High‐resolution myelin‐water fraction and quantitative relaxation mapping using <scp>3D ViSTa‐MR</scp> fingerprinting
Bibliographic record
Abstract
Abstract Purpose This study aims to develop a high‐resolution whole‐brain multi‐parametric quantitative MRI approach for simultaneous mapping of myelin‐water fraction (MWF), T1, T2, and proton‐density (PD), all within a clinically feasible scan time. Methods We developed 3D visualization of short transverse relaxation time component (ViSTa)‐MRF, which combined ViSTa technique with MR fingerprinting (MRF), to achieve high‐fidelity whole‐brain MWF and T1/T2/PD mapping on a clinical 3T scanner. To achieve fast acquisition and memory‐efficient reconstruction, the ViSTa‐MRF sequence leverages an optimized 3D tiny‐golden‐angle‐shuffling spiral‐projection acquisition and joint spatial–temporal subspace reconstruction with optimized preconditioning algorithm. With the proposed ViSTa‐MRF approach, high‐fidelity direct MWF mapping was achieved without a need for multicompartment fitting that could introduce bias and/or noise from additional assumptions or priors. Results The in vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide fast multi‐parametric mapping with high SNR and good quality. The in vivo results of 1 mm‐ and 0.66 mm‐isotropic resolution datasets indicate that the MWF values measured by the proposed method are consistent with standard ViSTa results that are 30× slower with lower SNR. Furthermore, we applied the proposed method to enable 5‐min whole‐brain 1 mm‐iso assessment of MWF and T1/T2/PD mappings for infant brain development and for post‐mortem brain samples. Conclusions In this work, we have developed a 3D ViSTa‐MRF technique that enables the acquisition of whole‐brain MWF, quantitative T1, T2, and PD maps at 1 and 0.66 mm isotropic resolution in 5 and 15 min, respectively. This advancement allows for quantitative investigations of myelination changes in the brain.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".