A gated deep inspiration breath-hold radiation therapy technique using a linear position transducer
Bibliographic record
Abstract
For patients with thoracic and abdominal lesions, respiration-induced internal organ motion and deformations during radiation therapy are limiting factors for the administration of high radiation dose. To increase the dose to the tumor and to reduce margins, tumor movement during treatment must be minimized. Currently, several types of breath-synchronized systems are in use. These systems include respiratory gating, deep inspiration breath-hold, active breathing control, and voluntary breath-hold. We used a linear position transducer (LPT) to monitor changes in a patient's abdominal cross-sectional area. The LPT tracks changes in body circumference during the respiratory cycle using a strap connected to the LPT and wrapped around the patient's torso. The LPT signal is monitored by a computer that provides a real-time plot of the patient's breathing pattern. In our technique, we use a CT study with multiple gated acquisitions. The Philips Medical Systems Q series CT imaging system is capable of operating in conjunction with a contrast injector. This allows a patient performing the deep inspiration breath-hold maneuver to send a signal to trigger the CT scanner acquisitions. The LPT system, when interfaced to a LINAC, allows treatment to be delivered only during deep inspiration breath-hold periods. Treatment stops automatically if the lung volume drops from a preset value. The whole treatment can be accomplished with 1 to 3 breath-holds. This technique has been used successfully to combine automatically gated radiation delivery with the deep inspiration breath-hold technique. This improves the accuracy of treatment for moving tumors, providing better target coverage, sparing more healthy tissue, and saving machine time. PACS numbers: 87.53.2j, 87.57.-s
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".