A realistic phantom for validating <scp>MRI</scp>‐based synthetic <scp>CT</scp> images of the human skull
Bibliographic record
Abstract
Purpose To introduce a new realistic human skull phantom for the validation of synthetic CT images of cortical bone from ultra‐short echo‐time ( UTE ) sequences. Methods A human skull of an adult female was utilized as a realistic representation of skull cortical bone. The skull was stabilized in a special acrylic container and was filled with contrast agents that have T 1 and T 2 relaxation times similar to human brain. The phantom was MR scanned at 3T with UTE and T 2 ‐weighted sequences, followed by CT . A clustering approach was developed to extract the cortical bone signal from MR images. maps of the skull were calculated. Synthetic CT images of the bone were compared to cortical bone signal extracted from CT images and confounding factors, such as registration errors, were analyzed. Results Dice similarity coefficient ( DSC ) of UTE ‐detected cortical bone was 0.84 and gradually decreased with decreasing number of spokes. DSC did not significantly depend on echo‐time. Registration errors were found to be significant confounding factors, with 25% decrease in DSC for consistent 2 mm error at each axis. Conclusion This work introduced a new realistic human skull phantom, specifically for the evaluation and analysis of synthetic CT images of cortical bone.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".