Elimination of low‐inversion‐efficiency induced artifacts in whole‐brain MP2RAGE using multiple RF‐shim configurations at 7 T
Bibliographic record
Abstract
The magnetization‐prepared two‐rapid‐gradient‐echo (MP2RAGE) sequence is used for structural T1‐weighted imaging and T1 mapping of the human brain. In this sequence, adiabatic inversion RF pulses are commonly used, which require the B1+ magnitude to be above a certain threshold. Achieving this threshold in the whole brain may not be possible at ultra‐high fields because of the short RF wavelength. This results in low‐inversion regions especially in the inferior brain (eg cerebellum and temporal lobes), which is reflected as regions of bright signal in MP2RAGE images. This study aims at eliminating the low‐inversion‐efficiency induced artifacts in MP2RAGE images at 7 T. The proposed technique takes advantage of parallel RF transmission systems by splitting the brain into two overlapping slabs and calculating the complex weights of transmit channels (ie RF shims) on these slabs for excitation and inversion independently. RF shims were calculated using fast methods implemented in the standard workflow. The excitation RF pulse was designed to obtain slabs with flat plateaus and sharp edges. These slabs were joined into a single volume during the online image reconstruction. The two‐slab strategy naturally results in a signal‐to‐noise ratio loss; however, it allowed the use of independent shims to make the B1+ field exceed the adiabatic threshold in the inferior brain, eliminating regions of low inversion efficiency. Accordingly, the normalized root‐mean‐square errors in the inversion were reduced to below 2%. The two‐slab strategy was found to outperform subject‐specific kT‐point inversion RF pulses in terms of inversion error. The proposed strategy is a simple yet effective method to eliminate low‐inversion‐efficiency artifacts; consequently, MP2RAGE‐based, artifact‐free T1‐weighted structural images were obtained in the whole brain at 7 T.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".