SU‐E‐I‐66: Estimating Organ and Effective Doses for Digital Tomosynthesis Imaging Using PCXMC Software
Bibliographic record
Abstract
Purpose: In order to accurately model a tomosynthesis image acquisition for estimating organ and effective doses with PCXMC, we must consider each discrete projection angle of the x‐ray tube separately, compute the dose in each case, and sum the results. This can be a time‐consuming task that requires a considerable amount of data entry. We have developed two techniques that we can use to improve the user‐friendliness and time‐efficiency of obtaining dose estimates for a tomosynthesis sweep. Methods: We developed a MATLAB program to automatically generate the definition files for each x‐ray tube position and compared the resulting definition files and dose calculations to those that we created manually for two test cases. We also calculated the doses by simplifying the imaging geometry to neglect the motion of the x‐ray tube during the tomosynthesis sweep. We applied the sum of the incident air kerma for the entire sweep to a single, central projection, and evaluated the impact of this simplification on the speed and accuracy of the dose calculations. Results: Our program produced the same definition files and doses as our manual calculation in both cases, and using the program resulted in a dramatic reduction in the amount of time required to complete the calculations. When neglecting the motion of the x‐ray tube, the doses to some organs were over‐ or underestimated but the effective doses still agreed with the values calculated using the more accurately modeled geometry. Conclusions: Our MATLAB program allows us to accurately model the imaging geometry of a tomosynthesis exam in PCXMC in a time‐efficient manner. For both test cases, a reasonable estimate of the effective dose can be achieved very quickly by neglecting the motion of the x‐ray tube, but this also sacrifices the accuracy of individual organ dose calculations. This research was supported by funding from GE Healthcare.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".