Dose heterogeneity correction for low-energy brachytherapy sources using dual-energy CT images
Bibliographic record
Abstract
Permanent seed implant brachytherapy is currently used for adjuvant radiotherapy of early stage prostate and breast cancer patients. The current standard for calculation of dose around brachytherapy sources is based on the AAPM TG-43 formalism, which generates the dose in a homogeneous water medium. Recently, AAPM TG-186 emphasized the importance of accounting for tissue heterogeneities. We have previously reported on a methodology where the absorbed dose in tissue can be obtained by multiplying the dose, calculated by the TG-43 formalism, by an inhomogeneity correction factor (ICF). In this work we make use of dual energy CT (DECT) images to extract ICF parameters. The advantage of DECT over conventional CT is that it eliminates the need for tissue segmentation as well as assignment of population based atomic compositions. DECT images of a heterogeneous phantom were acquired and the dose was calculated using both TG-43 and TG-43 [Formula: see text] formalisms. The results were compared to experimental measurements using Gafchromic films in the mid-plane of the phantom. For a seed implant configuration of 8 seeds spaced 1.5 cm apart in a cubic structure, the gamma passing score for 2%/2 mm criteria improved from 40.8% to 90.5% when ICF was applied to TG-43 dose distributions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".