Extracting more for less: multi‐echo MP2RAGE for simultaneous T<sub>1</sub>‐weighted imaging, T<sub>1</sub> mapping, mapping, SWI, and QSM from a single acquisition
Bibliographic record
Abstract
Purpose To demonstrate simultaneous T1‐weighted imaging, T1 mapping, mapping, SWI, and QSM from a single multi‐echo (ME) MP2RAGE acquisition. Methods A single‐echo (SE) MP2RAGE sequence at 7 tesla was extended to ME with 4 bipolar gradient echo readouts. T1‐weighted images and T1 maps calculated from individual echoes were combined using sum of squares and averaged, respectively. ME‐combined SWI and associated minimum intensity projection images were generated with TE‐adjusted homodyne filters. A QSM reconstruction pipeline was used, including a phase‐offsets correction and coil combination method to properly combine the phase images from the 32 receiver channels. Measurements of susceptibility, , and T1 of brain tissue from ME‐MP2RAGE were compared with those from standard ME‐gradient echo and SE‐MP2RAGE. Results The ME combined T1‐weighted, T1 map, SWI, and minimum intensity projection images showed increased SNRs compared to the SE results. The proposed coil combination method led to QSM results free of phase‐singularity artifacts, which were present in the standard adaptive combination method. T1‐weighted, T1, and susceptibility maps from ME‐MP2RAGE were comparable to those obtained from SE‐MP2RAGE and ME‐gradient echo, whereas maps showed increased blurring and reduced SNR. T1, , and susceptibility values of brain tissue from ME‐MP2RAGE were consistent with those from SE‐MP2RAGE and ME‐gradient echo. Conclusion High‐resolution structural T1 weighted imaging, T1 mapping, mapping, SWI, and QSM can be extracted from a single 8.5‐min ME‐MP2RAGE acquisition using a customized reconstruction pipeline. This method can be applied to replace separate SE‐MP2RAGE and ME‐gradient echo acquisitions to significantly shorten total scan time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".