SNR efficiency of combined bipolar gradient echoes: Comparison of three-dimensional FLASH, MPRAGE, and multiparameter mapping with VFA-FLASH and MP2RAGE
Bibliographic record
Abstract
Purpose High-bandwidth bipolar multiecho gradient echo sequences are increasingly popular in structural brain imaging because of reduced water–fat shifts, lower susceptibility effects, and improved signal-to-noise ratio (SNR) efficiency. In this study, we investigated the performance of three three-dimensional multiecho sequences (MPRAGE, MP2RAGE, and FLASH) with scan times < 9 min and 1-mm isotropic resolution against their single-echo, low-bandwidth counterparts at 3T. We also compared the performance of multiparameter mapping (PD, T1, and ) with bipolar multiecho MP2RAGE versus the variable flip angle technique with multiecho FLASH (VFA-FLASH). Methods Multiecho sequences were optimized to yield equivalent contrast and improved SNR compared with their single-echo counterparts. Theoretical SNR gains were verified with measurements in a multilayered phantom. Robust image processing pipelines extracted PD, T1, and maps from MP2RAGE or VFA-FLASH, and the corresponding SNR was measured with varying SENSE accelerations (R = 1–5) and number of echoes (N = 1–12). All sequences were tested on four healthy volunteers. Results Multiecho sequences achieved SNR gains of 1.3–1.6 over single-echo sequences. MP2RAGE yielded comparable T1-to-noise ratio to VFA-FLASH, but significantly lower SNR (<50%) in PD and maps. Measured SNR gains agreed with the theoretical predictions for SENSE accelerations ≤3. Conclusion Multiecho sequences achieve higher SNR efficiency over conventional single-echo sequences, despite three-fold higher sampling bandwidths. VFA-FLASH surpasses MP2RAGE in its ability to map three parameters with high SNR and 1-mm isotropic resolution in a clinically relevant scan time (∼8:30 min), whereas MP2RAGE yields lower intersubject variability in T1. Magn Reson Med 77:2186–2202, 2017. © 2016 International Society for Magnetic Resonance in Medicine
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| 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".