Optimization of Low-Dose CT Protocol in Pediatric Nuclear Medicine Imaging
Bibliographic record
Abstract
UNLABELLED: This study was performed to find the optimal low-dose CT protocol for children being imaged on SPECT/CT scanners not equipped with automatic dose control. For SPECT/CT systems with manually adjustable x-ray tube voltage (kV) and anode current (mA), an optimized protocol makes it possible to minimize the dose to patients. METHODS: Using the 4-slice low-dose CT component of a commercially available SPECT/CT scanner, we compared the signals reaching the CT detector after radiation passes through objects of different sizes. First, the exit dose rates were measured for combinations of available voltages and currents. Next, imaging parameters were selected on the basis of acceptable levels of exit dose rates, cylindric phantoms of different diameters approximating children of different sizes were scanned using these parameters, and the quality of the CT images was evaluated. Finally, weighted CT dose indexes for abdomen and head CT dose phantoms simulating, respectively, adult and pediatric patients were measured using exactly the same techniques to estimate and compare doses to these 2 groups of patients. RESULTS: For children with torsos smaller than 150 mm, imaging can be performed using the lowest available voltage and current (120 kV and 1 mA, respectively). For children with torsos less than 250 mm, 140 kV and 1.5 mA can be used. For patients with torsos greater than 250 and less than 300 mm, 140 kV and 2 mA can be used. Regarding the signal-to-noise ratio, all these parameters give an excellent signal and fully acceptable noise levels. CONCLUSION: For the SPECT/CT system studied, even the lowest available voltage and current used for scanning pediatric patients did not cause signal-to-noise degradation, and the use of these settings substantially lowered the dose to the patients.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| 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".