Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions
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We describe a new method to convert CT numbers into mass density and elemental weights of tissues required as input for dose calculations with Monte Carlo codes such as EGS4. As a first step, we calculate the CT numbers for 71 human tissues. To reduce the effort for the necessary fits of the CT numbers to mass density and elemental weights, we establish four sections on the CT number scale, each confined by selected tissues. Within each section, the mass density and elemental weights of the selected tissues are interpolated. For this purpose, functional relationships between the CT number and each of the tissue parameters, valid for media which are composed of only two components in varying proportions, are derived. Compared with conventional data fits, no loss of accuracy is accepted when using the interpolation functions. Assuming plausible values for the deviations of calculated and measured CT numbers, the mass density can be determined with an accuracy better than 0.04 g cm(-3). The weights of phosphorus and calcium can be determined with maximum uncertainties of 1 or 2.3 percentage points (pp) respectively. Similar values can be achieved for hydrogen (0.8 pp) and nitrogen (3 pp). For carbon and oxygen weights, errors up to 14 pp can occur. The influence of the elemental weights on the results of Monte Carlo dose calculations is investigated and discussed.
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Material decomposition by thresholding works well for large HU discrepancies but may fail between soft tissue and bone in presence of low mineral density.
“This works well for large HU discrepancies, e. g. air ([ -1000] HU) and bone ([200 , 3000] HU), but may fail otherwise, particularly between soft tissue ([ -150 , 300] HU) and bone in presence of low mineral density.”
From DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures (2018) · cites this paper as [12]
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DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures
Unberath, M., Zaech, J.-N., Lee, S. C., Bier, B., Fotouhi, J., Armand, M., & Navab, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00937-3_12 · Cited by 145 · see citing works
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- Authors
- Schneider, W., Bortfeld, T., & Schlegel, W.
- Affiliations
- German Cancer Research Center (DE)
- ORCID
- Bortfeld, Thomas
- Venue
- Physics in Medicine and Biology
- Publisher
- IOP Publishing
- Published
- 2000-01-25
- Volume
- 45
- Issue
- 2
- Pages
- 459-478
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W2045415729
- Cited by
- 831
- References
- 20
- Citations by year
- 2026: 37 · 2025: 59 · 2024: 41 · 2023: 57 · 2022: 58 · 2021: 40 · 2020: 49 · 2019: 39 · 2018: 63 · 2017: 45 · 2016: 47 · 2015: 54 · 2014: 26 · 2013: 53 · 2012: 27
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Radiation Therapy and Dosimetry
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Keywords
- Hounsfield units, elemental composition, Monte Carlo dose calculation, EGS4, interpolation functions, human tissue, phosphorus, calcium, radiotherapy dose distribution
- MeSH terms
- Female, Humans, Image Processing, Computer-Assisted, Male, Monte Carlo Method, Organ Specificity, Radiotherapy Planning, Computer-Assisted, Tomography, X-Ray Computed, Phantoms, Imaging
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