SU‐FF‐I‐34: Effect of Motion On High Contrast Vessel‐Like Objects for Volumetric DCE‐CT
Bibliographic record
Abstract
Purpose: This study aims to assess the impact of motion on high‐contrast vessel‐like objects in volumetric CT scans as a step towards volumetric DCE‐CT on a 320‐slice CT scanner. Material and Methods: An acrylic cylindrical phantom was constructed to investigate influence of motion on contrast‐enhanced cylindrical structures (capsules) mimicking contrast‐bearing blood vessels. The phantom consists of 12 Teflon capsules of varying diameters (1, 2, 5 and 10 mm) embedded at predefined positions and in different orientations (longitudinal, diagonal and axial). A motor‐driven platform provided uniform phantom motion speeds of 0, 0.5, 1.0 and 2.0 cm/s along the longitudinal axis of the scanner. Gantry rotation speeds TG was varied between 0.35 and 3 s. All scans were acquired on a Toshiba Aquilion ONE CT scanner with a field‐of‐view of 16 cm in one rotation. Results: For a given phantom motion speed, HU decreased with increasing TG. Increasing the phantom motion speed from 0 to 2.0 cm/s reduced the HU by 9% for TG = 0.35 s and by 21% for TG = 3 s. Measured HU values also decreased with increasing TG for the other capsule orientations, however, it was least pronounced for the longitudinal orientation. The discrepancy between longitudinal and axial orientation is 2% for TG = 0.35 s and 9% for TG = 3 s. These differences are more pronounced for smaller capsules. Conclusions: A systematic study was performed to quantify the impact of motion on dynamic contrast‐enhanced CT measurements. It was found that contrast in vessel‐like objects is affected by orientation and motion. However, for fast gantry rotations (< 0.5 s) motion affects the contrast measurement by generally less than 10% for different phantom speeds and less than 2% for different vessel orientations. With this phantom, optimization of different scan parameters is possible.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".