Quality assessment of high angular resolution diffusion imaging data using bootstrap on Q‐ball reconstruction
Bibliographic record
Abstract
PURPOSE: To develop a bootstrap method to assess the quality of High Angular Resolution Diffusion Imaging (HARDI) data using Q-Ball imaging (QBI) reconstruction. MATERIALS AND METHODS: HARDI data were re-shuffled using regular bootstrap with jackknife sampling. For each bootstrap dataset, the diffusion orientation distribution function (ODF) was estimated voxel-wise using QBI reconstruction based on spherical harmonics functions. The reproducibility of the ODF was assessed using the Jensen-Shannon divergence (JSD) and the angular confidence interval was derived for the first and the second ODF maxima. The sensitivity of the bootstrap method was evaluated on a human subject by adding synthetic noise to the data, by acquiring a map of image signal-to-noise ratio (SNR) and by varying the echo time and the b-value. RESULTS: The JSD was directly linked to the image SNR. The impact of echo times and b-values was reflected by both the JSD and the angular confidence interval, proving the usefulness of the bootstrap method to evaluate specific features of HARDI data. CONCLUSION: The bootstrap method can effectively assess the quality of HARDI data and can be used to evaluate new hardware and pulse sequences, perform multifiber probabilistic tractography, and provide reliability metrics to support clinical studies.
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 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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".