https://github.com/adler-j/huest

Hounsfield estimation from spectrums

https://github.com/adler-j/huest

Science Score: 10.0%

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    1 of 1 committers (100.0%) from academic institutions
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    Low similarity (6.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Hounsfield estimation from spectrums

Basic Info
  • Host: GitHub
  • Owner: adler-j
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 10.7 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 9 years ago · Last pushed over 9 years ago
Metadata Files
Readme

README.md

huest

HoUnsfield ESTimate, a package for estimation of hounsfield values given material compositions.

Includes functionality for

  • Computing HU value from a given material composition and a density
  • Computing the density from a given material composition

Installation

Simply run

pip install huest

The library uses xraylib in order to find the attenuation coefficients by the material. This needs to be installed separately.

Examples

Compute HU value of water

>>> import huest
>>> density = 1.0  # g / cm^3
>>> energy = 50  # kilovolt
>>> huest.hounsfield_value('Water, Liquid', density, energy)
0.0

Compute the HU value of bone with a given energy spectrum

>>> import huest
>>> density = 1.92  # g / cm^3
>>> energies = [40, 60, 80]  # kilovolt
>>> spectrum = [2, 2, 1]  # relative density of each energy
>>> huest.hounsfield_value('Bone, Cortical (ICRP)', density, energies, spectrum)
2617.5413064730405

Can also be used to compute densities from HU values

>>> import huest
>>> hu_value = 2000.0
>>> energies = [40, 60, 80]
>>> spectrum = [2, 2, 1]
>>> density = huest.density('Bone, Cortical (ICRP)', hu_value, energies, spectrum)
1.5922416669281303

The full list of supported materials can be acquired from xraylib:

>>> import xraylib
>>> xraylib.GetCompoundDataNISTList()
['A-150 Tissue-Equivalent Plastic',
 ...,
 'Xylene']

Materials can also be given according to their stoichiometric composition. Note that water is not exactly 'H2O':

>>> import huest
>>> huest.hounsfield_value('H2O', 1, 50)
0.10973551267046133

Owner

  • Name: Jonas Adler
  • Login: adler-j
  • Kind: user
  • Location: London, UK
  • Company: @deepmind

Research Scientist at Google DeepMind

GitHub Events

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Last Year

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Last synced: over 3 years ago

All Time
  • Total Commits: 9
  • Total Committers: 1
  • Avg Commits per committer: 9.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Jonas Adler j****l@k****e 9
Committer Domains (Top 20 + Academic)
kth.se: 1

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Last synced: 11 months ago

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  • Total issues: 0
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  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
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  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
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  • Average comments per issue: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 4 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 2
  • Total maintainers: 1
pypi.org: huest

HoUnsfield ESTimate

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 4 Last month
Rankings
Dependent packages count: 9.8%
Dependent repos count: 21.8%
Forks count: 29.9%
Average: 35.2%
Stargazers count: 38.9%
Downloads: 75.6%
Maintainers (1)
Last synced: 11 months ago

Dependencies

setup.py pypi
  • numpy *