# Monte Carlo dosimetry

Source: https://nuclide.cc/technologies/monte-carlo-dosimetry/  
Nuclide record `monte-carlo-dosimetry` (Technology). Data CC BY-NC 4.0, attribute "Data from Nuclide (nuclide.cc)"; commercial use needs a licence.

## TL;DR

Powerful computer simulations trace millions of individual particles of radiation as they fly through a virtual copy of a patient's body, giving the most detailed and realistic estimate of exactly how much radiation dose each organ and tumour actually receives.

## Summary

Monte Carlo (MC) radiation-transport simulation is widely regarded as the reference-standard method for internal dosimetry in nuclear medicine. General-purpose codes such as MCNP6 and dedicated nuclear-medicine codes built on Geant4, principally GATE (Geant4 Application for Tomographic Emission) and GAMOS, simulate the full physics of radioactive decay and the subsequent transport and energy deposition of emitted photons, electrons and (increasingly) alpha particles through a voxelised representation of a patient's anatomy, derived from CT, and their measured or assumed radioactivity distribution, derived from SPECT or PET. Within the MIRD formalism, MC methods are used either to derive tabulated dosimetric quantities (the specific absorbed fraction, and the S-value, or mean absorbed dose per unit accumulated activity) for standard reference phantoms, or to run direct, patient-specific voxel-level simulations.

Validation studies comparing GATE, GAMOS and MCNP6 for reference phantoms and clinical patient datasets have found strong cross-code agreement, typically within about 1-5% for self-organ S-values and within 10% (usually under 5%) for cross-organ absorbed fractions across a range of isotopes including F-18, Lu-177, I-131 and Y-90. Organ-level comparisons between GATE-derived and traditional MIRD/OLINDA S-value dosimetry have shown roughly 2% difference in total organ absorbed dose, though larger discrepancies (tens of per cent) can occur for individual cross-organ pairs. The principal drawback of direct MC simulation is computational cost, historically minutes to hours per patient dataset, which has motivated faster approximations (dose point kernel convolution, voxel S-value convolution) and, more recently, deep-learning surrogates trained on MC-generated dose maps for routine clinical throughput.

## Fields

- Kind: Technology
- Last checked: 2026-09-22
- Also known as: GATE; MCNP; voxel S-values
- Tags: dosimetry; simulation; physics
- Principle: A computer simulates the random path of millions of individual emitted particles as they travel and deposit energy through a voxel-by-voxel digital copy of the patient built from CT (anatomy) and SPECT or PET (activity distribution), tracking enough particle histories that the statistical average of where the energy lands closely approximates the true physical radiation transport.
- Since: GATE toolkit released 2004; validated for clinical-scale nuclear medicine dosimetry from the mid-2000s onward

## Sources

- Advancing internal dosimetry in personalised nuclear medicine: MCNP6/GATE/GAMOS comparison (PMC 2025): https://pmc.ncbi.nlm.nih.gov/articles/PMC12655809/
- Internal dosimetry with the Monte Carlo code GATE: validation against ICRP/ICRU reference phantom (Phys Med Biol 2017): https://pubmed.ncbi.nlm.nih.gov/28182580/
- Preclinical voxel-based dosimetry in theranostics: a review (PMC 2020): https://pmc.ncbi.nlm.nih.gov/articles/PMC7198676/
- Development and validation of RAPID, a Geant4-based patient-specific voxel dosimetry tool (PMC 2018): https://pmc.ncbi.nlm.nih.gov/articles/PMC5963670/

## Connected records

- technologies: [AI in nuclear medicine](https://nuclide.cc/technologies/ai-in-nuclear-medicine/), [Quantitative SPECT](https://nuclide.cc/technologies/quantitative-spect/), [Radioligand dosimetry](https://nuclide.cc/technologies/radioligand-dosimetry/), [SPECT dosimetry workflow for radionuclide therapy](https://nuclide.cc/technologies/spect-dosimetry-workflow/)
- isotopes: [Fluorine-18](https://nuclide.cc/isotopes/f-18/), [Iodine-131](https://nuclide.cc/isotopes/i-131/), [Lutetium-177](https://nuclide.cc/isotopes/lu-177/), [Yttrium-90](https://nuclide.cc/isotopes/y-90/)

---
JSON: https://nuclide.cc/api/v1/entities/monte-carlo-dosimetry.json