A review of treatment planning for precision image-guided photon beam pre-clinical animal radiation studies

Frank Verhaegen*, Stefan van Hoof, Patrick V Granton, Daniela Trani

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

57 Citations (Web of Science)


Recently, precision irradiators integrated with a high-resolution CT imaging device became available for pre-clinical studies. These research platforms offer significant advantages over older generations of animal irradiators in terms of precision and accuracy of image-guided radiation targeting. These platforms are expected to play a significant role in defining experiments that will allow translation of research findings to the human clinical setting. In the field of radiotherapy, but also others such as neurology, the platforms create unique opportunities to explore e.g. the synergy between radiation and drugs or other agents. To fully exploit the advantages of this new technology, accurate methods are needed to plan the irradiation and to calculate the three-dimensional radiation dose distribution in the specimen. To this end, dedicated treatment planning systems are needed. In this review we will discuss specific issues for precision irradiation of small animals, we will describe the workflow of animal treatment planning, and we will examine several dose calculation algorithms (factorization, superposition-convolution, Monte Carlo simulation) used for animal irradiation with kilovolt photon beams. Issues such as dose reporting methods, photon scatter, tissue segmentation and motion will also be discussed briefly.

Original languageEnglish
Pages (from-to)323-34
Number of pages12
JournalZeitschrift für Medizinische Physik
Issue number4
Publication statusPublished - Dec 2014


  • Animals
  • Computer Simulation
  • Models, Biological
  • Patient Positioning
  • Photons
  • Radiometry
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Setup Errors
  • Radiotherapy, Conformal
  • Radiotherapy, Image-Guided
  • Reproducibility of Results
  • Sensitivity and Specificity

Cite this