Susceptibility weighted imaging with multiple echoes
Bibliographic record
Abstract
PURPOSE: To extend susceptibility weighted imaging (SWI) to multiple echoes with an adapted homodyne filtering of phase images for the computation of venograms with improved signal to noise ratio (SNR) and contrast to noise ratio (CNR) and to produce high resolution maps of R(2) relaxation. MATERIALS AND METHODS: Three-dimensional multi echo gradient echo data were acquired with five equidistant echoes ranging from 13 to 41 ms. The phase images of each echo were filtered with filter parameters adjusted to the echo time, converted into a phase mask, and combined with the corresponding magnitude images to obtain susceptibility weighted images. The individual images were then averaged. Conventional single echo data were acquired for comparison. Maps of R(2) relaxation rates were computed from the magnitude data. Field maps derived from the phase data were used to correct R(2) for the influences from background inhomogeneities of the static magnetic field. RESULTS: Compared with the single echo images, the combined images had an increase in SNR by 46% and an improvement in CNR by 34 to 80%, improved visibility of small venous vessels and reduced blurring along the readout direction. The R(2) values of different tissue types are in good agreement with values from the literature. CONCLUSION: Acquisition of SWI with multiple echoes leads to an increase in SNR and CNR and it allows the computation of high resolution maps of R(2) relaxation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".