The impact of mass density variations on an electron Monte Carlo algorithm for radiotherapy dose calculations

Multi-Institutional Audit of FLASH and Conventional Dosimetry With a 3D Printed Anatomically Realistic Mouse Phantom

PURPOSE

We conducted a multi-institutional dosimetric audit between FLASH and conventional dose rate (CONV) electron irradiations by using an anatomically realistic 3-dimensional (3D) printed mouse phantom.

METHODS AND MATERIALS

A computed tomography (CT) scan of a live mouse was used to create a 3D model of bony anatomy, lungs, and soft tissue. A dual-nozzle 3D printer was used to print the mouse phantom using acrylonitrile butadiene styrene (∼1.02 g/cm3) and polylactic acid (∼1.24 g/cm3) simultaneously to simulate soft tissue and bone densities, respectively. The lungs were printed separately using lightweight polylactic acid (∼0.64 g/cm3). Hounsfield units (HU), densities, and print-to-print stability of the phantoms were assessed. Three institutions were each provided a phantom and each institution performed 2 replicates of irradiations at selected anatomic regions. The average dose difference between FLASH and CONV dose distributions and deviation from the prescribed dose were measured with radiochromic film.

RESULTS

Compared with the reference CT scan, CT scans of the phantom demonstrated mass density differences of 0.10 g/cm3 for bone, 0.12 g/cm3 for lung, and 0.03 g/cm3 for soft tissue regions. Differences in HU between phantoms were <10 HU for soft tissue and bone, with lung showing the most variation (54 HU), but with minimal effect on dose distribution (<0.5%). Mean differences between FLASH and CONV decreased from the first to the second replicate (4.3%-1.2%), and differences from the prescribed dose decreased for both CONV (3.6%-2.5%) and FLASH (6.4%-2.7%). Total dose accuracy suggests consistent pulse dose and pulse number, although these were not specifically assessed. Positioning variability was observed, likely due to the absence of robust positioning aids or image guidance.

CONCLUSIONS

This study marks the first dosimetric audit for FLASH using a nonhomogeneous phantom, challenging conventional calibration practices reliant on homogeneous phantoms. The comparison protocol offers a framework for credentialing multi-institutional studies in FLASH preclinical research to enhance reproducibility of biologic findings.

HU-based material conversion for BNCT accurate dose estimation

NeuMANTA is a new generation boron neutron capture therapy (BNCT)-specific treatment planning system developed by the Neuboron Medical Group and upgraded to an important feature, a Hounsfield unit (HU)-based material conversion algorithm. The range of HU values was refined to 96 specific groups and established corresponding to tissue information. The elemental compositions and mass densities have an important effect on the calculated dose distribution. The region of interest defined in the treatment plan can be converted into multiple material compositions based on HU values or assigned specified single material composition in NeuMANTA. Different material compositions may cause normal tissue maximum dose rates to differ by more than 10% in biologically equivalent doses and to differ by up to 6% in physically absorbed doses. Although the tumor has a lower proportion of BNCT background dose, the material composition difference may affect the minimum dose of biologically equivalent dose and physically absorbed dose by more than 3%. In addition, the difference in material composition could lead to a change in neutron moderation as well as scattering. Therefore, the material composition has a significant impact on the assessment of normal tissue side effects and tumor control probability. It is essential for accurate dose estimation in BNCT.

Physical density estimations of single- and dual-energy CT using material-based forward projection algorithm: a simulation study

OBJECTIVES

This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully simulation-based method using a material-based forward projection algorithm (MBFPA).

METHODS

We used biological tissues referenced in ICRU Report 44 and tissue substitutes to prepare three different types of phantoms for calibrating the Hounsfield unit (HU)-to-density curves. Sinograms were first virtually generated by the MBFPA with four representative energy spectra (i.e. 80 kVp, 100 kVp, 120 kVp, and 6 MVp) and then reconstructed to form realistic CT images by adding statistical noise. The HU-to-density curves in each spectrum and their pairwise combinations were derived from the CT images. The accuracy of these curves was validated using the ICRP110 human phantoms.

RESULTS

The relative mean square errors (RMSEs) of the physical density by the HU-to-density curves calibrated with kV SECT nearly presented no phantom size dependence. The kV-kV DECT calibrated curves were also comparable with those from the kV SECT. The phantom size effect became notable when the MV X-ray beams were employed for both SECT and DECT due to beam-hardening effects. The RMSEs were decreased using the biological tissue phantom.

CONCLUSION

Simulation-based density prediction can be useful in the theoretical analysis of SECT and DECT calibrations. The results of this study indicated that the accuracy of SECT calibration is comparable with that of DECT using biological tissues. The size and shape of the calibration phantom could affect the accuracy, especially for MV CT calibrations.

ADVANCES IN KNOWLEDGE

The present study is based on a full simulation environment, which accommodates various situations such as SECT, kV-kV DECT, and even kV-MV DECT. In this paper, we presented the advances pertaining to the accuracy of the physical density prediction when applied to SECT and DECT in the MV X-ray energy range. To the best of our knowledge, this study is the first to validate the physical density estimation both in SECT and DECT using human-type phantoms.

On the molecular relationship between Hounsfield Unit (HU), mass density, and electron density in computed tomography (CT)

Accurate determination of physical/mass and electron densities are critical to accurate spatial and dosimetric delivery of radiotherapy for photon and charged particles. In this manuscript, the biology, chemistry, and physics that underly the relationship between computed tomography (CT) Hounsfield Unit (HU), mass density, and electron density was explored. In standard radiation physics practice, quantities such as mass and electron density are typically calculated based off a single kilovoltage CT (kVCT) scan assuming a one-to-one relationship between HU and density. It is shown that, in absence of mass density assumptions on tissues, the relationship between HU and density is not one-to-one with uncertainties as large as 7%. To mitigate this uncertainty, a novel multi-dimensional theoretical approach is defined between molecular (water, lipid, protein, and mineral) composition, HU, mass density, and electron density. Empirical parameters defining this relationship are x-ray beam energy/spectrum dependent and, in this study, two methods are proposed to solve for them including through a tissue mimicking phantom calibration process. As a proof of concept, this methodology was implemented in a separate in-house created tissue mimicking phantom and it is shown that sub 1% accuracy is possible for both mass and electron density. As molecular composition is not always known, the sensitivity of this model to uncertainties in molecular composition was investigated and it was found that, for soft tissue, sub 1% accuracy is achievable assuming nominal organ/tissue compositions. For boney tissues, the uncertainty in mineral content may lead to larger errors in mass and electron density compared with soft tissue. In this manuscript, a novel methodology to directly determine mass and electron density based off CT HU and knowledge of molecular compositions is presented. If used in conjunction with a methodology to determine molecular compositions, mass and electron density can be accurately calculated from CT HU.