4 - Déroulement de phase : application à la correction de distorsions géométriques en IRM
Bibliographic record
Abstract
Magnetic Resonance Imaging has entered clinical practice about fifteen years ago, and has become one of the most widely used imaging modality. MRI suffers from important geometric distortions, leading to pixel shifts and intensity variations in the acquired images. Correction of these distortions is clearly required in stereotactic surgery using frame-based registrations or neuro-navigation. These distortions can be corrected using the phase of signal or image. However, as in Inverse Synthetic Aperture Radar (ISAR), the phase of the signal is obtained modulo 2 π. The goal of Phase unwrapping is to retrieve the initial phase of the signal. After a brief summary of related works and applications mainly using ISAR data, this paper presents a new algorithm for phase unwrapping. This algorithm is fast, robust to noise and takes into account the discontinuities of the acquired object. It is based upon the notion of homogeneous region. This homogeneity is defined by phase jumps and no parameters have to be determined a priori. Experiments on noisy phantoms exhibit good robustness to noise. An application to the correction of MRI of the head is presented.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".