# Monte Carlo modelling for medical staff dosimetry:Summary of the submitted problem solutions.

Lara Struelens, jean-marc bordy, Frank Schultz

► To cite this version:

Lara Struelens, jean-marc bordy, Frank Schultz. Monte Carlo modelling for medical staff dosimetry:Summary of the submitted problem solutions. International Workshop on Uncertainty Assessment in Computational Dosimetry, A Comparison of Approaches, Oct 2007, Bologne, Italy. cea-05461237

HAL Id: cea-05461237

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Submitted on 3 Feb 2026

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Medical Staff Dosimetry

## Summary of the Submitted Problem Solutions
Medical Staff Dosimetry

Restricted Problem – CONRAD WP4 & WP7

Jean-Marc Bordy - Design of a Realistic Calibration
Field for Diagnostic Radiology

Frank Schultz - Simulation of an Interventional
Cardiac Catheterization
Procedure

Lara Struelens - Summary of the Submitted
Problem Solutions

Lara Struelens - Medical staff dosimetry:
Summary of the Submitted Problem Solutions
# Outline

## Problem 1:

A comparison of calculated results and experimental data concerning the characterization of the scattered field

- Belgium (MCNPX)

- The Netherlands (MCNP4C)

- Slovakia (MCNP4C)

- Portugal (MCNPX)

- Denmark (Penelope 2006)

## Problem 2:

A comparison of calculated results concerning the dose to the interventional cardiologist

- Belgium (MCNPX)

- The Netherlands (MCNP4C)

- Portugal (MCNPX)

- Denmark (Penelope 2006)
**Problem 1: A comparison of calculated results and experimental data concerning the characterization of the scattered field**

<figure><img src="image_2.png" /></figure>

* Calculate energy spectra at P1 and P2

* Calculate ‘dosemeter’ readings (P3) per unit of dose-area-product (DAP)

a. Free-in-air

b. In front of ‘cardiologist’ phantom

* Comparison with experiments: intercomparison of APDs at CEA and IRSN (CONRAD-WP7)
## Results: Energy spectra at P1

<figure><img src="image_3.png" /></figure>

### Average energy:

* A: 47.97 keV ± 0.06%

* B: 47.26 keV ± 0.25%

* C: 47.26 keV ± 1.08%

* D: 48.23 keV ± 0.04%

* E: 47.96 keV ± 0.18%

Standard deviation: 0.90%

* Cross section libraries

* Source spectrum: Xcomp5r ↔ IPEM78
source spectrum generator

<figure><img src="image_4.png" /></figure>

• A: Xcomp5r

• B: IPEM78

• C: Xcomp5r

• D: Xcomp5r

• E: Xcomp5r

Source spectrum: Xcomp5r ↔ IPEM78
# Results: Energy spectra at P2

<figure><img src="image_5.png" /></figure>

## Average energy:

* A: 45.10 keV ± 0.07%

* B: 44.10 keV ± 0.55%

* C: 45.15 keV ± 1.50%

* D: 45.40 keV ± 0.07%

* E: 44.53 keV ± 0.51%

Standard deviation: 1.18%

* Cross section libraries

* Source spectrum: Xcomp5r ↔ IPEM78
Results: 'Dosemeter readings' free-in-air

<figure><img src="image_6.png" /></figure>

Lara Struelens - Medical staff dosimetry:
Summary of the Submitted Problem Solutions

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
conversion coefficients

$$K = \int \bar{\Phi}_E \cdot E \cdot \left( \frac{\mu_{tr}}{\rho} \right) \cdot dE$$

<figure><img src="image_9.png" /></figure>

Lara Struelens - Medical staff dosimetry:
Summary of the Submitted Problem Solutions
## Results: 'Dosemeter readings' in front of slab phantom

<figure><img src="image_10.png" /></figure>

Lara Struelens - Medical staff dosimetry:
Summary of the Submitted Problem Solutions

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>
Comparison with experiments

<figure><img src="image_13.png" /></figure>

APD intercomparison with same
ISO slab geometry.

CEA: pulsed radiation

square field

IRSN: continuous radiation

round field
# Comparison with experiments

Measurements:

* Energy spectra in point 3 free-in-air
  (CdTe XR-100T spectrometer)

→ compared to calculated spectra in point 3 (A, C and E)

* Air kerma $K_{air}$ at point 3 free-in-air
  (ionisation chamber)

→ compared to calculated values in point 3 (A, B, C, D and E)
Comparison with experiments

<figure><img src="image_14.png" /></figure>

Lara Struelens - Medical staff dosimetry:
Summary of the Submitted Problem Solutions

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>
# Comparison with experiments

## Measurements:

* Energy spectra in point 3 free-in-air
  (CdTe XR-100T spectrometer)

→ compared to calculated spectra in point 3 (A, C and E)

* Air kerma $K_{air}$ at point 3 free-in-air
  (ionisation chamber)

→ compared to calculated values in point 3 (A, B, C, D and E)
# Comparison with experiments

* Air kerma $K_{\text{air}}$ at point 3 free-in-air

* Measurements with ionisation chamber

- Pulsed: $15.7 \ \mu\text{Gy/Gycm}^2$ ($k = 2$; uncertainty: 6.2%)

- Continuous: $10.2 \ \mu\text{Gy/Gy.cm}^2$ ($k = 2$; uncertainty: 7.3%)

* Calculations Only results using `mcplib.04p`

- Kerma in tissue $\rightarrow K_{\text{air}} : (\mu_{\text{en}}/\rho)_{\text{air}} / (\mu_{\text{en}}/\rho)_{\text{tissue}}$ (ICRU-44; the complete spectra from institutes was taken into account)

- Average $K_{\text{air}} = 12.3 \ \mu\text{Gy/Gy.cm}^2$ (uncertainty: 1.5 %)

* Systematic error: 22% (pulsed) and -20% (continuous)
# Conclusions problem 1

* Problems were solved using different codes: MCNP4C, MCNPX and Penelope

* The main differences between the calculated results were the *choice of library* and the *choice of dose conversion coefficients*

→ flux tallies are sensitive to angular distributions of secondary particles
⇒ recommended to use the latest set of data tables: mcplib.04p

→ differences were larger than the statistical uncertainties

* Systematic error of ± 20% between experimental and calculated data

→ Where do the differences in experimental data come from?

→ calculations done in a sphere filled with tissue ↔ air kerma measurement

→ Simulation of environment necessary?
# Problem 2: A comparison of calculated results concerning the dose to the interventional cardiologist

A representative clinical practice

<figure><img src="image_17.png" /></figure>

* Calculate 'dosemeter readings' per DAP at waist, chest and neck level

a. Without protective clothing

b. Above and under lead-apron

* Calculate effective dose to the cardiologist per DAP

a. Without protective clothing

b. Above and under lead-apron

Geometry, technical parameters: pres. F. Schultz
# Problem 2: A comparison of calculated results concerning the dose to the interventional cardiologist

Implementation of clinical situation by participant:

* choice of spectrum generator

A, D and E: X-comp5r

B: IPEM78

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

* choice of phantoms

- Patient: mathematical phantom (ADAM, Bodybuilder)

- Staff: B, D and E: ADAM mathematical phantom
  A: MAX voxel phantom

<figure><img src="image_20.png" /></figure>
## Results: without protective clothing (4 participants)

<figure><img src="image_21.png" /></figure>

**Standard deviations:**

* dosemeters

a. Neck: 20%

b. Chest: 13%

* effective dose: 20%

Lara Struelens - Medical staff dosimetry:
Summary of the Submitted Problem Solutions

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>
## Results: without protective clothing: organ doses

<figure><img src="image_24.png" /></figure>

* Different phantoms

* Calculation method for skeleton and Red Bone Marrow (RBM): skeleton mixture, distribution of RBM, correction factors, ...
## Results: with protective clothing (3 participants)

<figure><img src="image_25.png" /></figure>

Lara Struelens - Medical staff dosimetry:
Summary of the Submitted Problem Solutions

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>
# Results: with protective clothing

## differences:

* dosemeters above

a. Neck: 24%

b. Chest: 19%

c. Waist: 11%

* dosemeters under

a. Neck: 32%

b. Chest: 28%

c. Waist: 45%

* effective dose: 47% (only 2 results)

## Ratio $H_p(10) / E$:

* dosemeters **above** apron: average overestimation of E by a factor of 25

minimum: factor 10 (waist dosemeter)

maximum: factor 43 (chest dosemeter)

* dosemeters **under** apron: average underestimation of E by a factor 0.22

minimum: factor 0.35 (chest dosemeter)

maximum: factor 0.07 (waist dosemeter)

⇒ Need of algorithm to combine 2 dosemeters for conservative estimation of E
# Results: with protective clothing

## Example of 2 existing algorithms

1. From a large Belgian study: $E_{est} = 2.25 \times H_p(10)_{thorax, under} + 0.097 \times H_p(10)_{neck, above}$

Results for A: $E_{est} = 77 \text{ nSv/Gy.cm}^2$ and $E_{calc} = 24 \text{ nSv/Gy.cm}^2$
$\text{ratio } E_{est} / E_{calc} = 3.16$ (overestimation of 216%)

Results for B: $E_{est} = 122 \text{ nSv/Gy.cm}^2$ and $E_{calc} = 45 \text{ nSv/Gy.cm}^2$
$\text{ratio } E_{est} / E_{calc} = 2.69$ (overestimation of 169%)

2. From Wambersie: $E_{est} = 1.0 \times H_p(10)_{waist, under} + 0.1 \times H_p(10)_{neck, above}$

Results for A: $E_{est} = 67 \text{ nSv/Gy.cm}^2$ and $E_{calc} = 24 \text{ nSv/Gy.cm}^2$
$\text{ratio } E_{est} / E_{calc} = 2.74$ (overestimation of 174%)

Results for B: $E_{est} = 101 \text{ nSv/Gy.cm}^2$ and $E_{calc} = 45 \text{ nSv/Gy.cm}^2$
$\text{ratio } E_{est} / E_{calc} = 2.22$ (overestimation of 122%)

NOTE: result for only 1 configuration (PA thorax), 1 spectrum, 1 position of cardiologist!!!
# Conclusions problem 2

Monte Carlo modelling for medical staff dosimetry – difficulties

* A realistic clinical situation is a combination of a very large amount of parameters

- Beam projections (PA, AP, LAT, OBL, ...)

- Field sizes (17 x 17 cm²)

- Irradiated parts of the patient (head, thorax, abdomen, ...)

- Energy spectra: combination of tube voltage and filtration
(60, 70 - 100 kVp; 2.0-4.0 mm Al + 0.1,0.2-0.6 mm Cu)

- Position of the worker in respect to the scattered radiation field

* There is a large influence on how a clinical practice is implemented in a calculation problem for the evaluation of the effective dose to the worker

- Larger difference in effective dose than in dosemeter results between participants
  ⇒ Different correlation between dosemeters results and effective dose
  ⇒ Different results between participants when 1 specific algorithm is used
Conclusions problem 2

* Minimise this influence by

→ type of phantom, organ dose calculation

- 1 reference phantom (voxelised) for dose and radiation protection studies

→ data libraries, statistical uncertainties

- most recent libraries

- under lead apron: variance reduction techniques

* There will always remain a large systematic uncertainty between calculation results and reality due to the difference in anatomy between real persons and a reference phantom
Thanks to all participants !!!

Lara Struelens - Medical staff dosimetry:
Summary of the Submitted Problem Solutions