Quantification of pulsed saturation transfer at 1.5T and 3T
Bibliographic record
Abstract
Purpose To compare magnetization transfer (MT) and CEST effects between 1.5T and 3T in phantom and in vivo experiments. Methods A pulsed saturation scheme using block‐shaped pulses separated by gaps was used to overcome the single RF amplifier duty cycle limitations of a clinical 1.5T scanner. Modeling was performed by incorporating the extended phase graph formalism into a Bloch‐McConnell simulation. Two saturation pulse types (with long and short pulses) were used. Estimated parameters for MT (the semi‐solid pool fraction, M0B; the semi‐solid transverse relaxation time, T2B) and CEST (asymmetry; areas) were compared between 1.5T and 3T in phantoms and in the healthy brain. Results Improved fits were shown after inclusion of extended phase graphs. Semi‐solid pool fractions in phantom (for agar with ammonium chloride) were higher for short compared to long pulses at 3T (by 19% over all concentrations) and higher at 1.5T compared to 3T (by 5%) using short pulses. In the in vivo experiments, differentiation of white and gray matter was seen in the brain at both field strengths with improved white–gray matter contrast at 3T. In white matter, the mean semi‐solid fractions were 18 ± 2% at 3T and 15 ± 2% at 1.5T. The CEST asymmetry in white matter was negative (−4.9 ± 0.4%) at 3T and zero (0.0 ± 0.3%) at 1.5T. Conclusions The pulsed saturation method with short pulses, using the extended phase graph formalism in the Bloch McConnell simulations, led to improved model fits to the data, when compared to those without extended phase graphs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".