Design and performance of a respiratory amplitude gating device for PET/CT imaging
Bibliographic record
Abstract
PURPOSE: Recently, the authors proposed a free-breathing amplitude gating (FBAG) technique for PET/CT scanners. The implementation of this technique required specialized hardware and software components that were specifically designed to interface with commercial respiratory gating devices to generate the necessary triggers required for the FBAG technique. The objective of this technical note is to introduce an in-house device that integrates all the necessary hardware and software components as well as tracks the patient's respiratory motion to realize amplitude gating on PET/CT scanners. METHODS: The in-house device is composed of a piezoelectric transducer coupled to a data-acquisition system in order to monitor the respiratory waveform. A LABVIEW program was designed to control the data-acquisition device and inject triggers into the PET list stream whenever the detected respiratory amplitude crossed a predetermined amplitude range. A timer was also programmed to stop the scan when the accumulated time within the selected amplitude range REACHED a user-set interval. This device was tested using a volunteer and a phantom study. RESULTS: The results from the volunteer and phantom studies showed that the in-house device can detect similar respiratory signals as commercially available respiratory gating systems and is able to generate the necessary triggers to suppress respiratory motion artifacts. CONCLUSIONS: The proposed in-house device can be used to implement the FBAG technique in current PET/CT scanners.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".