A multivendor phantom study comparing the image quality produced from three state-of-the-art SPECT-CT systems
Bibliographic record
Abstract
OBJECTIVE: Ongoing advancements in single photon emission computed tomography with on-board X-ray computed tomography (SPECT-CT) hardware and software raise important questions regarding the relative performances of various cameras and their respective image-processing software. This phantom-based study compares images produced from three state-of-the-art cameras using four image quality measurements. METHODS: A thorax phantom modeling the spine, lungs, a healthy heart, and three tumors (cylindrical bottles) was scanned using the following SPECT-CT systems: Philips' Precedence (PP), GE's Infinia-Hawkeye (GH), and Siemens' Symbia-T6 (SS). For each scan, Tc-99m solutions were injected into the heart, three bottles, and thorax to yield activity concentration ratios of roughly 6:1 for both heart:thorax and tumor:thorax. The data were reconstructed using the most advanced software available on the cameras, namely, Evolution for Bone and Evolution for Cardiac (EVB and EVC, respectively), Astonish (AST), and Flash3D (FLA) for GH, PP, and SS, respectively. In addition, all sets of data were reconstructed using our in-house software. The mean values of activity error, uniformity, signal-to-noise ratio, and contrast error were used as figures of merit (FOM). RESULTS: No significant differences were observed for all FOM between all in-house reconstructions using PP, GH, and SS acquisition data. The mean activity error for the AST reconstruction (-24.0±1.6%) was significantly closer to the truth relative to EVB (-38.0±1.6%), EVC (-34.5±2.3%), and FLA (-33.8±1.6%). No significant differences were found between EVC and FLA for all FOM. CONCLUSION: In this phantom-based study, Philips' AST provided the most quantitatively accurate and highest contrast images, whereas Siemens' FLA and GE's EVC provided relatively higher signal-to-noise ratios and more uniform 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".