Quantitative diffusion imaging with steady‐state free precession
Bibliographic record
Abstract
The addition of a single, unbalanced diffusion gradient to the steady-state free precession (SSFP) imaging sequence sensitizes the resulting signal to free diffusion. Unfortunately, the confounding influence of both longitudinal (T1) and transverse (T2) relaxation on the diffusion-weighted SSFP (dwSSFP) signal has made it difficult to quantitatively determine the apparent diffusion coefficient (ADC). Here, a multistep method in which the T1, T2, and spin density (Mo) constants are first determined using a rapid mapping technique described previously is presented. Quantitative ADC can then be determined through a novel inversion of the appropriate signal model. The accuracy and precision of our proposed method (which we term DESPOD) was determined by comparing resulting ADC values from phantoms to those calculated from traditional diffusion-weighted echo planar imaging (dwEPI) images. Error within the DESPOD-derived ADC maps was found to be less than 3%, with good precision over a biologically relevant range of ADC values.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".