Appendix A: Body Tissue Compositions

White, D. R.; Griffith, R. V.; Wilson, I. J.

doi:10.1093/jicru_os24.1.11

Cited by 5 publications

(8 citation statements)

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“…In our simulation, we assumed that breast tissue consists of a mixture of adipose and glandular tissues following the composition provided by the International Commission on Radiation Units and Measurements (ICRU) report 46 [31]. Although the adipose and the glandular tissues in the breast were not explicitly contoured by the radiation oncologist, they can be distinguished in the tissue segmentation process and the resulting reconstruction geometry.…”

Section: Discussionmentioning

confidence: 99%

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Organ absorbed doses in the IORT treatment of breast cancer with the INTRABEAM device: a Monte-Carlo study

Nasir

Probst

Schneider

et al. 2023

Biomed. Phys. Eng. Express

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Purpose: The current prescription and the assessment of the delivered absorbed dose in intraoperative radiation therapy (IORT) with the INTRABEAM system rely mainly on depth-dose measurements in water. The accuracy of this approach is limited because tissue heterogeneity is ignored. It is also difficult to accurately determine the dose delivered to the patient experimentally as the steep dose gradient is highly sensitive to geometric errors. Our goal is to determine the dose to the target volume and the organs at risk of a clinical breast cancer patient from treatment with the system. Methods: A homogeneous water-equivalent CT dataset was derived from the preoperative CT scan of a patient by setting all materials in the patient volume as water-equivalent. This homogeneous CT data represents the current assumption of a homogenous patient, while the original CT data is considered the ground truth. An in-house Monte Carlo algorithm was used to simulate the delivered dose in both setups for a prescribed treatment dose of 20 Gy to the surface of the 3.5-cm-diameter spherical applicator. Results: The doses received by 2% (D2%) of the target volume for the homogeneous and heterogeneous geometries are 16.26 Gy and 9.33 Gy, respectively. The D2% for the heart are 0.035 Gy and 0.119 Gy for the homogeneous and heterogeneous geometries, respectively. This trend is also observed for the other organs at risk. Conclusions: The assumption of a homogeneous patient overestimates the dose to the target volume and underestimates the doses to the organs at risk.

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Section: Discussionmentioning

confidence: 99%

“…Mass density, electron density range, and elemental composition of each material for the MC dose calculations[30,31].…”

mentioning

confidence: 99%

Organ absorbed doses in the IORT treatment of breast cancer with the INTRABEAM device: a Monte-Carlo study

Nasir

Probst

Schneider

et al. 2023

Biomed. Phys. Eng. Express

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“…Each body structure was then assigned an index signifying a material of pre-set elemental composition, as listed in Appendix A from ICRU Report 46. 17 To introduce substructure in the brain, the brain structures were classified as white matter, grey matter or a mixed brain material. If the structure was categorized as mixed, it was assigned the whole brain material from ICRU Report 46.…”

Section: Methods 21 Phantoms and Ground Truth Spr Mapsmentioning

confidence: 99%

Proton-stopping power ratio prediction using photon-counting computed tomography and deep learning

Larsson,

Hein,

Huang

et al. 2024

Medical Imaging 2024: Physics of Medical Imaging

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Proton radiation therapy has the potential of achieving precise dose delivery to the tumor while sparing noncancerous surrounding tissue, owing to the sharp Bragg peaks of protons. Aligning the high dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors within CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution and filtering of electronic noise. In this study, the potential of photon-counting computed tomography for improving SPR estimation was assessed by training a deep neural network on a domain transform from photon-counting CT images to SPR maps. XCAT phantoms of the head were generated and used to simulate photon-counting CT images with CatSim, as well as to compute corresponding ground truth SPR maps. The CT images and SPR maps were then used as input and labels to a neural network. Prediction of SPR with the network yielded mean root mean square errors (RMSE) of 0.26-0.41 %, which is an improvement on errors reported for methods based on dual energy CT (DECT). These early results show promise for using a combination of photoncounting CT and deep learning for predicting SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.

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“…In addition, the data library of thermal neutron scattering law is optionally available in PHITS, and low-energy neutrons can be tracked precisely. Thus, the energy spectra of neutrons and γ-rays are calculated for light water, however, the dose rates were calculated for the composition of the average soft tissue of the adult ICRU-44 (male) 24) using kerma factors of each constituent element, which consists of 10.6% hydrogen, 25.6% carbon, 2.7% nitrogen, and 60.2% oxygen in mass percentages. Other inconsiderable nuclei such as sodium and phosphorous were omitted.…”

Section: Verification Of the Sidementioning

confidence: 99%

Development of a simple calculation tool of dose distributions in a phantom for boron neutron capture therapy

Ishikawa

Watanabe

Yoshihashi

et al. 2022

Jpn. J. Appl. Phys.

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A simple dose calculation tool, SiDE, was developed for dose evaluation in a water phantom for boron neutron capture therapy, which shortens the calculation time much more than the conventional particle transportation Monte Carlo codes and is applicable to any type of incident neutron spectra to the phantom. As the SiDE can not only calculate quantitatively the dose distribution in the phantom but also output dose indexes such as advantage depth and peak tumor dose, comparison between different boron neutron capture therapy neutron sources can be easily performed. Consistency with a Monte Carlo transportation code was verified through comparison with the conventional dose calculation with the Particle and Heavy Ion Transport Code System, and the calculation time was nearly 1/90 in the SiDE. The dose distributions for a reactor and accelerator-based neutron sources were compared, and the differences were found to be small although large differences between the incident spectra.

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Appendix A: Body Tissue Compositions

Cited by 5 publications

References 0 publications

Organ absorbed doses in the IORT treatment of breast cancer with the INTRABEAM device: a Monte-Carlo study

Organ absorbed doses in the IORT treatment of breast cancer with the INTRABEAM device: a Monte-Carlo study

Proton-stopping power ratio prediction using photon-counting computed tomography and deep learning

Development of a simple calculation tool of dose distributions in a phantom for boron neutron capture therapy

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