In vivo evaluation of population‐specific inversion pulses in parallel transmission
Bibliographic record
Abstract
PURPOSE: The aim of the study was to conduct in vivo evaluation of population-specific pulses in the context of the MP2RAGE sequence for brain imaging at 7T. METHODS: Five clusters were identified in a cohort of 39 volunteers. One cluster centered around the average head shape and position, whereas four others were located toward the extremes of the population distribution. Additionally, the one-size-fits-all solution was considered, using a standard universal pulse (UP) approach. Head shapes were characterized using lateral head breadth (HB), anterior-posterior head length (HL), and Y-shift metrics. For each group, a 5kT-points universal inversion pulse was computed and evaluated on four new test anatomies. A Python pipeline was integrated into the image reconstruction routine, using localizer scans to classify head shapes and positions. The pipeline selected one of five precomputed population-specific pulses or defaulted to the generic UP without extending scan time. RESULTS: The pipeline accurately classified head shapes and selected suitable pulses, enhancing the contrast of MP2RAGE images. Population-specific pulses helped mitigate some of the performance loss associated with using a one-size-fits-all UP, bringing performance closer to that of fully tailored solutions. This approach was particularly beneficial for individuals with smaller head sizes. However, the performance was worse in larger, more challenging head shapes. CONCLUSION: The novel head clustering and pulse selection pipeline facilitates the implementation of population-specific pulses in clinical practice by allowing pulse selection tailored to each head shape and position without increasing 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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".