The “Macaroni Sign” of Takayasu’s Arteritis: Figure 1.
Bibliographic record
Abstract
1355 Objectives: The penalized likelihood estimation reconstruction algorithm Q.Clear (GE Healthcare) allows for full convergence and edge preservation through a block sequential regularized expectation maximization technique. In this study the performance of Q.Clear was investigated for different penalization factors (β) with the aim to optimize its clinical use for four different tracers. Methods: Q.Clear reconstructions with β values of 200, 400, 600 and 800 were compared to time-of-flight ordered subset expectation maximization (TF-OSEM) (3 iterations, 16 subsets and 5 mm Gaussian filter) with point spread function recovery. Clinical whole-body PET/CT (Discovery MI, GE Healthcare) scans with 68Ga-DOTATOC, 18F-FDG, 11C-acetate or 18F-fluoride were analyzed for level of noise in healthy liver tissue, signal to noise ratio (SNR), signal to background ratio (SBR) and maximum standardized uptake value (SUVmax). In addition, acquisition times per bed position and transaxial field of view (FOV) of the reconstructed images were varied. For each tracer, images from 10 patients were included, with a mean of 30 lesions per tracer. A spherical reference volume of interest (VOI) was placed in the liver and lesions were delineated employing a 41% threshold of the maximum voxel. Results: The lowest levels of noise were reached with the highest beta factor resulting in the highest SNR, but this in turn gave the lowest SBR. Noise equivalence to OSEM was found with β 600 for 68Ga-DOTATOC, 18F-FDG and 18F-fluoride, and β 400 for 11C-acetate with a resulting significant increase of SUVmax (19.4%, 9.7%, 22.5% and 19.0% respectively) (P < 0.0001, paired t-test), SNR (22.1%, 22.6%, 9.5% and 33.6%) and SBR (19.5%, 11.7%, 21.3% and 18.5%) compared to OSEM. SNR decreased while SBR increased for all tracers when extending FOV from 500 to 700 mm, but only significantly for 18F-fluoride. Decreasing image acquisition time gave no statistical difference of SUVmax for 68Ga-DOTATOC, 18F-fluoride (2 to 1.5 min) for any reconstruction method nor for 11C-acetate (3 to 2 min) with β 蠅 400. Decreasing time for 18F-FDG (3 to 2 min) resulted in a change of optimal beta to β 800 in order to reach noise equivalence to OSEM along with maintaining a higher SNR than OSEM. Conclusion: Images reconstructed by Q.Clear result in a tracer-dependent increase in tumour SUVmax values compared to OSEM at matched levels of noise, and an improved SNR. The optimal penalization factor, both in terms of noise-equivalence to OSEM and in terms of absolute SNR, is tracer dependent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".