Comparison of X-ray beam energy spectrum and effective energy in small animal imaging and dosimetry
Bibliographic record
Abstract
In this study, we compared the X-ray energy spectra against its estimated effective energy for small animal CT imaging and dosimetry applications. Two realistic Monte Carlo energy spectra were generated by means of an X-ray source at 50 kVp and 120 kVp, and validated against published spectra. The mono-energetic beam energies were 33 keV and 44 keV considered as the effective energies of the energy spectra of 50 kVp and 120 kVp, respectively. The effective energy was estimated regarding to the ASTM standards. For imaging application, we investigated the spatial resolution and contrast using a needle phantom and a uniform water cylinder with 4 sub-cylinders made of bone, brain tissue, fat and air. For dosimetry investigations, the mono- and the poly-energetic beam absorbed dose to a targeted lung tumor and to other organs was evaluated during small animal external beam radiotherapy treatment using Digimouse phantom. The results showed slight differences in the spatial resolution of 2.90% and −4.10% between the mono-(33 keV and 44 keV) and their respective poly-energetic beams (50 kVp and 120 kVp). The contrast depended on the materials: for 50 kVp and its effective energy 33 keV, the difference in the brain tissue was 1.17% and in bone tissue it was 13.48%. For 120 kVp and 44 keV, it was 1.84% and 10.16% in the brain and bone tissues respectively. In the case of bone, the lowest is the energy, the highest is the difference between the mono- and the poly-energetic contrast. The dose induced by mono-energetic beams was found higher than their counterparts poly-energetic beams in all tissues in the Digimouse phantom, and they showed the same behavior in terms of absorbed dose to neighboring organs relatively the tumor dose.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".