TU‐G‐211‐08: A Multi‐Vendor Phantom Study Comparing the Image Quality Produced from Three State‐Of‐The‐Art SPECT‐CT Systems
Bibliographic record
Abstract
Purpose: Ongoing advancements in SPECT‐CT hardware and software raise important questions regarding the relative performances of various cameras and their respective image‐processing software. This phantom study compares images produced from three state‐of‐the‐art cameras using four figures‐of‐merit (FOM) to assess image quality. Methods: A thorax phantom modeling the spine, lungs, a healthy heart and 2 tumours (cylindrical bottles) was scanned with the following SPECT‐CT systems: Philipsˈ Precedence (PP), GEˈs Infinia‐Hawkeye (GH), and Siemensˈ Symbia‐T6 (SS). For each scan, Tc‐99m solutions were injected into the heart (120mL), two bottles (33mL) and thorax (7000mL) to yield activity concentration ratios of roughly 6:1 and 8:1 for heart:thorax and tumour:thorax, respectively. The processing was performed using the reconstruction software available on the cameras; namely, Evolution, Astonish and Flash3D for GH, PP, and SS, respectively. Additionally, all sets of data were reconstructed using our in‐house (MIRG) software. Mean values of activity error, uniformity, signal to noise ratio (SNR) and image contrast were used as FOM for the three objects of interest in each image (heart and 2 bottles). Two‐tailed paired t‐tests were used to test significance between means, considering p<0.05 as significant. Results: No significant differences were observed for all FOM between MIRG reconstructions using PP, GH and SS acquisition data. Mean activity errors for the PP reconstructions were significantly closer to the truth relative to GH and SS reconstructions and contrast measurements were significantly better for PP relative to SS. However, PP uniformity was significantly lower than GH and SS. No significant differences were found between GH and SS for all FOM. Conclusions: When reconstructing the data with the same algorithm, no significant differences were observed for any FOM; however, when using the respective vendor algorithms, PP yielded more accurate activity and contrast measurements, yet lower uniformity relative to GH and SS images.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".