Correction of orientation dependence in magnetization transfer measures in the context of tractometry: Challenges, pitfalls and solutions
Bibliographic record
Abstract
In this work we investigate the feasibility of a correction method for removing the orientation dependence of magnetization transfer (MT) measures in the context of tractometry. Following previous work on the track-based characterization of such orientation dependence using diffusion MRI, a correction method was developed. It uses polynomial fits to extrapolate the single-fiber characterizations and allows the MT measures across all white matter tracks to be shifted towards a chosen reference value, effectively removing the bias of fiber orientation with respect to the main magnetic field. Three different references were tested on a dataset of one hundred acquisitions and the performance was accessed by evaluating the removal of the orientation dependence and the reduction of variance between acquisitions, while also exploring the effects on tractometry results. Throughout these experiments, various challenges and pitfalls of an empirical correction method were laid out, like the absence of ground truth or the lack of knowledge about the complex behavior of the phenomenon in crossing-fiber voxels. Nonetheless, a solution was presented, paving the way towards a fully validated correction method for MT measures.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".