Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques. METHODS: Given a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness. RESULTS: Performance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6. CONCLUSION: The results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it