Dosimetric Verification of Computed Tomography (CT) Systems Using CTDI Phantom
Bibliographic record
Abstract
Background: Computed tomography (CT) is a medical imaging modality that contributes widely over the world for the diagnosis of disease and for treatment planning in the radiotherapy department. The purpose of the study is to measure the accuracy of dose of CT System for quality treatment. Materials and Methods: The study was executed in a 16 slice SOMATOM Emotion CT Scanner of Delta Hospital Ltd. with Tube voltage 130 KV and Tube current 25 mA using Computed Tomography Dose Index (CTDI) phantom (CIRS) of MPD, Atomic Energy Centre, Dhaka. IBA pencil ionization chamber was used to measure the dose at different positions inside the CTDI phantom and data were collected using IBA MagicMax Universal software. The CT radiation doses were estimated using formalisms in the AAPM Report 96 and 111. Results: For the Adult Body Phantom Console displayed dose was 16.03 mGy and estimated dose was found as16.40 mGy. For the Adult Head Phantom, console displayed dose was 32.40 mGy and estimated dose was found as 34.60 mGy. Between the estimated and console displayed doses for Adult Body Phantom and Adult Head Phantom a deviation was realized of 2.3% and 6.8% respectively. Conclusion: Hence CTDI of the above mentioned machine comply with reference value within a tolerance of ± 20 % according to Food and Drug Administration (FDA). Bangladesh J. Nuclear Med. 26(2): 172-176, 2023
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".