An approach for measuring the spatial orientations of a computed‐tomography simulation system
Bibliographic record
Abstract
The quality assurance tests for measuring the spatial orientations between tabletop, external patient positioning lasers, couch longitudinal moving direction, and imaging plane in a CT simulation system are a complicated and time-consuming process. We proposed a simple and efficient approach to acquire the angular deviations of spatial orientations between these components. An in-house cross-jig was used in this study. We found a relationship between the orientations of the jig's arms shown on the CT images and the orientations of the components in a CT simulator. We verified this relationship with 16 misalignment orientations of known errors, to simulate all possible deviation situations. Generally, the tabletop and external lasers system are mounted separately in a CT simulation system; the former is on the couch trail, the later is on the wall and ceiling. They are independent to each other and will cause different effects on CT images. We only need two scans to acquire the angular deviations of our system: i) when aligning the cross-jig with tabletop, we can check the orientations between the tabletop, couch longitudinal moving direction, and imaging plane; ii) while aligning the cross-jig with the external axial lasers, we will know the angular deviation between the lasers, couch longitudinal moving direction, and imaging plane. The CT simulator had been carefully examined by performing the QA procedures recommended by the AAPM Task Group 66. The measurements of the spatial orientations using the proposed method agree well with TG 66 recommendations. However, the time taken to perform the QA using our method is considerably shorter than the method described in TG 66--5 minutes versus 30 minutes. The deliberate misalignment orientations tests with known errors were detected successfully by our in-house analysis program. The maximum difference between the known errors and the measured angles is only 0.07°. We determined that the relationship between the orientations of the jig's arms and the orientations of the CT components. By means of quantifying the deviations in degree we can correct the errors accurately. This approach can also be used to inspect the spatial orientations of other imaging systems, such as PET-CT and MRI.
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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 | 0.002 | 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) | 0.003 | 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".