Implementation and characterization of a 320‐slice volumetric CT scanner for simulation in radiation oncology
Bibliographic record
Abstract
PURPOSE: Effective target definition and broad employment of treatment response assessment with dynamic contrast-enhanced CT in radiation oncology requires increased speed and coverage for use within a single bolus injection. To this end, a novel volumetric CT scanner (Aquilion One, Toshiba, Tochigi Pref., Japan) has been installed at the Princess Margaret Hospital for implementation into routine CT simulation. This technology offers great advantages for anatomical and functional imaging in both scan speed and coverage. The aim of this work is to investigate the system's imaging performance and quality as well as CT quantification accuracy which is important for radiotherapy dose calculations. METHODS: The 320-slice CT scanner uses a 160 mm wide-area (2D) solid-state detector design which provides the possibility to acquire a volumetric axial length of 160 mm without moving the CT couch. This is referred to as "volume" and can be scanned with a rotation speed of 0.35-3 s. The scanner can also be used as a 64-slice CT scanner and perform conventional (axial) and helical acquisitions with collimation ranges of 1-32 and 16-32 mm, respectively. Commissioning was performed according to AAPM Reports TG 66 and 39 for both helical and volumetric imaging. Defrise and other cone-beam image analysis tests were performed. RESULTS: Overall, the imaging spatial resolution and geometric efficiency (GE) were found to be very good (>10 lp/mm, <1 mm spatial integrity and GE160 mm=85%) and within the AAPM guidelines as well as IEC recommendations. Although there is evidence of some cone-beam artifacts when scanning the Defrise phantom, image quality was found to be good and sufficient for treatment planning (soft tissue noise <10 HU). Measurements of CT number stability and contrast-to-noise values across the volume indicate clinically acceptable scan accuracy even at the field edge. CONCLUSIONS: Initial experience with this exciting new technology confirms its accuracy for routine CT simulation within radiation oncology and allows for future investigations into specialized dynamic volumetric imaging applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".