Demonstration and suppression of respiration‐related artifacts in Bloch–Siegert shift‐based <i>B</i><sub>1</sub><sup>+</sup> maps of the human brain
Bibliographic record
Abstract
Respiration‐induced movement of the chest wall and internal organs causes temporal B0 variations extending throughout the brain. This study demonstrates that these variations can cause significant artifacts in maps obtained at 7 T with the Bloch–Siegert shift (BSS) mapping technique. To suppress these artifacts, a navigator correction scheme was proposed. Two sets of experiments were performed. In the first set of experiments, phase shifts induced by respiration‐related B0 variations were assessed for five subjects at 7 T by using a gradient echo (GRE) sequence without phase‐encoding. In the second set of experiments, maps were acquired using a GRE‐based BSS pulse sequence with navigator echoes. For this set, the measurements were consecutively repeated 16 times for the same imaging slice. These measurements were averaged to obtain the reference map. Due to the periodicity of respiration‐related phase shifts, their effect on the reference map was assumed to be negligible through averaging. The individual maps of the 16 repetitions were calculated with and without using the proposed navigator scheme. These maps were compared with the reference map. The peak‐to‐peak value of respiration‐related phase shifts varied between subjects. Without navigator correction, the interquartile range of percentage error in varied between 4.0% and 8.3% among subjects. When the proposed navigator scheme was used, these numbers were reduced to 2.5% and 2.9%, indicating an improvement in the precision of GRE‐based BSS mapping at high magnetic fields.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".