SU‐E‐T‐661: Quantitative MRI Assessment of a Novel Direction‐Modulated Brachytherapy Tandem Applicator for Cervical Cancer
Bibliographic record
Abstract
Purpose: To quantitatively evaluate the MR image quality of a novel direction modulated brachytherapy (DMBT) tandem applicator for cervical cancer, using the clinical MRI scanning protocol for image guided brachytherapy. Methods: The tungsten alloy‐based applicator was placed in a water phantom and clinical imaging protocol was performed. Axial images were acquired using 2D turbo‐spin echo (TSE) T2‐weighted sequence on a 1.5T GE 450w MR scanner and an 8‐channel body coil. As multi‐channel receiver coil was used, inhomogeneities in the B1 receive field must be considered before performing the quantification process. Therefore the applicator was removed from the phantom and the whole imaging session was performed again for the water phantom with the same parameters. Images from the two scans were then subtracted, resulting in a difference image that only shows the applicator with its surrounding magnetic susceptibility dipole artifact. Line profiles were drawn and plotted on the difference image at various angles and locations along the tandem. Full width at half maximum (FWHM) was measured at all the line profiles to quantify the extent of the artifact. Additionally, the extent of the artifact along the diameter of the tandem was measured at various angles and locations. Results: After removing the background inhomogeneities of the receiver coil, FWHM of the tandem measured 5.75 ± 0.35 mm (the physical tandem diameter is 5.4 mm). The average extent of the artifacts along the diameter of the tandem measured is 2.14 ± 0.56 mm. In contrast to CT imaging of the same applicator (not shown here), the tandem can be easily identified without additional correction algorithms. Conclusion: This work demonstrated that the novel DMBT tandem applicator has minimal susceptibility artifact in T2‐weighted images employed in clinical practice for MRI‐guided brachytherapy of cervical cancer.
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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 | 0.001 |
| 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 | 0.001 | 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".