiting clinical application.
  • Stochasticity is essential for capturing dose distribution variations in radiotherapy.

Purpose of the Study:

  • To introduce stochasticity into deep learning-based radiotherapy dose prediction.
  • To develop a novel vector quantized model (VQ-DoseNet) for probabilistic dose prediction.
  • To improve the efficiency and clinical acceptance of radiotherapy treatment planning.

Main Methods:

  • Proposed a novel vector quantized model (VQ-DoseNet) for stochastic dose prediction.
  • Incorporated input feature perturbation to generate multiple plausible dose distributions.
  • Evaluated model performance against state-of-the-art methods on in-house and OpenKBP datasets.

Main Results:

  • VQ-DoseNet achieved a mean absolute error (MAE) of 0.106 Gy on an in-house dataset.
  • Reported dose and DVH scores of 3.608 ± 1.267 Gy and 1.329 ± 1.934 Gy on the OpenKBP dataset.
  • Generated stochastic dose distributions consistent with ground truth and within clinical constraints.

Conclusions:

  • VQ-DoseNet successfully introduces stochasticity into dose prediction, maintaining high accuracy.
  • The model generates clinically relevant, variable dose distributions, enhancing treatment planning.
  • This approach offers a promising method for improving radiotherapy efficiency and patient outcomes.