Peak‐combination HARP: A method to correct for phase errors in HARP
Bibliographic record
Abstract
PURPOSE: To introduce a method to correct phase errors (e.g., from B0 inhomogeneity) in tagging images, which may affect harmonic phase (HARP) evaluation. MATERIALS AND METHODS: The phase images corresponding to the negative and positive harmonic peaks in k-space are combined before HARP evaluation to eliminate any spurious phase. To validate in vivo, two complementary spatial modulation of magnetization (CSPAMM) data sets were collected for each volunteer and evaluated with conventional HARP, using either the positive or the negative harmonic peak, and with peak-combination HARP. RESULTS: Elimination of phase distortion by peak combination was observed in vitro and in vivo. Improved reproducibility of motion parameters was found with peak-combination HARP. CONCLUSION: With peak-combination HARP, reproducibility of contractile parameters is improved, and consequently, the number of subjects needed to detect statistically significant changes in contractile function can be reduced to one third compared to conventional HARP evaluation.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".