Model-free marginal orientation distribution function reconstruction in single-shell Q-Ball imaging
Bibliographic record
Abstract
Q-Ball imaging (QBI) is a successful and widely used high angular resolution diffusion imaging (HARDI) technique which can compute orientation distribution function (ODF). This technique only needs single shell HARDI data and does not require any assumption about the diffusion signal outside the sampling sphere. However the originally proposed ODF (the radial project of the probability density function (PDF)) is not a true ODF. In contrast the marginal ODF with solid angle consideration, represents a true ODF with a correct probabilistic interpretation. In this paper a novel model-free and single-shell HARDI method for analytical reconstruction of the marginal ODF based on Funk-Radon transform (FRT) is proposed. In other words, a transformation of QBI for marginal ODF is proposed in this paper. While there's also a proportional factor introduced by FRT. Its complexity is comparable to that of the original QBI. Finally the present paper verify the algorithm and show the improved result of my method compared to the original QBI on synthetic data, biological phantom data and real human brain data.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".