A data‐driven T<sub>2</sub> relaxation analysis approach for myelin water imaging: Spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME‐ECOS)
Bibliographic record
Abstract
Purpose The decomposition of multi‐exponential decay data into a T2 spectrum poses substantial challenges for conventional fitting algorithms, including non‐negative least squares (NNLS). Based on a combination of the resolution limit constraint and machine learning neural network algorithm, a data‐driven and highly tailorable analysis method named spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME‐ECOS) was proposed. Theory and Methods The theory of SAME‐ECOS was derived. Then, a paradigm was presented to demonstrate the SAME‐ECOS workflow, consisting of a series of calculation, simulation, and model training operations. The performance of the trained SAME‐ECOS model was evaluated using simulations and six in vivo brain datasets. The code is available at https://github.com/hanwencat/SAME‐ECOS . Results Using NNLS as the baseline, SAME‐ECOS achieved over 15% higher overall cosine similarity scores in producing the T2 spectrum, and more than 10% lower mean absolute error in calculating the myelin water fraction (MWF), as well as demonstrated better robustness to noise in the simulation tests. Applying to in vivo data, MWF from SAME‐ECOS and NNLS was highly correlated among all study participants. However, a distinct separation of the myelin water peak and the intra/extra‐cellular water peak was only observed in the mean T2 spectra determined using SAME‐ECOS. In terms of data processing speed, SAME‐ECOS is approximately 30 times faster than NNLS, achieving a whole‐brain analysis in 3 min. Conclusion Compared with NNLS, the SAME‐ECOS method yields much more reliable T2 spectra in a dramatically shorter time, increasing the feasibility of multi‐component T2 decay analysis in clinical settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".