Incorporating multislice imaging into x‐ray CT polymer gel dosimetry
Bibliographic record
Abstract
PURPOSE: To evaluate multislice computed tomography (CT) scanning for fast and reliable readout of radiation therapy (RT) dose distributions using CT polymer gel dosimetry (PGD) and to establish a baseline assessment of image noise and uniformity in an unirradiated gel dosimeter. METHODS: A 16-slice CT scanner was used to acquire images through a 1 L cylinder filled with water. Additional images were collected using a single slice machine. The variability in CT number (NCT) associated with the anode heel effect was evaluated and used to define a new slice-by-slice background subtraction artifact removal technique for CT PGD. Image quality was assessed for the multislice system by evaluating image noise and uniformity. The agreement in NCT for slices acquired simultaneously using the multislice detector array was also examined. Further study was performed to assess the effects of increasing x-ray tube load on the constancy of measured NCT and overall scan time. In all cases, results were compared to the single slice machine. Finally, images were collected throughout the volume of an unirradiated gel dosimeter to quantify image noise and uniformity before radiation is delivered. RESULTS: Slice-by-slice background subtraction effectively removes the variability in NCT observed across images acquired simultaneously using the multislice scanner and is the recommended background subtraction method when using a multislice CT system. Image noise was higher for the multislice system compared to the single slice scanner, but overall image quality was comparable between the two systems. Further study showed NCT was consistent across image slices acquired simultaneously using the multislice detector array for each detector configuration of the slice thicknesses examined. In addition, the multislice system was found to eliminate variations in NCT due to increasing x-ray tube load and reduce scanning time by a factor of 4 when compared to imaging a large volume using a single slice scanner. Images acquired through an unirradiated, active gel revealed NCT varies between the top and bottom of the 1 L cylinder as well as across the diameter of the cylinder by up to 7 HU. CONCLUSIONS: Multislice CT imaging has been evaluated for CT PGD and found to be the superior technique compared to single slice imaging in terms of the time required to complete a scan and the tube load characteristics associated with each scanning method. The implementation of multislice scanning is straightforward and expected to facilitate routine gel dosimetry measurements for complex dose distributions in modern RT centers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".