Susceptibility mapping of air, bone, and calcium in the head
Bibliographic record
Abstract
PURPOSE: To demonstrate the mapping of structures with high susceptibility values, such as the sinuses, bones and teeth, using short echo times. METHODS: Four in vivo datasets were collected with a gradient-echo sequence (TE1 = 2.5 ms, TE2 = 5 ms and TE3 = 7.5 ms). Complex division was performed to remove the phase offset term and generate the phase at TE = 2.5 ms. Susceptibility maps were generated from unwrapped phase images, using a geometry-constrained iterative algorithm, by preserving phase information in the extracerebral tissues. The susceptibility results were improved by updating the missing phase information inside structures with no signal using the predicted phase at each iteration step. Simulated phase images of a three-dimensional brain model and tooth phantom were used to validate the proposed method. RESULTS: Improved susceptibility maps were obtained once the phase information in the extracerebral tissue region was incorporated, for both the model and in vivo data. For in vivo data, the average susceptibilities of air (sphenoid sinus), bone and calcium (teeth) were found to be (in ppm): Δχ(sinus-tissue) = +9.2 ± 1.3, Δχ(bone-tissue) = -2.1 ± 0.6 and Δχ(teeth-tissue) = -3.3 ± 1.2, respectively. CONCLUSION: High susceptibility structures with little or no signal can be imaged using quantitative susceptibility mapping and can be used to improve background field removal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".