Q‐space truncation and sampling in diffusion spectrum imaging
Bibliographic record
Abstract
Purpose To characterize the q‐space truncation and sampling on the spin‐displacement probability density function (PDF) in diffusion spectrum imaging (DSI). Methods DSI data were acquired using the MGH‐USC connectome scanner (Gmax = 300 mT/m) withbmax = 30,000 s/mm2, 17 × 17 × 17, 15 × 15 × 15 and 11 × 11 × 11 grids in ex vivo human brains andbmax = 10,000 s/mm2, 11 × 11 × 11 grid in vivo. An additional in vivo scan usingbmax=7,000 s/mm2, 11 × 11 × 11 grid was performed with a derated gradient strength of 40 mT/m. PDFs and orientation distribution functions (ODFs) were reconstructed with different q‐space filtering and PDF integration lengths, and from down‐sampled data by factors of two and three. Results Both ex vivo and in vivo data showed Gibbs ringing in PDFs, which becomes the main source of artifact in the subsequently reconstructed ODFs. For down‐sampled data, PDFs interfere with the first replicas or their ringing, leading to obscured orientations in ODFs. Conclusion The minimum required q‐space sampling density corresponds to a field‐of‐view approximately equal to twice the mean displacement distance (MDD) of the tissue. The 11 × 11 × 11 grid is suitable for both ex vivo and in vivo DSI experiments. To minimize the effects of Gibbs ringing, ODFs should be reconstructed from unfiltered q‐space data with the integration length over the PDF constrained to around the MDD. Magn Reson Med 76:1750–1763, 2016. © 2016 International Society for Magnetic Resonance in Medicine
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".