Effects of gradient encoding and number of signal averages on fractional anisotropy and fiber density index in vivo at 1.5 tesla
Bibliographic record
Abstract
BACKGROUND: Tensor estimation can be improved by increasing the number of gradient directions (NGD) or increasing the number of signal averages (NSA), but at a cost of increased scan time. PURPOSE: To evaluate the effects of NGD and NSA on fractional anisotropy (FA) and fiber density index (FDI) in vivo. MATERIAL AND METHODS: Ten healthy adults were scanned on a 1.5T system using nine different diffusion tensor sequences. Combinations of 7 NGD, 15 NGD, and 25 NGD with 1 NSA, 2 NSA, and 3 NSA were used, with scan times varying from 2 to 18 min. Regions of interest (ROIs) were placed in the internal capsules, middle cerebellar peduncles, and splenium of the corpus callosum, and FA and FDI were calculated. Analysis of variance was used to assess whether there was a difference in FA and FDI of different combinations of NGD and NSA. RESULTS: There was no significant difference in FA of different combinations of NGD and NSA of the ROIs (P>0.005). There was a significant difference in FDI between 7 NGD/1 NSA and 25 NGD/3 NSA in all three ROIs (P<0.005). There were no significant differences in FDI between 15 NGD/3 NSA, 25 NGD/1 NSA, and 25 NGD/2 NSA and 25 NGD/3 NSA in all ROIs (P>0.005). CONCLUSION: We have not found any significant difference in FA with varying NGD and NSA in vivo in areas with relatively high anisotropy. However, lower NGD resulted in reduced FDI in vivo. With larger NGD, NSA has less influence on FDI. The optimal sequence among the nine sequences tested with the shortest scan time was 25 NGD/1 NSA.
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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 | 0.000 |
| 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) | 0.000 | 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".