Comparison of T1-weighted 3D high-resolution anatomical sequences for the brain at 3 Tesla: FLASH, MP-RAGE and MDEFT
Bibliographic record
Abstract
Introduction: The outcome of automated quantitative morphometric population studies relies greatly on the quality of the cerebral anatomical images. For these applications, it is crucial that the images be uniform and have high spatial resolution, SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) between brain tissues. This study evaluates the performance of three different T1-weighted pulse sequences at 3T (Tesla) in terms of SNR and CNR efficiency, as well as signal intensity NU (nonuniformity): FLASH (fast low angle shot), MP-RAGE (magnetization prepared rapid acquisition by gradient echo), and MDEFT (modified driven equilibrium Fourier transform). In both MP-RAGE and MDEFT, the 3D-FLASH readout is preceded by a magnetization preparation period to modify the contrast characteristics in a time-compact sequence design. The MP-RAGE preparation consists of an inversion pulse followed by a delay TI before the readout. The MDEFT preparation consists of two pulses: first a saturation pulse immediately followed by spoiler gradients to remove transverse magnetization then, after a time delay τ1, an inversion pulse is applied followed by an additional delay τ2. The total preparation time is TI= τ1+τ2. The inner centric 3D phase encoding loop of the readout in MDEFT is divided into 2 segments. Our objective was to determine the optimal 3T sequence for automated image analysis, and to identify further challenges that need to be addressed.
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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) | 0.000 | 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".