Impact of patient comfort on diagnostic image quality during PET/MR exam: A quantitative survey study for clinical workflow management
Bibliographic record
Abstract
BACKGROUND: PET/MR is transferring from a powerful scientific research tool to an imaging modality in clinical routine practice. Whole body PET/MR screening usually takes 30-50 minutes to finish, during which a few factors might induce patient discomfort and further cause degraded image quality. The aim of this report is to investigate the patients' perception of the imaging procedure and its correlation with image quality. METHODS: One hundred and twenty patients (63 males and 57 females, average age = 51.3 years, range 22-70 years) who had been diagnosed with cancer or had previous history of cancer were recruited and scanned with a simultaneous PET/MR system. A questionnaire was given to all patients retrospectively after the PET/MR scan, which has nine questions to assess patients' feeling of the scan on a Likert scale scoring system (1-5, 1 as most satisfied). All PET/MR images were also visually examined by two experts independently to evaluate the quality of the images. Six body locations were assessed and each location was evaluated also with a Likert scale scoring system (1-5, 5 as the best quality). Mann-Whitney U-test was used for statistical analysis to check if there is significant correlation between image quality and patient perceptions. RESULTS: With a total of 120 patients, 118 questionnaires were filled and returned for analysis. The patients' characteristics were summarized in Table 4. The statistics of the patients' perception in the questionnaire were illustrated in Tables 5-7. Statistical significant correlations were found between MR image quality and patients' characteristics/perception. CONCLUSION: Our results show that PET/MR scanning is generally safe and comfortable for most of the patients. Statistical analysis does not support the hypothesis that bad patient's perception leads to degraded image quality.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".