TU-F-18A-03: Improving Tissue Segmentation for Monte Carlo Dose Calculation Using DECT Data
Bibliographic record
Abstract
Purpose: To develop a new segmentation technique using dual energy CT (DECT) to overcome limitations related to segmentation from a standard Hounsfield unit (HU) to electron density (ED) calibration curve. Both methods are compared with a Monte Carlo analysis of dose distribution. Methods: DECT allows a direct calculation of both ED and effective atomic number (EAN) within a given voxel. The EAN is here defined as a function of the total electron cross-section of a medium. These values can be effectively acquired using a calibrated method from scans at two different energies. A prior stoichiometric calibration on a Gammex RMI phantom allows us to find the parameters to calculate EAN and ED within a voxel. Scans from a Siemens SOMATOM Definition Flash dual source system provided the data for our study. A Monte Carlo analysis compares dose distribution simulated by dosxyz_nrc, considering a head phantom defined by both segmentation techniques. Results: Results from depth dose and dose profile calculations show that materials with different atomic compositions but similar EAN present differences of less than 1%. Therefore, it is possible to define a short list of basis materials from which density can be adapted to imitate interaction behavior of any tissue. Comparison of the dose distributions on both segmentations shows a difference of 50% in dose in areas surrounding bone at low energy. Conclusion: The presented segmentation technique allows a more accurate medium definition in each voxel, especially in areas of tissue transition. Since the behavior of human tissues is highly sensitive at low energies, this reduces the errors on calculated dose distribution. This method could be further developed to optimize the tissue characterization based on anatomic site.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".