Susceptibility weighted imaging with multiple echoes
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it