OpenDosimeter: Open Hardware Personal X-ray Dosimeter

TL;DR

OpenDosimeter is presented, an open hardware solution for real-time personal X-ray dose monitoring using a scintillator-based X-ray sensor on a custom board powered by a Raspberry Pi Pico, and enables cost-effective local reproducibility on a global scale.

Abstract

We present OpenDosimeter (www.opendosimeter.org), an open hardware solution for real-time personal X-ray dose monitoring based on a scintillation counter. Using an X-ray sensor assembly (LYSO + SiPM) on a custom board powered by a Raspberry Pi Pico, OpenDosimeter provides real-time feedback (1 Hz), data logging (10 hours), and battery-powered operation. One of the core innovations is that we calibrate the device using $^{241}$Am found in ionization smoke detectors. Specifically, we use the $γ$-emissions to spectrally calibrate the dosimeter, then calculate the effective dose from X-ray exposure by compensating for the scintillator absorption efficiency and applying energy-to-dose coefficients derived from tabulated data in the ICRP 116 publication. We demonstrate that this transparent approach enables real-time dose rate readings with a linear response between 0.1-1000 $μ$Sv/h at $\pm$25% accuracy, tested for energies up to 120 keV. The maximum dose rate readings are limited by pile-up effects when approaching count rate saturation ($\sim$77 kcps at $\sim$13 $μ$s average pulse processing time). The total component cost for making an OpenDosimeter is <\$100, which, combined with its open design (both hardware and software), enables cost-effective local reproducibility on a global scale. Through a student workshop, we also demonstrate its effectiveness as an educational and capacity-building tool. This paper complements the open-source documentation by explaining the underlying technology, the algorithm for dose calculation, and areas for future improvement.

Figures (4)

• Figure 1: Device overview.a, OpenDosimeter custom board as assembled and delivered by a PCB manufacturer. b, X-ray sensor: LYSO crystal mounted on a SiPM. c, SiPM photon detection efficiency overlapping with the LYSO emission spectrum (arbitrary y-axis). d, Signal processing: raw SiPM pulses (blue), amplified signal (yellow), and peak signal amplitude (purple) sampled by the Raspberry Pi Pico (shaded purple); $\sim$13 $\upmu$s typical sampling time. e, Histogram generation from triggered sampling of peak amplitudes whenever the signal exceeds the threshold. f, Fully assembled OpenDosimeter, without (left) and with (right) the case. g, User interface: single-button navigation with short/long presses. h, Component cost breakdown (as of July 2024).
• Figure 2: Calibration and dose calculation.a, Disassembled ionization smoke detector with $^{241}$Am used for spectral calibration b, Raw calibration histogram (light purple; shaded) and moving average (dark purple; solid) showing two peaks. Inset: zoom on the two peaks with the underlying $^{241}$Am $\gamma$-spectrum overlaid (measured with a CdTe spectrometer). The spectral calibration performs a linear fit of the two peaks with the expected $^{241}$Am emission energies. c, LYSO (1 mm thick) spectral X-ray absorption efficiency. d, Per-photon-dose spectral coefficients, derived from ICRP 116 (Table A.1; AP direction) and scaled with the LYSO crystal area (25 mm$^2$). e, Algorithm for calculating the dose rate and accumulated dose.
• Figure 3: Characterization and performance demonstration.a, Output count rate (purple; left) and estimated deadtime (orange; right) as a function of input count rate. b, Output dose rate of the OpenDosimeter (blue) and the RaySafe i3 reference dosimeter (grey) plotted against the reference dose rate (RaySafe X2 survey meter). c, Relative errors in dose rate measurements. d, Demonstration of real-time dose rate performance (blue / grey) and accumulated dose (green). e, Relative response in the angular direction.
• Figure 4: OpenDosimeter as a tool for capacity-building in radiation technology.a, Workshop format spanning seven days, integrating stakeholder visits, learning activities, and design improvements by the student participants. b, Pre- and post-workshop survey results (n=20 and n=12, respectively) showing improvements in knowledge, confidence, and practical experience, scored from 1 (low) to 5 (high). c, Excerpt of reflections from entry and exit surveys highlighting participant motivations and learning outcomes (quotes lightly edited for brevity while preserving language and content). d, Our workshop as a case study on open hardware implementation towards locally sustainable innovation.